diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index dd8f49ed4b..a5db9ae27e 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -1,9 +1,34 @@
-Please see [contribution-guide.org](http://www.contribution-guide.org/) for steps we expect from contributors before submitting an issue or bug report. Be as concrete as possible, include relevant logs, package versions etc.
+# How to submit an issue?
 
-Please check the [Gensim FAQ](https://github.com/RaRe-Technologies/gensim/wiki/Recipes-&-FAQ) page before posting.
+First, please see [contribution-guide.org](http://www.contribution-guide.org/) for the steps we expect from contributors before submitting an issue or bug report. Be as concrete as possible, include relevant logs, package versions etc.
 
-**The proper place for open-ended questions is the [gensim mailing list](https://groups.google.com/forum/#!forum/gensim).** Github is not the right place for research discussions or feature requests.
+Also, please check the [Gensim FAQ](https://github.com/RaRe-Technologies/gensim/wiki/Recipes-&-FAQ) page before posting.
 
-For developers: see our [Developer Page](https://github.com/piskvorky/gensim/wiki/Developer-page#code-style) for details on code style, testing and similar.
+**The proper place for open-ended questions is the [Gensim mailing list](https://groups.google.com/forum/#!forum/gensim).** Github is not the right place for research discussions or feature requests.
 
-Thanks!
+# How to add a new feature or create a pull request?
+
+1. <a href="https://github.com/RaRe-Technologies/gensim/fork">Fork the Gensim repository</a>
+2. Clone your fork: `git clone https://github.com/<YOUR_GITHUB_USERNAME>/gensim.git`
+3. Create a new branch based on `develop`: `git checkout -b my-feature develop`
+4. Setup your Python enviroment
+   - Create a new [virtual environment](https://virtualenv.pypa.io/en/stable/): `pip install virtualenv; virtualenv gensim_env; source gensim_env/bin/activate`
+   - Install Gensim and its test dependencies in [editable mode](https://pip.pypa.io/en/stable/reference/pip_install/#editable-installs): 
+      - For linux: `pip install -e .[test]`
+      - For windows: `pip install -e .[test-win]`
+5. Implement your changes
+6. Check that everything's OK in your branch:
+   - Check it for PEP8: `tox -e flake8`
+   - Build its documentation (works only for MacOS/Linux): `tox -e docs` (documentation stored in `docs/src/_build`)
+   - Run unit tests: `tox -e py{version}-{os}`, for example `tox -e py27-linux` or `tox -e py36-win` where
+      - `{version}` is one of `27`, `35`, `36`
+      - `{os}` is either `win` or `linux`
+7. Add files, commit and push: `git add ... ; git commit -m "my commit message"; git push origin my-feature`
+8. [Create a PR](https://help.github.com/articles/creating-a-pull-request/) on Github. Write a **clear description** for your PR, including all the context and relevant information, such as:
+   - The issue that you fixed, e.g. `Fixes #123`
+   - Motivation: why did you create this PR? What functionality did you set out to improve? What was the problem + an overview of how you fixed it? Whom does it affect and how should people use it?
+   - Any other useful information: links to other related Github or mailing list issues and discussions, benchmark graphs, academic papers…
+
+P.S. for developers: see our [Developer Page](https://github.com/piskvorky/gensim/wiki/Developer-page#code-style) for details on the Gensim code style, CI, testing and similar.
+
+**Thanks and let's improve the open source world together!**
diff --git a/docs/notebooks/Poincare Evaluation.ipynb b/docs/notebooks/Poincare Evaluation.ipynb
index 839e54c760..cffd7c8491 100644
--- a/docs/notebooks/Poincare Evaluation.ipynb	
+++ b/docs/notebooks/Poincare Evaluation.ipynb	
@@ -41,14 +41,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/home/jayant/projects/gensim\n"
+      "/home/jayant/Projects/gensim/gensim\n"
      ]
     }
    ],
@@ -58,24 +58,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "/home/jayant/projects/gensim/docs/notebooks\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "% cd docs/notebooks/"
+    "# Some libraries need to be installed that are not part of Gensim\n",
+    "! pip install click>=6.7 nltk>=3.2.5 prettytable>=0.7.2 pygtrie>=2.2"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [
     {
@@ -92,7 +85,7 @@
        "True"
       ]
      },
-     "execution_count": 63,
+     "execution_count": 71,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -100,6 +93,7 @@
    "source": [
     "import csv\n",
     "from collections import OrderedDict\n",
+    "from IPython.display import display, HTML\n",
     "import logging\n",
     "import os\n",
     "import pickle\n",
@@ -123,19 +117,29 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Note that not all the above libraries are part of the gensim dependencies, so they might need to be installed separately. These requirements are listed in the poincare [requirements.txt](poincare/requirements.txt)"
+    "Please set the variable `parent_directory` below to change the directory to which the repositories are cloned."
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 11,
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/home/jayant/Projects/gensim/gensim/docs/notebooks\n"
+     ]
+    }
+   ],
    "source": [
-    "Please set the variable `parent_directory` below to change the directory to which the repositories are cloned."
+    "% cd docs/notebooks/"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -144,7 +148,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -158,14 +162,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/home/jayant/projects/gensim/docs/notebooks/poincare\n"
+      "/home/jayant/Projects/gensim/gensim/docs/notebooks/poincare\n"
      ]
     }
    ],
@@ -192,7 +196,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -246,7 +250,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -265,7 +269,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -280,11 +284,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Prepare the WordNet data\n",
+    "# Can also be downloaded directly from -\n",
+    "# https://github.com/jayantj/gensim/raw/wordnet_data/docs/notebooks/poincare/data/wordnet_noun_hypernyms.tsv\n",
+    "\n",
     "wordnet_file = os.path.join(data_directory, 'wordnet_noun_hypernyms.tsv')\n",
     "if not os.path.exists(wordnet_file):\n",
     "    ! python {parent_directory}/{cpp_repo_name}/scripts/create_wordnet_noun_hierarchy.py {wordnet_file}"
@@ -292,7 +299,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 20,
    "metadata": {
     "scrolled": true
    },
@@ -349,7 +356,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -395,7 +402,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -407,6 +414,7 @@
     "    'eps': 1e-6,\n",
     "    'burn_in': 0,\n",
     "    'batch_size': 10,\n",
+    "    'reg': 0.0\n",
     "}\n",
     "\n",
     "non_default_params = {\n",
@@ -418,7 +426,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 23,
    "metadata": {
     "scrolled": true
    },
@@ -470,7 +478,7 @@
     "        elif implementation == 'gensim':\n",
     "            train_gensim_model(\n",
     "                train_file, output_file, model_size, params['epochs'],\n",
-    "                params['neg'], params['burn_in'], params['batch_size'], seed=0)\n",
+    "                params['neg'], params['burn_in'], params['batch_size'], params['reg'], seed=0)\n",
     "        else:\n",
     "            raise ValueError('Given implementation %s not found' % implementation)\n",
     "        files[model_size] = output_file\n",
@@ -479,7 +487,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -520,7 +528,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -529,7 +537,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -595,17 +603,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
     "def gensim_model_name_from_params(params, prefix):\n",
-    "    param_keys = ['neg', 'epochs', 'burn_in', 'batch_size']\n",
+    "    param_keys = ['neg', 'epochs', 'burn_in', 'batch_size', 'reg']\n",
     "    name = ['%s_%s' % (key, params[key]) for key in sorted(param_keys)]\n",
     "    return '%s_%s' % (prefix, '_'.join(name))\n",
     "\n",
     "def train_gensim_model(\n",
-    "    data_file, output_file, dim, epochs, neg, burn_in, batch_size, seed=0):\n",
+    "    data_file, output_file, dim, epochs, neg, burn_in, batch_size, reg, seed=0):\n",
     "    \"\"\"Train a poincare embedding using gensim implementation\n",
     "    \n",
     "    Args:\n",
@@ -616,6 +624,7 @@
     "        neg (int): Number of negative samples to use\n",
     "        burn_in (int): Number of epochs to use for burn-in initialization\n",
     "        batch_size (int): Size of batch to use for training\n",
+    "        reg (float): Coefficient used for l2-regularization while training\n",
     "    \n",
     "    Notes: \n",
     "        If `output_file` already exists, skips training\n",
@@ -624,22 +633,23 @@
     "        print('File %s exists, skipping' % output_file)\n",
     "        return\n",
     "    train_data = PoincareRelations(data_file)\n",
-    "    model = PoincareModel(train_data, size=dim, negative=neg, burn_in=burn_in)\n",
+    "    model = PoincareModel(train_data, size=dim, negative=neg, burn_in=burn_in, regularization_coeff=reg)\n",
     "    model.train(epochs=epochs, batch_size=batch_size)\n",
     "    model.save(output_file)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
-    "non_default_params_gensim = {\n",
-    "    'neg': [10],\n",
-    "    'burn_in': [10],\n",
-    "    'batch_size': [50]\n",
-    "}"
+    "non_default_params_gensim = [\n",
+    "    {'neg': 10,},\n",
+    "    {'burn_in': 10,},\n",
+    "    {'batch_size': 50,},\n",
+    "    {'neg': 10, 'reg': 1, 'burn_in': 10, 'epochs': 200},\n",
+    "]"
    ]
   },
   {
@@ -655,14 +665,13 @@
     "for dim, filepath in files.items():\n",
     "    model_files['gensim'][model_name][dim] = filepath\n",
     "# Train models with non-default params\n",
-    "for param, values in non_default_params_gensim.items():\n",
+    "for new_params in non_default_params_gensim:\n",
     "    params = default_params.copy()\n",
-    "    for value in values:\n",
-    "        params[param] = value\n",
-    "        model_name, files = train_model_with_params(params, wordnet_file, model_sizes, 'gensim_model', 'gensim')\n",
-    "        model_files['gensim'][model_name] = {}\n",
-    "        for dim, filepath in files.items():\n",
-    "            model_files['gensim'][model_name][dim] = filepath"
+    "    params.update(new_params)\n",
+    "    model_name, files = train_model_with_params(params, wordnet_file, model_sizes, 'gensim_model', 'gensim')\n",
+    "    model_files['gensim'][model_name] = {}\n",
+    "    for dim, filepath in files.items():\n",
+    "        model_files['gensim'][model_name][dim] = filepath"
    ]
   },
   {
@@ -674,7 +683,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -796,7 +805,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 176,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -808,33 +817,46 @@
     "        results (dict): mapping between embeddings and corresponding results\n",
     "    \n",
     "    \"\"\"\n",
-    "    data = PrettyTable()\n",
-    "    data.field_names = [\"Model Description\", \"Metric\"] + [str(dim) for dim in sorted(model_sizes)]\n",
+    "    result_table = PrettyTable()\n",
+    "    result_table.field_names = [\"Model Description\", \"Metric\"] + [str(dim) for dim in sorted(model_sizes)]\n",
     "    for model_name, model_results in results.items():\n",
     "        metrics = [metric for metric in model_results.keys()]\n",
     "        dims = sorted([dim for dim in model_results[metrics[0]].keys()])\n",
-    "        row = [model_name, '\\n'.join(metrics) + '\\n']\n",
+    "        description = model_description_from_name(model_name)\n",
+    "        row = [description, '\\n'.join(metrics) + '\\n']\n",
     "        for dim in dims:\n",
     "            scores = ['%.2f' % model_results[metric][dim] for metric in metrics]\n",
     "            row.append('\\n'.join(scores))\n",
-    "        data.add_row(row)\n",
-    "    data.align = 'r'\n",
-    "    data_cols = data.get_string().split('\\n')[0].split('+')[1:-1]\n",
-    "    col_lengths = [len(col) for col in data_cols]\n",
-    "    header_col_1_length = col_lengths[0] + col_lengths[1] - 1\n",
-    "    header_col_2_length = sum(col_lengths[2:]) + len(col_lengths[2:-1]) - 2\n",
+    "        result_table.add_row(row)\n",
+    "    result_table.align = 'r'\n",
+    "    result_html = result_table.get_html_string()\n",
+    "    search = \"<table>\"\n",
+    "    insert_at = result_html.index(search) + len(search)\n",
+    "    new_row = \"\"\"\n",
+    "        <tr>\n",
+    "            <th colspan=\"1\" style=\"text-align:left\">%s</th>\n",
+    "            <th colspan=\"1\"></th>\n",
+    "            <th colspan=\"%d\" style=\"text-align:center\"> Dimensions</th>\n",
+    "        </tr>\"\"\" % (task_name, len(model_sizes))\n",
+    "    result_html = result_html[:insert_at] + new_row + result_html[insert_at:]\n",
+    "    display(HTML(result_html))\n",
     "    \n",
-    "    header_col_2_content = \"Model Dimensions\"\n",
-    "    header_col_2_left_margin = (header_col_2_length - len(header_col_2_content)) // 2\n",
-    "    header_col_2_right_margin = header_col_2_length - len(header_col_2_content) - header_col_2_left_margin\n",
-    "    header_col_2_string = \"%s%s%s\" % (\n",
-    "        \" \" * header_col_2_left_margin, header_col_2_content, \" \" * header_col_2_right_margin)\n",
-    "    header = PrettyTable()\n",
-    "    header.field_names = [\" \" * header_col_1_length, header_col_2_string]\n",
-    "    header_lines = header.get_string(start=0, end=0).split(\"\\n\")[:2]\n",
-    "    print('Results for %s task' % task_name)\n",
-    "    print(\"\\n\".join(header_lines))\n",
-    "    print(data)        "
+    "def model_description_from_name(model_name):\n",
+    "    if model_name.startswith('gensim'):\n",
+    "        implementation = 'Gensim'\n",
+    "    elif model_name.startswith('cpp'):\n",
+    "        implementation = 'C++'\n",
+    "    elif model_name.startswith('np'):\n",
+    "        implementation = 'Numpy'\n",
+    "    else:\n",
+    "        raise ValueError('Unsupported implementation for model: %s' % model_name)\n",
+    "    description = []\n",
+    "    for param_key in sorted(default_params.keys()):\n",
+    "        pattern = '%s_([^_]*)_?' % param_key\n",
+    "        match = re.search(pattern, model_name)\n",
+    "        if match:\n",
+    "            description.append(\"%s=%s\" % (param_key, match.groups()[0]))\n",
+    "    return \"%s: %s\" % (implementation, \", \".join(description))"
    ]
   },
   {
@@ -844,9 +866,18 @@
     "### 4.1 WordNet reconstruction"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For this task, embeddings are learnt using the entire transitive closure of the WordNet noun hypernym hierarchy. Subsequently, for every hypernym pair `(u, v)`, the rank of `v` amongst all nodes that do not have a positive edge with `v` is computed. The final metric `mean_rank` is the average of all these ranks. The `MAP` metric is the mean of the Average Precision of the rankings for all positive nodes for a given node `u`.\n",
+    "\n",
+    "Note that this task tests representation capacity of the learnt embeddings, and not the generalization ability."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -880,54 +911,160 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 148,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Results for WordNet Reconstruction task\n",
-      "+-----------------------------------------------------------------------+----------------------------------------------------------+\n",
-      "|                                                                       |                     Model Dimensions                     |\n",
-      "+-----------------------------------------------------------+-----------+----------+---------+---------+---------+--------+--------+\n",
-      "|                                         Model Description |    Metric |        5 |      10 |      20 |      50 |    100 |    200 |\n",
-      "+-----------------------------------------------------------+-----------+----------+---------+---------+---------+--------+--------+\n",
-      "| cpp_model_burn_in_10_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank |   252.86 |  195.73 |  182.57 |  165.33 | 157.37 | 155.78 |\n",
-      "|                                                           |       MAP |     0.26 |    0.32 |    0.34 |    0.36 |   0.36 |   0.36 |\n",
-      "|                                                           |           |          |         |         |         |        |        |\n",
-      "|  cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank |   265.72 |  116.94 |   90.81 |   59.47 |  55.14 |  54.31 |\n",
-      "|                                                           |       MAP |     0.28 |    0.41 |    0.49 |    0.56 |   0.58 |   0.59 |\n",
-      "|                                                           |           |          |         |         |         |        |        |\n",
-      "|  cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_10_threads_8 | mean_rank |   280.17 |  129.46 |   92.06 |   80.41 |  71.42 |  69.30 |\n",
-      "|                                                           |       MAP |     0.27 |    0.40 |    0.49 |    0.53 |   0.56 |   0.56 |\n",
-      "|                                                           |           |          |         |         |         |        |        |\n",
-      "| cpp_model_burn_in_0_epochs_200_eps_1e-06_neg_20_threads_8 | mean_rank |   191.69 |   97.65 |   72.07 |   55.48 |  46.76 |  49.62 |\n",
-      "|                                                           |       MAP |     0.34 |    0.43 |    0.51 |    0.57 |   0.59 |   0.59 |\n",
-      "|                                                           |           |          |         |         |         |        |        |\n",
-      "|     gensim_model_batch_size_10_burn_in_0_epochs_50_neg_20 | mean_rank |   154.41 |   62.77 |   27.32 |   20.22 |  16.15 |  13.20 |\n",
-      "|                                                           |       MAP |     0.40 |    0.63 |    0.72 |    0.77 |   0.78 |   0.79 |\n",
-      "|                                                           |           |          |         |         |         |        |        |\n",
-      "|     gensim_model_batch_size_50_burn_in_0_epochs_50_neg_20 | mean_rank |   148.51 |   63.67 |   28.36 |   20.23 |  15.75 |  13.59 |\n",
-      "|                                                           |       MAP |     0.38 |    0.62 |    0.72 |    0.76 |   0.78 |   0.79 |\n",
-      "|                                                           |           |          |         |         |         |        |        |\n",
-      "|    gensim_model_batch_size_10_burn_in_10_epochs_50_neg_20 | mean_rank |   108.01 |  100.73 |   97.38 |   94.49 |  94.68 |  89.66 |\n",
-      "|                                                           |       MAP |     0.37 |    0.47 |    0.48 |    0.49 |   0.48 |   0.49 |\n",
-      "|                                                           |           |          |         |         |         |        |        |\n",
-      "|     gensim_model_batch_size_10_burn_in_0_epochs_50_neg_10 | mean_rank |   211.71 |   54.42 |   24.90 |   21.42 |  15.80 |  15.13 |\n",
-      "|                                                           |       MAP |     0.33 |    0.60 |    0.72 |    0.76 |   0.78 |   0.79 |\n",
-      "|                                                           |           |          |         |         |         |        |        |\n",
-      "|                                 np_model_epochs_50_neg_20 | mean_rank | 10169.24 | 5602.84 | 3631.57 | 1088.31 | 537.43 | 345.45 |\n",
-      "|                                                           |       MAP |     0.14 |    0.16 |    0.20 |    0.25 |   0.30 |   0.36 |\n",
-      "|                                                           |           |          |         |         |         |        |        |\n",
-      "+-----------------------------------------------------------+-----------+----------+---------+---------+---------+--------+--------+\n"
-     ]
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "    <tr>\n",
+       "        <th colspan=\"1\" style=\"text-align:left\">WordNet Reconstruction</th><th \n",
+       "        <th colspan=\"1\"></th>\n",
+       "        <th colspan=\"6\" style=\"text-align:center\"> Dimensions</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "        <th>Model Description</th>\n",
+       "        <th>Metric</th>\n",
+       "        <th>5</th>\n",
+       "        <th>10</th>\n",
+       "        <th>20</th>\n",
+       "        <th>50</th>\n",
+       "        <th>100</th>\n",
+       "        <th>200</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "        <td>C++: burn_in=0, epochs=200, eps=1e-06, neg=20, threads=8</td>\n",
+       "        <td>mean_rank<br>MAP<br></td>\n",
+       "        <td>191.69<br>0.34</td>\n",
+       "        <td>97.65<br>0.43</td>\n",
+       "        <td>72.07<br>0.51</td>\n",
+       "        <td>55.48<br>0.57</td>\n",
+       "        <td>46.76<br>0.59</td>\n",
+       "        <td>49.62<br>0.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "        <td>C++: burn_in=0, epochs=50, eps=1e-06, neg=10, threads=8</td>\n",
+       "        <td>mean_rank<br>MAP<br></td>\n",
+       "        <td>280.17<br>0.27</td>\n",
+       "        <td>129.46<br>0.40</td>\n",
+       "        <td>92.06<br>0.49</td>\n",
+       "        <td>80.41<br>0.53</td>\n",
+       "        <td>71.42<br>0.56</td>\n",
+       "        <td>69.30<br>0.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "        <td>C++: burn_in=0, epochs=50, eps=1e-06, neg=20, threads=8</td>\n",
+       "        <td>mean_rank<br>MAP<br></td>\n",
+       "        <td>265.72<br>0.28</td>\n",
+       "        <td>116.94<br>0.41</td>\n",
+       "        <td>90.81<br>0.49</td>\n",
+       "        <td>59.47<br>0.56</td>\n",
+       "        <td>55.14<br>0.58</td>\n",
+       "        <td>54.31<br>0.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "        <td>C++: burn_in=10, epochs=50, eps=1e-06, neg=20, threads=8</td>\n",
+       "        <td>mean_rank<br>MAP<br></td>\n",
+       "        <td>252.86<br>0.26</td>\n",
+       "        <td>195.73<br>0.32</td>\n",
+       "        <td>182.57<br>0.34</td>\n",
+       "        <td>165.33<br>0.36</td>\n",
+       "        <td>157.37<br>0.36</td>\n",
+       "        <td>155.78<br>0.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "        <td>Gensim: batch_size=10, burn_in=10, epochs=50, neg=20, reg=0.0</td>\n",
+       "        <td>mean_rank<br>MAP<br></td>\n",
+       "        <td>108.01<br>0.37</td>\n",
+       "        <td>100.73<br>0.47</td>\n",
+       "        <td>97.38<br>0.48</td>\n",
+       "        <td>94.49<br>0.49</td>\n",
+       "        <td>94.68<br>0.48</td>\n",
+       "        <td>89.66<br>0.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "        <td>Gensim: batch_size=10, burn_in=0, epochs=50, neg=20, reg=0.0</td>\n",
+       "        <td>mean_rank<br>MAP<br></td>\n",
+       "        <td>154.41<br>0.40</td>\n",
+       "        <td>62.77<br>0.63</td>\n",
+       "        <td>27.32<br>0.72</td>\n",
+       "        <td>20.22<br>0.77</td>\n",
+       "        <td>16.15<br>0.78</td>\n",
+       "        <td>13.20<br>0.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "        <td>Gensim: batch_size=10, burn_in=0, epochs=50, neg=10, reg=0.0</td>\n",
+       "        <td>mean_rank<br>MAP<br></td>\n",
+       "        <td>211.71<br>0.33</td>\n",
+       "        <td>54.42<br>0.60</td>\n",
+       "        <td>24.90<br>0.72</td>\n",
+       "        <td>21.42<br>0.76</td>\n",
+       "        <td>15.80<br>0.78</td>\n",
+       "        <td>15.13<br>0.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "        <td>Gensim: batch_size=50, burn_in=0, epochs=50, neg=20, reg=0.0</td>\n",
+       "        <td>mean_rank<br>MAP<br></td>\n",
+       "        <td>148.51<br>0.38</td>\n",
+       "        <td>63.67<br>0.62</td>\n",
+       "        <td>28.36<br>0.72</td>\n",
+       "        <td>20.23<br>0.76</td>\n",
+       "        <td>15.75<br>0.78</td>\n",
+       "        <td>13.59<br>0.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "        <td>Gensim: batch_size=10, burn_in=10, epochs=200, neg=10, reg=1</td>\n",
+       "        <td>mean_rank<br>MAP<br></td>\n",
+       "        <td>61.48<br>0.38</td>\n",
+       "        <td>54.70<br>0.41</td>\n",
+       "        <td>53.02<br>0.41</td>\n",
+       "        <td>50.80<br>0.42</td>\n",
+       "        <td>49.58<br>0.42</td>\n",
+       "        <td>48.56<br>0.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "        <td>Numpy: epochs=50, neg=20</td>\n",
+       "        <td>mean_rank<br>MAP<br></td>\n",
+       "        <td>9617.57<br>0.14</td>\n",
+       "        <td>5902.65<br>0.16</td>\n",
+       "        <td>3868.78<br>0.19</td>\n",
+       "        <td>1117.77<br>0.25</td>\n",
+       "        <td>529.92<br>0.30</td>\n",
+       "        <td>377.45<br>0.35</td>\n",
+       "    </tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
     "display_results('WordNet Reconstruction', reconstruction_results)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Results from the paper -\n",
+    "![Reconstruction Results](https://raw.githubusercontent.com/RaRe-Technologies/gensim/poincare_model_keyedvectors/docs/notebooks/poincare/reconstruction_paper.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The figures above illustrate a few things - \n",
+    "1. The gensim implementation does significantly better for all model sizes and hyperparameters than both the other implementations.\n",
+    "2. The results from the original paper have not been achieved by our implementation. Especially for models with lower dimensions, the paper mentions significantly better mean rank and MAP for the reconstruction task.\n",
+    "3. Using burn-in and regularization leads to much better results with low model sizes, however the results do not improve significantly with increasing model size. This might have to do with tuning the regularization coefficient, which the paper does not mention."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -935,6 +1072,15 @@
     "### 4.2 WordNet link prediction"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This task is similar to the reconstruction task described above, except that the list of relations is split into a training and testing set, and the mean rank reported is for the edges in the test set.\n",
+    "\n",
+    "Therefore, this tests the ability of the model to predict unseen edges between nodes, i.e. generalization ability, as opposed to the representation capacity tested in the Reconstruction task"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -944,7 +1090,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1008,7 +1154,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1042,7 +1188,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [
     {
@@ -1066,7 +1212,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1124,14 +1270,13 @@
     "for dim, filepath in files.items():\n",
     "    lp_model_files['gensim'][model_name][dim] = filepath\n",
     "# Train models with non-default params\n",
-    "for param, values in non_default_params_gensim.items():\n",
+    "for new_params in non_default_params_gensim:\n",
     "    params = default_params.copy()\n",
-    "    for value in values:\n",
-    "        params[param] = value\n",
-    "        model_name, files = train_model_with_params(params, wordnet_train_file, model_sizes, 'gensim_lp_model', 'gensim')\n",
-    "        lp_model_files['gensim'][model_name] = {}\n",
-    "        for dim, filepath in files.items():\n",
-    "            lp_model_files['gensim'][model_name][dim] = filepath"
+    "    params.update(new_params)\n",
+    "    model_name, files = train_model_with_params(params, wordnet_file, model_sizes, 'gensim_lp_model', 'gensim')\n",
+    "    lp_model_files['gensim'][model_name] = {}\n",
+    "    for dim, filepath in files.items():\n",
+    "        lp_model_files['gensim'][model_name][dim] = filepath"
    ]
   },
   {
@@ -1143,7 +1288,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 89,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1173,54 +1318,162 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 149,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Results for WordNet Link Prediction task\n",
-      "+--------------------------------------------------------------------------+------------------------------------------------------------+\n",
-      "|                                                                          |                      Model Dimensions                      |\n",
-      "+--------------------------------------------------------------+-----------+----------+---------+---------+---------+---------+---------+\n",
-      "|                                            Model Description |    Metric |        5 |      10 |      20 |      50 |     100 |     200 |\n",
-      "+--------------------------------------------------------------+-----------+----------+---------+---------+---------+---------+---------+\n",
-      "| cpp_lp_model_burn_in_0_epochs_200_eps_1e-06_neg_20_threads_8 | mean_rank |   218.26 |   99.09 |   60.50 |   52.24 |   60.81 |   69.13 |\n",
-      "|                                                              |       MAP |     0.15 |    0.24 |    0.31 |    0.35 |    0.36 |    0.36 |\n",
-      "|                                                              |           |          |         |         |         |         |         |\n",
-      "|  cpp_lp_model_burn_in_0_epochs_50_eps_1e-06_neg_10_threads_8 | mean_rank |   230.34 |  123.24 |   75.62 |   65.97 |   55.33 |   56.89 |\n",
-      "|                                                              |       MAP |     0.14 |    0.22 |    0.28 |    0.31 |    0.33 |    0.34 |\n",
-      "|                                                              |           |          |         |         |         |         |         |\n",
-      "|  cpp_lp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank |   687.48 |  281.88 |   72.95 |   57.37 |   52.56 |   61.42 |\n",
-      "|                                                              |       MAP |     0.12 |    0.15 |    0.31 |    0.35 |    0.36 |    0.36 |\n",
-      "|                                                              |           |          |         |         |         |         |         |\n",
-      "| cpp_lp_model_burn_in_10_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank |   236.31 |  214.85 |  193.30 |  180.27 |  169.00 |  163.22 |\n",
-      "|                                                              |       MAP |     0.10 |    0.13 |    0.14 |    0.15 |    0.16 |    0.16 |\n",
-      "|                                                              |           |          |         |         |         |         |         |\n",
-      "|     gensim_lp_model_batch_size_10_burn_in_0_epochs_50_neg_10 | mean_rank |   141.52 |   58.89 |   31.66 |   22.13 |   21.29 |   19.38 |\n",
-      "|                                                              |       MAP |     0.18 |    0.34 |    0.46 |    0.51 |    0.52 |    0.53 |\n",
-      "|                                                              |           |          |         |         |         |         |         |\n",
-      "|     gensim_lp_model_batch_size_10_burn_in_0_epochs_50_neg_20 | mean_rank |   121.42 |   52.51 |   24.61 |   19.96 |   20.44 |   19.55 |\n",
-      "|                                                              |       MAP |     0.19 |    0.37 |    0.46 |    0.52 |    0.50 |    0.54 |\n",
-      "|                                                              |           |          |         |         |         |         |         |\n",
-      "|    gensim_lp_model_batch_size_10_burn_in_10_epochs_50_neg_20 | mean_rank |   154.95 |  138.12 |  122.06 |  117.96 |  112.99 |  110.84 |\n",
-      "|                                                              |       MAP |     0.16 |    0.21 |    0.24 |    0.26 |    0.25 |    0.26 |\n",
-      "|                                                              |           |          |         |         |         |         |         |\n",
-      "|     gensim_lp_model_batch_size_50_burn_in_0_epochs_50_neg_20 | mean_rank |   144.19 |   53.65 |   25.21 |   20.68 |   21.32 |   18.97 |\n",
-      "|                                                              |       MAP |     0.19 |    0.35 |    0.47 |    0.52 |    0.51 |    0.53 |\n",
-      "|                                                              |           |          |         |         |         |         |         |\n",
-      "|                                 np_lp_model_epochs_50_neg_20 | mean_rank | 14100.99 | 7538.02 | 5297.38 | 2365.27 | 1552.72 | 1741.87 |\n",
-      "|                                                              |       MAP |     0.00 |    0.02 |    0.03 |    0.08 |    0.10 |    0.13 |\n",
-      "|                                                              |           |          |         |         |         |         |         |\n",
-      "+--------------------------------------------------------------+-----------+----------+---------+---------+---------+---------+---------+\n"
-     ]
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "    <tr>\n",
+       "        <th colspan=\"1\" style=\"text-align:left\">WordNet Link Prediction</th>\n",
+       "        <th colspan=\"1\"></th>\n",
+       "        <th colspan=\"6\" style=\"text-align:center\"> Dimensions</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "        <th>Model Description</th>\n",
+       "        <th>Metric</th>\n",
+       "        <th>5</th>\n",
+       "        <th>10</th>\n",
+       "        <th>20</th>\n",
+       "        <th>50</th>\n",
+       "        <th>100</th>\n",
+       "        <th>200</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "        <td>C++: burn_in=0, epochs=200, eps=1e-06, neg=20, threads=8</td>\n",
+       "        <td>mean_rank<br>MAP<br></td>\n",
+       "        <td>218.26<br>0.15</td>\n",
+       "        <td>99.09<br>0.24</td>\n",
+       "        <td>60.50<br>0.31</td>\n",
+       "        <td>52.24<br>0.35</td>\n",
+       "        <td>60.81<br>0.36</td>\n",
+       "        <td>69.13<br>0.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "        <td>C++: burn_in=0, epochs=50, eps=1e-06, neg=20, threads=8</td>\n",
+       "        <td>mean_rank<br>MAP<br></td>\n",
+       "        <td>687.48<br>0.12</td>\n",
+       "        <td>281.88<br>0.15</td>\n",
+       "        <td>72.95<br>0.31</td>\n",
+       "        <td>57.37<br>0.35</td>\n",
+       "        <td>52.56<br>0.36</td>\n",
+       "        <td>61.42<br>0.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "        <td>C++: burn_in=0, epochs=50, eps=1e-06, neg=10, threads=8</td>\n",
+       "        <td>mean_rank<br>MAP<br></td>\n",
+       "        <td>230.34<br>0.14</td>\n",
+       "        <td>123.24<br>0.22</td>\n",
+       "        <td>75.62<br>0.28</td>\n",
+       "        <td>65.97<br>0.31</td>\n",
+       "        <td>55.33<br>0.33</td>\n",
+       "        <td>56.89<br>0.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "        <td>C++: burn_in=10, epochs=50, eps=1e-06, neg=20, threads=8</td>\n",
+       "        <td>mean_rank<br>MAP<br></td>\n",
+       "        <td>236.31<br>0.10</td>\n",
+       "        <td>214.85<br>0.13</td>\n",
+       "        <td>193.30<br>0.14</td>\n",
+       "        <td>180.27<br>0.15</td>\n",
+       "        <td>169.00<br>0.16</td>\n",
+       "        <td>163.22<br>0.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "        <td>Gensim: batch_size=10, burn_in=0, epochs=50, neg=10, reg=0.0</td>\n",
+       "        <td>mean_rank<br>MAP<br></td>\n",
+       "        <td>141.52<br>0.18</td>\n",
+       "        <td>58.89<br>0.34</td>\n",
+       "        <td>31.66<br>0.46</td>\n",
+       "        <td>22.13<br>0.51</td>\n",
+       "        <td>21.29<br>0.52</td>\n",
+       "        <td>19.38<br>0.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "        <td>Gensim: batch_size=10, burn_in=0, epochs=50, neg=20, reg=0.0</td>\n",
+       "        <td>mean_rank<br>MAP<br></td>\n",
+       "        <td>121.42<br>0.19</td>\n",
+       "        <td>52.51<br>0.37</td>\n",
+       "        <td>24.61<br>0.46</td>\n",
+       "        <td>19.96<br>0.52</td>\n",
+       "        <td>20.44<br>0.50</td>\n",
+       "        <td>19.55<br>0.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "        <td>Gensim: batch_size=50, burn_in=0, epochs=50, neg=20, reg=0.0</td>\n",
+       "        <td>mean_rank<br>MAP<br></td>\n",
+       "        <td>144.19<br>0.19</td>\n",
+       "        <td>53.65<br>0.35</td>\n",
+       "        <td>25.21<br>0.47</td>\n",
+       "        <td>20.68<br>0.52</td>\n",
+       "        <td>21.32<br>0.51</td>\n",
+       "        <td>18.97<br>0.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "        <td>Gensim: batch_size=10, burn_in=10, epochs=50, neg=20, reg=0.0</td>\n",
+       "        <td>mean_rank<br>MAP<br></td>\n",
+       "        <td>154.95<br>0.16</td>\n",
+       "        <td>138.12<br>0.21</td>\n",
+       "        <td>122.06<br>0.24</td>\n",
+       "        <td>117.96<br>0.26</td>\n",
+       "        <td>112.99<br>0.25</td>\n",
+       "        <td>110.84<br>0.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "        <td>Gensim: batch_size=10, burn_in=10, epochs=200, neg=10, reg=1</td>\n",
+       "        <td>mean_rank<br>MAP<br></td>\n",
+       "        <td>51.72<br>0.22</td>\n",
+       "        <td>39.85<br>0.28</td>\n",
+       "        <td>38.60<br>0.29</td>\n",
+       "        <td>36.55<br>0.30</td>\n",
+       "        <td>35.32<br>0.31</td>\n",
+       "        <td>34.66<br>0.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "        <td>Numpy: epochs=50, neg=20</td>\n",
+       "        <td>mean_rank<br>MAP<br></td>\n",
+       "        <td>14526.67<br>0.01</td>\n",
+       "        <td>8411.10<br>0.02</td>\n",
+       "        <td>5749.57<br>0.04</td>\n",
+       "        <td>1873.12<br>0.07</td>\n",
+       "        <td>1639.50<br>0.10</td>\n",
+       "        <td>1350.13<br>0.13</td>\n",
+       "    </tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
     "display_results('WordNet Link Prediction', lp_results)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Results from the paper -\n",
+    "![Link Prediction Paper](https://raw.githubusercontent.com/RaRe-Technologies/gensim/poincare_model_keyedvectors/docs/notebooks/poincare/link_prediction_paper.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These results follow similar trends as the reconstruction results. Repeating here for ease of reading - \n",
+    "1. The gensim implementation does significantly better for all model sizes and hyperparameters than both the other implementations.\n",
+    "2. The results from the original paper have not been achieved by our implementation. Especially for models with lower dimensions, the paper mentions significantly better mean rank and MAP for the link prediction task.\n",
+    "4. Using burn-in and regularization leads to better results with low model sizes, however the results do not improve significantly with increasing model size.\n",
+    "\n",
+    "The main difference from the reconstruction results is that mean ranks for link prediction are slightly worse most of the time than the corresponding reconstruction results. This is to be expected, as link prediction is performed on a held-out test set."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1228,9 +1481,22 @@
     "### 4.3 HyperLex Lexical Entailment"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The Lexical Entailment task is performed using the HyperLex dataset, a collection of 2163 noun pairs and scores that denote \"To what degree is noun A a type of noun Y\". For example - \n",
+    "  \n",
+    "`girl person 9.85`\n",
+    "\n",
+    "These scores are out of 10.\n",
+    "\n",
+    "The [spearman's correlation score](https://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient) is computed for the predicted and actual similarity scores, with the models trained on the entire WordNet noun hierarchy.\n"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 168,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1241,7 +1507,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [],
    "source": [
     "for implementation, models in sorted(model_files.items()):\n",
@@ -1258,39 +1526,136 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 170,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Results for Lexical Entailment (HyperLex) task\n",
-      "+----------------------------------------------------------------------+-----------------------------------------+\n",
-      "|                                                                      |            Model Dimensions             |\n",
-      "+-----------------------------------------------------------+----------+------+------+------+------+------+------+\n",
-      "|                                         Model Description |   Metric |    5 |   10 |   20 |   50 |  100 |  200 |\n",
-      "+-----------------------------------------------------------+----------+------+------+------+------+------+------+\n",
-      "|  cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_8 | spearman | 0.45 | 0.44 | 0.47 | 0.44 | 0.46 | 0.45 |\n",
-      "|                                                           |          |      |      |      |      |      |      |\n",
-      "| cpp_model_burn_in_10_epochs_50_eps_1e-06_neg_20_threads_8 | spearman | 0.42 | 0.42 | 0.44 | 0.44 | 0.44 | 0.45 |\n",
-      "|                                                           |          |      |      |      |      |      |      |\n",
-      "|  cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_10_threads_8 | spearman | 0.42 | 0.43 | 0.44 | 0.43 | 0.44 | 0.44 |\n",
-      "|                                                           |          |      |      |      |      |      |      |\n",
-      "| cpp_model_burn_in_0_epochs_200_eps_1e-06_neg_20_threads_8 | spearman | 0.45 | 0.46 | 0.45 | 0.46 | 0.45 | 0.47 |\n",
-      "|                                                           |          |      |      |      |      |      |      |\n",
-      "|     gensim_model_batch_size_10_burn_in_0_epochs_50_neg_10 | spearman | 0.46 | 0.47 | 0.46 | 0.47 | 0.48 | 0.48 |\n",
-      "|                                                           |          |      |      |      |      |      |      |\n",
-      "|     gensim_model_batch_size_50_burn_in_0_epochs_50_neg_20 | spearman | 0.47 | 0.47 | 0.47 | 0.47 | 0.48 | 0.48 |\n",
-      "|                                                           |          |      |      |      |      |      |      |\n",
-      "|    gensim_model_batch_size_10_burn_in_10_epochs_50_neg_20 | spearman | 0.45 | 0.46 | 0.45 | 0.46 | 0.45 | 0.46 |\n",
-      "|                                                           |          |      |      |      |      |      |      |\n",
-      "|     gensim_model_batch_size_10_burn_in_0_epochs_50_neg_20 | spearman | 0.47 | 0.46 | 0.47 | 0.48 | 0.48 | 0.48 |\n",
-      "|                                                           |          |      |      |      |      |      |      |\n",
-      "|                                 np_model_epochs_50_neg_20 | spearman | 0.15 | 0.20 | 0.21 | 0.21 | 0.25 | 0.27 |\n",
-      "|                                                           |          |      |      |      |      |      |      |\n",
-      "+-----------------------------------------------------------+----------+------+------+------+------+------+------+\n"
-     ]
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "    <tr>\n",
+       "        <th colspan=\"1\" style=\"text-align:left\">Lexical Entailment (HyperLex)</th>\n",
+       "        <th colspan=\"1\"></th>\n",
+       "        <th colspan=\"6\" style=\"text-align:center\"> Dimensions</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "        <th>Model Description</th>\n",
+       "        <th>Metric</th>\n",
+       "        <th>5</th>\n",
+       "        <th>10</th>\n",
+       "        <th>20</th>\n",
+       "        <th>50</th>\n",
+       "        <th>100</th>\n",
+       "        <th>200</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "        <td>C++: burn_in=0, epochs=200, eps=1e-06, neg=20, threads=8</td>\n",
+       "        <td>spearman<br></td>\n",
+       "        <td>0.45</td>\n",
+       "        <td>0.46</td>\n",
+       "        <td>0.45</td>\n",
+       "        <td>0.45</td>\n",
+       "        <td>0.45</td>\n",
+       "        <td>0.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "        <td>C++: burn_in=0, epochs=50, eps=1e-06, neg=10, threads=8</td>\n",
+       "        <td>spearman<br></td>\n",
+       "        <td>0.42</td>\n",
+       "        <td>0.41</td>\n",
+       "        <td>0.43</td>\n",
+       "        <td>0.42</td>\n",
+       "        <td>0.43</td>\n",
+       "        <td>0.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "        <td>C++: burn_in=0, epochs=50, eps=1e-06, neg=20, threads=8</td>\n",
+       "        <td>spearman<br></td>\n",
+       "        <td>0.44</td>\n",
+       "        <td>0.43</td>\n",
+       "        <td>0.47</td>\n",
+       "        <td>0.44</td>\n",
+       "        <td>0.45</td>\n",
+       "        <td>0.44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "        <td>C++: burn_in=10, epochs=50, eps=1e-06, neg=20, threads=8</td>\n",
+       "        <td>spearman<br></td>\n",
+       "        <td>0.43</td>\n",
+       "        <td>0.42</td>\n",
+       "        <td>0.44</td>\n",
+       "        <td>0.44</td>\n",
+       "        <td>0.44</td>\n",
+       "        <td>0.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "        <td>Gensim: batch_size=10, burn_in=10, epochs=50, neg=20, reg=0.0</td>\n",
+       "        <td>spearman<br></td>\n",
+       "        <td>0.45</td>\n",
+       "        <td>0.46</td>\n",
+       "        <td>0.45</td>\n",
+       "        <td>0.46</td>\n",
+       "        <td>0.45</td>\n",
+       "        <td>0.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "        <td>Gensim: batch_size=10, burn_in=0, epochs=50, neg=20, reg=0.0</td>\n",
+       "        <td>spearman<br></td>\n",
+       "        <td>0.47</td>\n",
+       "        <td>0.45</td>\n",
+       "        <td>0.47</td>\n",
+       "        <td>0.47</td>\n",
+       "        <td>0.48</td>\n",
+       "        <td>0.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "        <td>Gensim: batch_size=10, burn_in=0, epochs=50, neg=10, reg=0.0</td>\n",
+       "        <td>spearman<br></td>\n",
+       "        <td>0.46</td>\n",
+       "        <td>0.46</td>\n",
+       "        <td>0.45</td>\n",
+       "        <td>0.47</td>\n",
+       "        <td>0.47</td>\n",
+       "        <td>0.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "        <td>Gensim: batch_size=50, burn_in=0, epochs=50, neg=20, reg=0.0</td>\n",
+       "        <td>spearman<br></td>\n",
+       "        <td>0.46</td>\n",
+       "        <td>0.46</td>\n",
+       "        <td>0.47</td>\n",
+       "        <td>0.47</td>\n",
+       "        <td>0.48</td>\n",
+       "        <td>0.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "        <td>Gensim: batch_size=10, burn_in=10, epochs=200, neg=10, reg=1</td>\n",
+       "        <td>spearman<br></td>\n",
+       "        <td>0.52</td>\n",
+       "        <td>0.51</td>\n",
+       "        <td>0.51</td>\n",
+       "        <td>0.51</td>\n",
+       "        <td>0.52</td>\n",
+       "        <td>0.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "        <td>Numpy: epochs=50, neg=20</td>\n",
+       "        <td>spearman<br></td>\n",
+       "        <td>0.15</td>\n",
+       "        <td>0.19</td>\n",
+       "        <td>0.20</td>\n",
+       "        <td>0.20</td>\n",
+       "        <td>0.24</td>\n",
+       "        <td>0.26</td>\n",
+       "    </tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -1301,17 +1666,49 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 4.4 Link Prediction for collaboration networks\n"
+    "Results from paper (for Poincaré Embeddings, as well as other embeddings from previous papers) - \n",
+    "![LE Results](https://raw.githubusercontent.com/RaRe-Technologies/gensim/poincare_model_keyedvectors/docs/notebooks/poincare/entailment_paper.png)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 68,
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Some observations - \n",
+    "1. We achieve a max spearman score of 0.48, fairly close to the spearman score of 0.512 mentioned in the paper.\n",
+    "2. The best results are obtained with 20 negative examples, a batch size of 10, and no burn-in, however the differences are too low to make a meaningful conclusion.\n",
+    "\n",
+    "However, there are a few ambiguities and caveats - \n",
+    "1. The paper does not mention which hyperparameters and model size have been used for the above mentioned result. Hence it is possible that the results are achieved with a significantly lower model size than the one we use, which would imply that our implementation still has some way to go.\n",
+    "2. The same word can have multiple nodes in the WordNet dataset for different senses of the word, and it is unclear in the paper how to decide which node to pick. For the above results, we have gone with the sane default of picking the particular sense that has the maximum similarity score with the target word.\n",
+    "3. Certain words in the HyperLex dataset seem to be absent from the WordNet data - the paper does not mention any such thing. Pairs containing missing words have been omitted from the evaluation (182/2163).\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.4 Link Prediction on Collaboration Networks\n",
+    "\n",
+    "The paper also describes a variant of the Poincaré model to learn embeddings of nodes in a symmetric graph, unlike the WordNet noun hierarchy, which is directed and asymmetric. The datasets used in the paper for this model are scientific collaboration networks, in which the nodes are researchers and an edge represents that the two researchers have co-authored a paper.\n",
+    "\n",
+    "This variant has not been implemented yet, and is therefore not a part of our experiments."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Next Steps"
+   ]
+  },
+  {
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# TODO - quite tricky, since the loss function used for training the model on this network is different\n",
-    "# Will require changes to how gradients are calculated in C++ code"
+    "1. The model can be investigated further to understand why it doesn't produce results as good as the paper. It is possible that this might be due to training details not present in the paper, or due to us incorrectly interpreting some ambiguous parts of the paper. We have not been able to clarify all such ambiguitities in communication with the authors.\n",
+    "2. Optimizing the training process further - with a model size of 50 dimensions and a dataset with ~700k relations and ~80k nodes, the Gensim implementation takes around 45 seconds to complete an epoch (~15k relations per second), whereas the open source C++ implementation takes around 1/6th the time (~95k relations per second).\n",
+    "3. Implementing the variant of the model mentioned in the paper for symmetric graphs and evaluating on the scientific collaboration datasets described earlier in the report."
    ]
   }
  ],
@@ -1331,7 +1728,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.5.4"
   }
  },
  "nbformat": 4,
diff --git a/docs/notebooks/Poincare Tutorial.ipynb b/docs/notebooks/Poincare Tutorial.ipynb
index 88eff23862..c7eeb23b3f 100644
--- a/docs/notebooks/Poincare Tutorial.ipynb	
+++ b/docs/notebooks/Poincare Tutorial.ipynb	
@@ -11,7 +11,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This notebook discusses the motivation and idea behind Poincaré embeddings and demonstrates what kind of operations can be done with them. It also presents quantitative evaluation results on evaluation tasks mentioned in the [original paper](https://arxiv.org/pdf/1705.08039.pdf), and compares them to results obtained from other, non-gensim implementations. Lastly, it tries to provide some intuition behind the nature of the embeddings and hyperbolic space through visualizations."
+    "This notebook discusses the basic ideas and use-cases for Poincaré embeddings and demonstrates what kind of operations can be done with them. For more comprehensive technical details and results, this [blog post](https://rare-technologies.com/implementing-poincare-embeddings) may be a more appropriate resource."
    ]
   },
   {
@@ -35,7 +35,7 @@
     "\n",
     "The paper also describes a variant of the Poincaré model to learn embeddings of nodes in a symmetric graph, unlike the WordNet noun hierarchy, which is directed and asymmetric. The datasets used in the paper for this model are scientific collaboration networks, in which the nodes are researchers and an edge represents that the two researchers have co-authored a paper.\n",
     "\n",
-    "This variant has not been implemented yet, and is therefore not a part of our experiments.\n",
+    "This variant has not been implemented yet, and is therefore not a part of our tutorial and experiments.\n",
     "\n",
     "\n",
     "### 1.2 Motivation\n",
@@ -44,7 +44,7 @@
     " \n",
     " ![Example tree](https://raw.githubusercontent.com/RaRe-Technologies/gensim/poincare_model_keyedvectors/docs/notebooks/poincare/example_tree.png)\n",
     "\n",
-    "Here, the positions of nodes represent their position in 2-D euclidean space. Ideally, the distances between the nodes `(A, D)` should be the same as that between `(D, H)` and as that between `H` and its child nodes. Similarly, all the child nodes of `H` must be equally far away from node `A`. It becomes progressively hard to accurately preserve these distances in Euclidean space as the degree and depth of the tree grows larger. Hierarchical structures may also have cross-connections (effectively a directed graph).\n",
+    "Here, the positions of nodes represent the positions of their vectors in 2-D euclidean space. Ideally, the distances between the vectors for nodes `(A, D)` should be the same as that between `(D, H)` and as that between `H` and its child nodes. Similarly, all the child nodes of `H` must be equally far away from node `A`. It becomes progressively hard to accurately preserve these distances in Euclidean space as the degree and depth of the tree grows larger. Hierarchical structures may also have cross-connections (effectively a directed graph), making this harder.\n",
     "\n",
     "There is no representation of this simple tree in 2-dimensional Euclidean space which can reflect these distances correctly. This can be solved by adding more dimensions, but this becomes computationally infeasible as the number of required dimensions grows exponentially. \n",
     "Hyperbolic space is a metric space in which distances aren't straight lines - they are curves, and this allows such tree-like hierarchical structures to have a representation that captures the distances more accurately even in low dimensions."
@@ -71,7 +71,6 @@
     }
    ],
    "source": [
-    "# Import required modules and train an example embedding\n",
     "% cd ../.."
    ]
   },
@@ -668,106886 +667,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 4. Evaluation"
+    "## 4. Useful Links"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The following section presents the results of Poincaré models trained using some open-source implementations of the model, as well as the Gensim implementation. Original results as mentioned in the paper are also provided for reference."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following two external, open-source implementations are used - \n",
-    "1. [C++](https://github.com/TatsuyaShirakawa/poincare-embedding)\n",
-    "2. [Numpy](https://github.com/nishnik/poincare_embeddings)\n",
-    "\n",
-    "This is the list of tasks from the paper that have been evaluated - \n",
-    "1. WordNet reconstruction\n",
-    "2. WordNet link prediction\n",
-    "4. Lexical entailment on HyperLex"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 4.1 WordNet Reconstruction"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For this task, embeddings are learnt using the entire transitive closure of the WordNet noun hypernym hierarchy. Subsequently, for every hypernym pair `(u, v)`, the rank of `v` amongst all nodes that do not have a positive edge with `v` is computed. The final metric `mean_rank` is the average of all these ranks. The `MAP` metric is the mean of the Average Precision of the rankings for all positive nodes for a given node `u`.\n",
-    "\n",
-    "Note that this task tests representation capacity of the learnt embeddings, and not the generalization ability.\n",
-    "\n",
-    "The prefix in the model names denote - \n",
-    "1. `cpp_`: Model trained using [open source C++ implementation](https://github.com/TatsuyaShirakawa/poincare-embedding)\n",
-    "2. `numpy_`: Model trained using [open source Numpy implementation](https://github.com/nishnik/poincare_embeddings)\n",
-    "3. `gensim_`: Model trained using Gensim implementation\n",
-    "\n",
-    "The rest of the model name contains information about the model hyperparameters used for training. \n",
-    "\n",
-    "For more details about the exact evaluation method, and to reproduce the results described above, refer to the detailed [evaluation notebook](http://nbviewer.jupyter.org/github/RaRe-Technologies/gensim/blob/9e3190f0e4e38f1c93804feb0a62ce025a8489d3/docs/notebooks/Poincare%20Evaluation.ipynb)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Our results -"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Reconstruction Results](https://raw.githubusercontent.com/RaRe-Technologies/gensim/poincare_model_keyedvectors/docs/notebooks/poincare/reconstruction_eval.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Results from the paper -\n",
-    "![Reconstruction Results](https://raw.githubusercontent.com/RaRe-Technologies/gensim/poincare_model_keyedvectors/docs/notebooks/poincare/reconstruction_paper.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The figures above illustrate a few things - \n",
-    "1. The gensim implementation does significantly better for all model sizes and hyperparameters than both the other implementations.\n",
-    "2. The results from the original paper have not been achieved completely. Especially for models with lower dimensions, the paper mentions significantly better mean rank and MAP for the reconstruction task.\n",
-    "3. Using a higher number of negatives or a different batch size does not have a consistently significant impact on the results.\n",
-    "4. Using burn-in leads to better results with low model sizes, however the results do not improve significantly with increasing model size."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 4.2 Link Prediction"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This task is similar to the reconstruction task described above, except that the list of relations is split into a training and testing set, and the mean rank reported is for the edges in the test set.\n",
-    "\n",
-    "Therefore, this tests the ability of the model to predict unseen edges between nodes, i.e. generalization ability, as opposed to the representation capacity tested in the Reconstruction task\n",
-    "\n",
-    "Our results -"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Link Prediction Eval](https://raw.githubusercontent.com/RaRe-Technologies/gensim/poincare_model_keyedvectors/docs/notebooks/poincare/link_prediction_eval.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Link Prediction Paper](https://raw.githubusercontent.com/RaRe-Technologies/gensim/poincare_model_keyedvectors/docs/notebooks/poincare/link_prediction_paper.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "These results follow similar trends to the reconstruction results. Specifically - \n",
-    "1. The gensim implementation does significantly better for all model sizes and hyperparameters than both the other implementations.\n",
-    "2. The results from the original paper have not been achieved completely. Especially for models with lower dimensions, the paper mentions significantly better mean rank and MAP for the reconstruction task.\n",
-    "3. Using a higher number of negatives or a different batch size does not have a consistently significant impact on the results.\n",
-    "4. Using burn-in leads to better results with low model sizes, however the results do not improve significantly with increasing model size.\n",
-    "\n",
-    "The main difference from the reconstruction results is that mean ranks for link prediction are slightly worse most of the time than the corresponding reconstruction results. This is to be expected, as link prediction is performed on a held-out test set."
-   ]
-  },
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-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 4.3 Lexical Entailment"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The Lexical Entailment task is performed using the HyperLex dataset, a collection of 2163 noun pairs and scores that denote \"To what degree is noun A a type of noun Y\". For example - \n",
-    "  \n",
-    "`girl person 9.85`\n",
-    "\n",
-    "These scores are out of 10.\n",
-    "\n",
-    "The [spearman's correlation score](https://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient) is computed for the predicted and actual similarity scores, with the models trained on the entire WordNet noun hierarchy.\n",
-    "\n",
-    "Our results - \n",
-    "![LE Results](https://raw.githubusercontent.com/RaRe-Technologies/gensim/poincare_model_keyedvectors/docs/notebooks/poincare/entailment_eval.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Results from paper (for Poincaré Embeddings, as well as other embeddings from previous papers) - \n",
-    "![LE Results](https://raw.githubusercontent.com/RaRe-Technologies/gensim/poincare_model_keyedvectors/docs/notebooks/poincare/entailment_paper.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Some observations - \n",
-    "1. We achieve a max spearman score of 0.48, fairly close to the spearman score of 0.512 mentioned in the paper.\n",
-    "2. The best results are obtained with 20 negative examples, a batch size of 10, and no burn-in, however the differences are too low to make a meaningful conclusion.\n",
-    "\n",
-    "However, there are a few ambiguities and caveats - \n",
-    "1. The paper does not mention which hyperparameters and model size have been used for the above mentioned result. Hence it is possible that the results are achieved with a significantly lower model size than the one we use, which would imply that our implementation still has some way to go.\n",
-    "2. The same word can have multiple nodes in the WordNet dataset for different senses of the word, and it is unclear in the paper how to decide which node to pick. For the above results, we have gone with the sane default of picking the particular sense that has the maximum similarity score with the target word.\n",
-    "3. Certain words in the HyperLex dataset seem to be absent from the WordNet data - the paper does not mention any such thing. Pairs containing missing words have been omitted from the evaluation (182/2163).\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 4.4 Link Prediction on Collaboration Networks\n",
-    "\n",
-    "The paper also describes a variant of the Poincaré model to learn embeddings of nodes in a symmetric graph, unlike the WordNet noun hierarchy, which is directed and asymmetric. The datasets used in the paper for this model are scientific collaboration networks, in which the nodes are researchers and an edge represents that the two researchers have co-authored a paper.\n",
-    "\n",
-    "This variant has not been implemented yet, and is therefore not a part of our experiments."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 5. Visualization"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The paper presents a visual representation of a 2-D model trained on the mammals subtree of the WordNet noun hierarchy. This is a useful tool to get an intuitive sense of the model, and is also helpful for the purposes of debugging where the model could be learning incorrect representations. Visualizations for some models are presented below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
-      ],
-      "text/vnd.plotly.v1+html": [
-       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import pickle\n",
-    "from plotly.offline import init_notebook_mode, iplot\n",
-    "from gensim.viz.poincare import poincare_2d_visualization, poincare_distance_heatmap\n",
-    "\n",
-    "init_notebook_mode(connected=True)\n",
-    "\n",
-    "show_node_labels = [\n",
-    "    'mammal.n.01', 'placental.n.01', 'ungulate.n.01', 'carnivore.n.01', 'rodent.n.01',\n",
-    "    'canine.n.02', 'even-toed_ungulate.n.01', 'odd-toed_ungulate.n.01', 'elephant.n.01',\n",
-    "    'rhinoceros.n.01', 'german_shepherd.n.01', 'feline.n.01', 'tiger.n.02', 'homo_sapiens.n.01']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tree = pickle.load(open(os.path.join(poincare_directory, 'data', 'mammal_tree.pkl'), 'rb'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:gensim.utils:loading PoincareModel object from /home/jayant/projects/gensim/docs/notebooks/poincare/models/gensim_mammals_epochs_50_dim_2\n",
-      "INFO:gensim.utils:loading kv recursively from /home/jayant/projects/gensim/docs/notebooks/poincare/models/gensim_mammals_epochs_50_dim_2.kv.* with mmap=None\n",
-      "INFO:gensim.utils:loaded /home/jayant/projects/gensim/docs/notebooks/poincare/models/gensim_mammals_epochs_50_dim_2\n"
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-          "rock_wallaby.n.01",
-          "carthorse.n.01",
-          "tamarau.n.01",
-          "buckskin.n.01",
-          "brahman.n.04",
-          "bonnet_macaque.n.01",
-          "anthropoid.n.02",
-          "entlebucher.n.01",
-          "pinniped_mammal.n.01",
-          "newfoundland.n.01",
-          "durham.n.02",
-          "wood_mouse.n.01",
-          "assault.n.03",
-          "aardvark.n.01",
-          "giant_schnauzer.n.01",
-          "common_wallaby.n.01",
-          "mantled_ground_squirrel.n.01",
-          "bullterrier.n.01",
-          "homo.n.02",
-          "gorilla.n.01",
-          "great_ape.n.01",
-          "jackrabbit.n.01",
-          "roe_deer.n.01",
-          "imperial_mammoth.n.01",
-          "elephant.n.01",
-          "rabbit-eared_bandicoot.n.01",
-          "chickeree.n.01",
-          "flickertail.n.01",
-          "reynard.n.01",
-          "clydesdale_terrier.n.01",
-          "bear_cub.n.01",
-          "bear.n.01",
-          "stoat.n.01",
-          "rhodesian_man.n.01",
-          "ram.n.05",
-          "sheep.n.01",
-          "santa_gertrudis.n.01",
-          "mountain_sheep.n.01",
-          "eastern_chimpanzee.n.01",
-          "pouched_mouse.n.01",
-          "pacific_bottlenose_dolphin.n.01",
-          "attack_dog.n.01",
-          "ungulata.n.01",
-          "deer.n.01",
-          "domestic_sheep.n.01",
-          "welsh.n.03",
-          "solo_man.n.01",
-          "great_dane.n.01",
-          "pithecanthropus.n.01",
-          "lhasa.n.02",
-          "italian_greyhound.n.01",
-          "bull.n.11",
-          "jersey.n.05",
-          "polo_pony.n.01",
-          "hare.n.01",
-          "working_dog.n.01",
-          "trotting_horse.n.01",
-          "wirehair.n.01",
-          "lionet.n.01",
-          "zebu.n.01",
-          "glutton.n.02",
-          "irish_water_spaniel.n.01",
-          "sealyham_terrier.n.01",
-          "musk_kangaroo.n.01",
-          "water_chevrotain.n.01",
-          "baronduki.n.01",
-          "guinea_pig.n.02",
-          "english_springer.n.01",
-          "viscacha.n.01",
-          "harnessed_antelope.n.01",
-          "chesapeake_bay_retriever.n.01",
-          "numbat.n.01",
-          "dasyurid_marsupial.n.01",
-          "large_poodle.n.01",
-          "roan.n.02",
-          "entellus.n.01",
-          "old_world_monkey.n.01",
-          "squirrel_monkey.n.01",
-          "wild_goat.n.01",
-          "anoa.n.01",
-          "native_cat.n.01",
-          "bandicoot.n.01",
-          "mare.n.01",
-          "river_dolphin.n.01",
-          "wharf_rat.n.02",
-          "cardigan.n.02",
-          "saki.n.03",
-          "zoril.n.01",
-          "dark_horse.n.02",
-          "world.n.08",
-          "schnauzer.n.01",
-          "irish_terrier.n.01",
-          "keeshond.n.01",
-          "aardwolf.n.01",
-          "pembroke.n.01",
-          "foxhound.n.01",
-          "red_fox.n.03",
-          "hooded_skunk.n.01",
-          "woolly_indris.n.01",
-          "miniature_poodle.n.01",
-          "pocket_rat.n.01",
-          "japanese_deer.n.01",
-          "lesser_ape.n.01",
-          "slender_loris.n.01",
-          "cow.n.02",
-          "merino.n.01",
-          "bullock.n.01",
-          "cavy.n.01",
-          "fur_seal.n.02",
-          "jird.n.01",
-          "protohippus.n.01",
-          "greater_swiss_mountain_dog.n.01",
-          "capuchin.n.02",
-          "chigetai.n.01",
-          "southern_flying_squirrel.n.01",
-          "mangabey.n.01",
-          "sled_dog.n.01",
-          "spouter.n.03",
-          "tennessee_walker.n.01",
-          "pangolin.n.01",
-          "old_world_porcupine.n.01",
-          "sewer_rat.n.01",
-          "brown_rat.n.01",
-          "swamp_rabbit.n.02",
-          "cheviot.n.01",
-          "collared_peccary.n.01",
-          "mountain_goat.n.01",
-          "golden_retriever.n.01",
-          "yorkshire_terrier.n.01",
-          "pachyderm.n.01",
-          "fisher.n.02",
-          "tenrec.n.01",
-          "jumping_mouse.n.01",
-          "cinnamon_bear.n.01",
-          "american_black_bear.n.01",
-          "liver_chestnut.n.01",
-          "howler_monkey.n.01",
-          "sow.n.01",
-          "manchester_terrier.n.01",
-          "king_charles_spaniel.n.01",
-          "mouser.n.01",
-          "american_shrew_mole.n.01",
-          "peba.n.01",
-          "asiatic_shrew_mole.n.01",
-          "hudson_bay_collared_lemming.n.01",
-          "bunny.n.02",
-          "rat_terrier.n.01",
-          "langur.n.01",
-          "european_wood_mouse.n.01",
-          "nude_mouse.n.01",
-          "mole.n.06",
-          "mule.n.01",
-          "barren_ground_caribou.n.01",
-          "galago.n.01",
-          "paca.n.01",
-          "wolfhound.n.01",
-          "mountain_nyala.n.01",
-          "bezoar_goat.n.01",
-          "muntjac.n.01",
-          "packrat.n.02",
-          "skye_terrier.n.01",
-          "douroucouli.n.01",
-          "prairie_dog.n.01",
-          "lucy.n.01",
-          "hack.n.07",
-          "seeing_eye_dog.n.01",
-          "grivet.n.01",
-          "rhodesian_ridgeback.n.01",
-          "manatee.n.01",
-          "farm_horse.n.01",
-          "quagga.n.01",
-          "bouvier_des_flandres.n.01",
-          "poodle.n.01",
-          "stepper.n.03",
-          "rottweiler.n.01",
-          "tarsius_syrichta.n.01",
-          "affenpinscher.n.01",
-          "tailless_tenrec.n.01",
-          "eastern_dasyure.n.01",
-          "dasyure.n.01",
-          "elk.n.01",
-          "boarhound.n.01",
-          "woolly_rhinoceros.n.01",
-          "rambouillet.n.01",
-          "bowhead.n.01",
-          "grasshopper_mouse.n.01",
-          "dall_sheep.n.01",
-          "gerbil.n.01",
-          "prairie_vole.n.01",
-          "finback.n.01",
-          "polar_hare.n.01",
-          "hairy-legged_vampire_bat.n.01",
-          "white-lipped_peccary.n.01",
-          "peccary.n.01",
-          "dwarf_sperm_whale.n.01",
-          "mule_deer.n.01",
-          "sausage_dog.n.01",
-          "female_mammal.n.01",
-          "digitigrade_mammal.n.01",
-          "siamang.n.01",
-          "kanchil.n.01",
-          "chevrotain.n.01",
-          "giant_eland.n.01",
-          "timber_wolf.n.01",
-          "european_hare.n.01",
-          "guernsey.n.02",
-          "warrigal.n.01",
-          "european_rabbit.n.01",
-          "pug.n.01",
-          "white_wolf.n.01",
-          "new_world_mouse.n.01",
-          "beaked_whale.n.01",
-          "leonberg.n.01",
-          "pinche.n.01",
-          "big_brown_bat.n.01",
-          "mexican_hairless.n.01",
-          "american_marten.n.01",
-          "asian_wild_ox.n.01",
-          "steller's_sea_cow.n.01",
-          "old_world_buffalo.n.01",
-          "ord_kangaroo_rat.n.01",
-          "kangaroo_rat.n.01",
-          "lynx.n.02",
-          "alley_cat.n.01",
-          "serow.n.01",
-          "grison.n.01",
-          "common_eland.n.01",
-          "phyllostomus_hastatus.n.01",
-          "grizzly.n.01",
-          "eland.n.01",
-          "silky_pocket_mouse.n.01",
-          "elephant_seal.n.01",
-          "starnose_mole.n.01",
-          "gazella_subgutturosa.n.01",
-          "kerry_blue_terrier.n.01",
-          "vespertilian_bat.n.01",
-          "colobus.n.01",
-          "sea_otter.n.01",
-          "flying_fox.n.01",
-          "black-tailed_deer.n.01",
-          "mouse-eared_bat.n.01",
-          "chestnut.n.06",
-          "burro.n.01",
-          "kid.n.05",
-          "hereford.n.01",
-          "snow_leopard.n.01",
-          "indri.n.01",
-          "cocker_spaniel.n.01",
-          "kob.n.01",
-          "european_lemming.n.01",
-          "carabao.n.01",
-          "harbor_seal.n.01",
-          "shetland_pony.n.01",
-          "water_spaniel.n.01",
-          "grey_whale.n.01",
-          "papillon.n.01",
-          "toy_dog.n.01",
-          "draft_horse.n.01",
-          "eastern_grey_squirrel.n.01",
-          "arabian.n.02",
-          "northern_bog_lemming.n.01",
-          "lemming.n.01",
-          "weimaraner.n.01",
-          "civet.n.01",
-          "saiga.n.01",
-          "jennet.n.01",
-          "edentate.n.01",
-          "pilot_whale.n.01",
-          "dolphin.n.02",
-          "grey_fox.n.01",
-          "grampus.n.02",
-          "devon.n.02",
-          "walker_hound.n.01",
-          "shrew_mole.n.01",
-          "chihuahua.n.03",
-          "redbone.n.01",
-          "borzoi.n.01",
-          "mapinguari.n.01",
-          "binturong.n.01",
-          "scottish_deerhound.n.01",
-          "greyhound.n.01",
-          "field_spaniel.n.01",
-          "griffon.n.03",
-          "forest_goat.n.01",
-          "pocket_mouse.n.01",
-          "hart.n.03",
-          "pere_david's_deer.n.01",
-          "algeripithecus_minutus.n.01",
-          "red_bat.n.01",
-          "vixen.n.02",
-          "malamute.n.01",
-          "western_grey_squirrel.n.01",
-          "striped_muishond.n.01",
-          "gallant_fox.n.01",
-          "margay.n.01",
-          "saber-toothed_tiger.n.01",
-          "groenendael.n.01",
-          "silver_fox.n.01",
-          "jack.n.12",
-          "bengal_tiger.n.01",
-          "mandrill.n.01",
-          "asiatic_black_bear.n.01",
-          "pied_lemming.n.01",
-          "lioness.n.01",
-          "bloodhound.n.01",
-          "australopithecus_boisei.n.01",
-          "milking_shorthorn.n.01",
-          "jerboa.n.01",
-          "bongo.n.02",
-          "norfolk_terrier.n.01",
-          "serotine.n.01",
-          "rock_hyrax.n.01",
-          "welsh_terrier.n.01",
-          "west_highland_white_terrier.n.01",
-          "water_dog.n.02",
-          "red_deer.n.01",
-          "takin.n.01",
-          "blue_whale.n.01",
-          "hooded_seal.n.01",
-          "secretariat.n.02",
-          "cape_buffalo.n.01",
-          "stone_marten.n.01",
-          "whippet.n.01",
-          "aoudad.n.01",
-          "bottlenose_dolphin.n.01",
-          "lechwe.n.01",
-          "lion.n.01",
-          "hartebeest.n.01",
-          "red_poll.n.01",
-          "eastern_cottontail.n.01",
-          "murine.n.01",
-          "sussex_spaniel.n.01",
-          "leopard_cat.n.01",
-          "coydog.n.01",
-          "big-eared_bat.n.01",
-          "java_man.n.01",
-          "coyote.n.01",
-          "macaque.n.01",
-          "american_harvest_mouse.n.01",
-          "belgian_sheepdog.n.01",
-          "eohippus.n.01",
-          "ibex.n.01",
-          "longwool.n.01",
-          "nilgai.n.01",
-          "quarter_horse.n.01",
-          "africander.n.01",
-          "stalking-horse.n.04",
-          "apar.n.01",
-          "three-toed_sloth.n.01",
-          "indian_elephant.n.01",
-          "gayal.n.01",
-          "malayan_tapir.n.01",
-          "cairn.n.02",
-          "chiacoan_peccary.n.01",
-          "rhinoceros.n.01",
-          "naked_mole_rat.n.01",
-          "loir.n.01",
-          "hack.n.08",
-          "eskimo_dog.n.01",
-          "queen.n.09",
-          "south_american_sea_lion.n.01",
-          "lippizan.n.01",
-          "smiledon_californicus.n.01",
-          "norwegian_elkhound.n.01",
-          "stirk.n.01",
-          "mustang.n.01",
-          "cayuse.n.01",
-          "courser.n.03",
-          "paranthropus.n.01",
-          "hominoid.n.01",
-          "napu.n.01",
-          "bushbuck.n.01",
-          "atlantic_bottlenose_dolphin.n.01",
-          "ayrshire.n.01",
-          "border_collie.n.01",
-          "norwich_terrier.n.01",
-          "liger.n.01",
-          "oryx.n.01",
-          "pine_vole.n.01",
-          "european_water_shrew.n.01",
-          "two-toed_sloth.n.02",
-          "sloth.n.02",
-          "bison.n.01",
-          "burmese_cat.n.01",
-          "wolverine.n.03",
-          "aye-aye.n.01",
-          "puku.n.01",
-          "mastiff.n.01",
-          "common_opossum.n.01",
-          "potto.n.02",
-          "typical_jerboa.n.01",
-          "house_mouse.n.01",
-          "tarsius_glis.n.01",
-          "tarsier.n.01",
-          "slender-tailed_meerkat.n.01",
-          "cow_pony.n.01",
-          "fox_squirrel.n.01",
-          "bay.n.07",
-          "madagascar_cat.n.01",
-          "australopithecus_afarensis.n.01",
-          "australopithecine.n.01",
-          "livestock.n.01",
-          "lerot.n.01",
-          "pole_horse.n.01",
-          "banded_palm_civet.n.01",
-          "american_red_squirrel.n.01",
-          "mara.n.02",
-          "pteropus_hypomelanus.n.01",
-          "meerkat.n.01",
-          "bernese_mountain_dog.n.01",
-          "przewalski's_horse.n.01",
-          "sea_lion.n.01",
-          "black_rhinoceros.n.01",
-          "onager.n.02",
-          "common_zebra.n.01",
-          "narwhal.n.01",
-          "pouched_mole.n.01",
-          "crab-eating_opossum.n.01",
-          "pollard.n.02",
-          "porpoise.n.01",
-          "brush-tailed_phalanger.n.01",
-          "arabian_camel.n.01",
-          "camel.n.01",
-          "sei_whale.n.01",
-          "field_mouse.n.02",
-          "packhorse.n.01",
-          "lapin.n.02",
-          "palomino.n.01",
-          "prosimian.n.01",
-          "greater_kudu.n.01",
-          "macrotus.n.01",
-          "horseshoe_bat.n.02",
-          "pademelon.n.01",
-          "indian_buffalo.n.01",
-          "new_world_beaver.n.01",
-          "charger.n.01",
-          "irish_setter.n.01",
-          "hog-nosed_skunk.n.01",
-          "cow.n.01",
-          "ox.n.01",
-          "workhorse.n.02",
-          "tree_wallaby.n.01",
-          "pygmy_mouse.n.01",
-          "bearded_seal.n.01",
-          "guenon.n.01",
-          "brood_bitch.n.01",
-          "masked_shrew.n.01",
-          "sir_barton.n.01",
-          "cheetah.n.01",
-          "columbian_mammoth.n.01",
-          "giant_panda.n.01",
-          "american_bison.n.01",
-          "round-tailed_muskrat.n.01",
-          "gerenuk.n.01",
-          "smooth-haired_fox_terrier.n.01",
-          "platypus.n.01",
-          "monotreme.n.01",
-          "feist.n.01",
-          "jaculus_jaculus.n.01",
-          "wolf.n.01",
-          "coypu.n.01",
-          "remount.n.01",
-          "kit_fox.n.02",
-          "jaguarundi.n.01",
-          "american_mink.n.01",
-          "arctic_ground_squirrel.n.01",
-          "weasel.n.02",
-          "abrocome.n.01",
-          "pteropus_capestratus.n.01",
-          "fissiped_mammal.n.01",
-          "eurasian_badger.n.01",
-          "little_chief_hare.n.01",
-          "fossorial_mammal.n.01",
-          "phenacomys.n.01",
-          "gee-gee.n.01",
-          "blacktail_prairie_dog.n.01",
-          "little_brown_bat.n.01",
-          "hog_badger.n.01",
-          "pointer.n.04",
-          "large_civet.n.01",
-          "roebuck.n.01",
-          "bandicoot_rat.n.01",
-          "common_lynx.n.01",
-          "razorback.n.01",
-          "puppy.n.01",
-          "kelpie.n.02",
-          "pipistrelle.n.01",
-          "sable.n.05",
-          "pacer.n.01",
-          "brocket.n.01",
-          "alaska_fur_seal.n.01",
-          "hoary_marmot.n.01",
-          "marmot.n.01",
-          "alpaca.n.03",
-          "spotted_lynx.n.01",
-          "setter.n.02",
-          "dusky-footed_woodrat.n.01",
-          "stag.n.02",
-          "dik-dik.n.01",
-          "hippopotamus.n.01",
-          "mountain_beaver.n.01",
-          "argali.n.01",
-          "impala.n.01",
-          "mastodon.n.01",
-          "dryopithecine.n.01",
-          "pole_horse.n.02",
-          "true_marmoset.n.01",
-          "royal.n.02",
-          "plains_pocket_gopher.n.01",
-          "rat_kangaroo.n.01",
-          "ferret.n.02",
-          "australopithecus_robustus.n.01",
-          "patas.n.01",
-          "orange_bat.n.01",
-          "harvest_mouse.n.02",
-          "dinoceras.n.01",
-          "siberian_husky.n.01",
-          "water_vole.n.01",
-          "wolf_pup.n.01",
-          "briard.n.01",
-          "plow_horse.n.01",
-          "ice_bear.n.01",
-          "siamese_cat.n.01",
-          "southeastern_pocket_gopher.n.01",
-          "kaffir_cat.n.01",
-          "grey_lemming.n.01",
-          "cob.n.02",
-          "white_whale.n.01",
-          "american_water_spaniel.n.01",
-          "leopardess.n.01",
-          "tamandua.n.01",
-          "longtail_weasel.n.01",
-          "springer_spaniel.n.01",
-          "spider_monkey.n.01",
-          "skunk.n.04",
-          "brush-tailed_porcupine.n.01",
-          "fallow_deer.n.01",
-          "white_rhinoceros.n.01",
-          "crowbait.n.01",
-          "bactrian_camel.n.01",
-          "brown_hyena.n.01",
-          "cynocephalus_variegatus.n.01",
-          "flying_lemur.n.01",
-          "galloway.n.02",
-          "tiger.n.02",
-          "welsh_springer_spaniel.n.01",
-          "valley_pocket_gopher.n.01",
-          "mesohippus.n.01",
-          "mammoth.n.01",
-          "okapi.n.01",
-          "moke.n.01",
-          "angora.n.04",
-          "cougar.n.01",
-          "humpback.n.03",
-          "river_otter.n.01",
-          "otter.n.02",
-          "bitch.n.04",
-          "maltese_dog.n.01",
-          "yellowbelly_marmot.n.01",
-          "housedog.n.01",
-          "miniature_schnauzer.n.01",
-          "aurochs.n.02",
-          "guadalupe_fur_seal.n.01",
-          "suricate.n.01",
-          "ground_sloth.n.01",
-          "american_water_shrew.n.01",
-          "old_world_least_weasel.n.01",
-          "agouti.n.01",
-          "indian_mongoose.n.01",
-          "gazelle.n.01",
-          "crab-eating_dog.n.01",
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\"linkText\": \"Export to plot.ly\"})});</script>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
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-   ],
-   "source": [
-    "model = PoincareModel.load(os.path.join(models_directory, 'gensim_mammals_epochs_50_dim_2'))\n",
-    "figure_title = \"\"\"\n",
-    "<b>2-D Visualization of model trained on mammals subtree</b><br>\n",
-    "50 epochs, model hasn't converged\"\"\"\n",
-    "iplot(poincare_2d_visualization(model, tree, figure_title, show_node_labels=show_node_labels))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:gensim.utils:loading PoincareModel object from /home/jayant/projects/gensim/docs/notebooks/poincare/models/gensim_mammals_epochs_200_dim_2\n",
-      "INFO:gensim.utils:loading kv recursively from /home/jayant/projects/gensim/docs/notebooks/poincare/models/gensim_mammals_epochs_200_dim_2.kv.* with mmap=None\n",
-      "INFO:gensim.utils:loaded /home/jayant/projects/gensim/docs/notebooks/poincare/models/gensim_mammals_epochs_200_dim_2\n"
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-          "brocket.n.02",
-          "shrew.n.02",
-          "bucking_bronco.n.01",
-          "rock_wallaby.n.01",
-          "carthorse.n.01",
-          "tamarau.n.01",
-          "buckskin.n.01",
-          "brahman.n.04",
-          "bonnet_macaque.n.01",
-          "anthropoid.n.02",
-          "entlebucher.n.01",
-          "pinniped_mammal.n.01",
-          "newfoundland.n.01",
-          "durham.n.02",
-          "wood_mouse.n.01",
-          "assault.n.03",
-          "aardvark.n.01",
-          "giant_schnauzer.n.01",
-          "common_wallaby.n.01",
-          "mantled_ground_squirrel.n.01",
-          "bullterrier.n.01",
-          "homo.n.02",
-          "gorilla.n.01",
-          "great_ape.n.01",
-          "jackrabbit.n.01",
-          "roe_deer.n.01",
-          "imperial_mammoth.n.01",
-          "elephant.n.01",
-          "rabbit-eared_bandicoot.n.01",
-          "chickeree.n.01",
-          "flickertail.n.01",
-          "reynard.n.01",
-          "clydesdale_terrier.n.01",
-          "bear_cub.n.01",
-          "bear.n.01",
-          "stoat.n.01",
-          "rhodesian_man.n.01",
-          "ram.n.05",
-          "sheep.n.01",
-          "santa_gertrudis.n.01",
-          "mountain_sheep.n.01",
-          "eastern_chimpanzee.n.01",
-          "pouched_mouse.n.01",
-          "pacific_bottlenose_dolphin.n.01",
-          "attack_dog.n.01",
-          "ungulata.n.01",
-          "deer.n.01",
-          "domestic_sheep.n.01",
-          "welsh.n.03",
-          "solo_man.n.01",
-          "great_dane.n.01",
-          "pithecanthropus.n.01",
-          "lhasa.n.02",
-          "italian_greyhound.n.01",
-          "bull.n.11",
-          "jersey.n.05",
-          "polo_pony.n.01",
-          "hare.n.01",
-          "working_dog.n.01",
-          "trotting_horse.n.01",
-          "wirehair.n.01",
-          "lionet.n.01",
-          "zebu.n.01",
-          "glutton.n.02",
-          "irish_water_spaniel.n.01",
-          "sealyham_terrier.n.01",
-          "musk_kangaroo.n.01",
-          "water_chevrotain.n.01",
-          "baronduki.n.01",
-          "guinea_pig.n.02",
-          "english_springer.n.01",
-          "viscacha.n.01",
-          "harnessed_antelope.n.01",
-          "chesapeake_bay_retriever.n.01",
-          "numbat.n.01",
-          "dasyurid_marsupial.n.01",
-          "large_poodle.n.01",
-          "roan.n.02",
-          "entellus.n.01",
-          "old_world_monkey.n.01",
-          "squirrel_monkey.n.01",
-          "wild_goat.n.01",
-          "anoa.n.01",
-          "native_cat.n.01",
-          "bandicoot.n.01",
-          "mare.n.01",
-          "river_dolphin.n.01",
-          "wharf_rat.n.02",
-          "cardigan.n.02",
-          "saki.n.03",
-          "zoril.n.01",
-          "dark_horse.n.02",
-          "world.n.08",
-          "schnauzer.n.01",
-          "irish_terrier.n.01",
-          "keeshond.n.01",
-          "aardwolf.n.01",
-          "pembroke.n.01",
-          "foxhound.n.01",
-          "red_fox.n.03",
-          "hooded_skunk.n.01",
-          "woolly_indris.n.01",
-          "miniature_poodle.n.01",
-          "pocket_rat.n.01",
-          "japanese_deer.n.01",
-          "lesser_ape.n.01",
-          "slender_loris.n.01",
-          "cow.n.02",
-          "merino.n.01",
-          "bullock.n.01",
-          "cavy.n.01",
-          "fur_seal.n.02",
-          "jird.n.01",
-          "protohippus.n.01",
-          "greater_swiss_mountain_dog.n.01",
-          "capuchin.n.02",
-          "chigetai.n.01",
-          "southern_flying_squirrel.n.01",
-          "mangabey.n.01",
-          "sled_dog.n.01",
-          "spouter.n.03",
-          "tennessee_walker.n.01",
-          "pangolin.n.01",
-          "old_world_porcupine.n.01",
-          "sewer_rat.n.01",
-          "brown_rat.n.01",
-          "swamp_rabbit.n.02",
-          "cheviot.n.01",
-          "collared_peccary.n.01",
-          "mountain_goat.n.01",
-          "golden_retriever.n.01",
-          "yorkshire_terrier.n.01",
-          "pachyderm.n.01",
-          "fisher.n.02",
-          "tenrec.n.01",
-          "jumping_mouse.n.01",
-          "cinnamon_bear.n.01",
-          "american_black_bear.n.01",
-          "liver_chestnut.n.01",
-          "howler_monkey.n.01",
-          "sow.n.01",
-          "manchester_terrier.n.01",
-          "king_charles_spaniel.n.01",
-          "mouser.n.01",
-          "american_shrew_mole.n.01",
-          "peba.n.01",
-          "asiatic_shrew_mole.n.01",
-          "hudson_bay_collared_lemming.n.01",
-          "bunny.n.02",
-          "rat_terrier.n.01",
-          "langur.n.01",
-          "european_wood_mouse.n.01",
-          "nude_mouse.n.01",
-          "mole.n.06",
-          "mule.n.01",
-          "barren_ground_caribou.n.01",
-          "galago.n.01",
-          "paca.n.01",
-          "wolfhound.n.01",
-          "mountain_nyala.n.01",
-          "bezoar_goat.n.01",
-          "muntjac.n.01",
-          "packrat.n.02",
-          "skye_terrier.n.01",
-          "douroucouli.n.01",
-          "prairie_dog.n.01",
-          "lucy.n.01",
-          "hack.n.07",
-          "seeing_eye_dog.n.01",
-          "grivet.n.01",
-          "rhodesian_ridgeback.n.01",
-          "manatee.n.01",
-          "farm_horse.n.01",
-          "quagga.n.01",
-          "bouvier_des_flandres.n.01",
-          "poodle.n.01",
-          "stepper.n.03",
-          "rottweiler.n.01",
-          "tarsius_syrichta.n.01",
-          "affenpinscher.n.01",
-          "tailless_tenrec.n.01",
-          "eastern_dasyure.n.01",
-          "dasyure.n.01",
-          "elk.n.01",
-          "boarhound.n.01",
-          "woolly_rhinoceros.n.01",
-          "rambouillet.n.01",
-          "bowhead.n.01",
-          "grasshopper_mouse.n.01",
-          "dall_sheep.n.01",
-          "gerbil.n.01",
-          "prairie_vole.n.01",
-          "finback.n.01",
-          "polar_hare.n.01",
-          "hairy-legged_vampire_bat.n.01",
-          "white-lipped_peccary.n.01",
-          "peccary.n.01",
-          "dwarf_sperm_whale.n.01",
-          "mule_deer.n.01",
-          "sausage_dog.n.01",
-          "female_mammal.n.01",
-          "digitigrade_mammal.n.01",
-          "siamang.n.01",
-          "kanchil.n.01",
-          "chevrotain.n.01",
-          "giant_eland.n.01",
-          "timber_wolf.n.01",
-          "european_hare.n.01",
-          "guernsey.n.02",
-          "warrigal.n.01",
-          "european_rabbit.n.01",
-          "pug.n.01",
-          "white_wolf.n.01",
-          "new_world_mouse.n.01",
-          "beaked_whale.n.01",
-          "leonberg.n.01",
-          "pinche.n.01",
-          "big_brown_bat.n.01",
-          "mexican_hairless.n.01",
-          "american_marten.n.01",
-          "asian_wild_ox.n.01",
-          "steller's_sea_cow.n.01",
-          "old_world_buffalo.n.01",
-          "ord_kangaroo_rat.n.01",
-          "kangaroo_rat.n.01",
-          "lynx.n.02",
-          "alley_cat.n.01",
-          "serow.n.01",
-          "grison.n.01",
-          "common_eland.n.01",
-          "phyllostomus_hastatus.n.01",
-          "grizzly.n.01",
-          "eland.n.01",
-          "silky_pocket_mouse.n.01",
-          "elephant_seal.n.01",
-          "starnose_mole.n.01",
-          "gazella_subgutturosa.n.01",
-          "kerry_blue_terrier.n.01",
-          "vespertilian_bat.n.01",
-          "colobus.n.01",
-          "sea_otter.n.01",
-          "flying_fox.n.01",
-          "black-tailed_deer.n.01",
-          "mouse-eared_bat.n.01",
-          "chestnut.n.06",
-          "burro.n.01",
-          "kid.n.05",
-          "hereford.n.01",
-          "snow_leopard.n.01",
-          "indri.n.01",
-          "cocker_spaniel.n.01",
-          "kob.n.01",
-          "european_lemming.n.01",
-          "carabao.n.01",
-          "harbor_seal.n.01",
-          "shetland_pony.n.01",
-          "water_spaniel.n.01",
-          "grey_whale.n.01",
-          "papillon.n.01",
-          "toy_dog.n.01",
-          "draft_horse.n.01",
-          "eastern_grey_squirrel.n.01",
-          "arabian.n.02",
-          "northern_bog_lemming.n.01",
-          "lemming.n.01",
-          "weimaraner.n.01",
-          "civet.n.01",
-          "saiga.n.01",
-          "jennet.n.01",
-          "edentate.n.01",
-          "pilot_whale.n.01",
-          "dolphin.n.02",
-          "grey_fox.n.01",
-          "grampus.n.02",
-          "devon.n.02",
-          "walker_hound.n.01",
-          "shrew_mole.n.01",
-          "chihuahua.n.03",
-          "redbone.n.01",
-          "borzoi.n.01",
-          "mapinguari.n.01",
-          "binturong.n.01",
-          "scottish_deerhound.n.01",
-          "greyhound.n.01",
-          "field_spaniel.n.01",
-          "griffon.n.03",
-          "forest_goat.n.01",
-          "pocket_mouse.n.01",
-          "hart.n.03",
-          "pere_david's_deer.n.01",
-          "algeripithecus_minutus.n.01",
-          "red_bat.n.01",
-          "vixen.n.02",
-          "malamute.n.01",
-          "western_grey_squirrel.n.01",
-          "striped_muishond.n.01",
-          "gallant_fox.n.01",
-          "margay.n.01",
-          "saber-toothed_tiger.n.01",
-          "groenendael.n.01",
-          "silver_fox.n.01",
-          "jack.n.12",
-          "bengal_tiger.n.01",
-          "mandrill.n.01",
-          "asiatic_black_bear.n.01",
-          "pied_lemming.n.01",
-          "lioness.n.01",
-          "bloodhound.n.01",
-          "australopithecus_boisei.n.01",
-          "milking_shorthorn.n.01",
-          "jerboa.n.01",
-          "bongo.n.02",
-          "norfolk_terrier.n.01",
-          "serotine.n.01",
-          "rock_hyrax.n.01",
-          "welsh_terrier.n.01",
-          "west_highland_white_terrier.n.01",
-          "water_dog.n.02",
-          "red_deer.n.01",
-          "takin.n.01",
-          "blue_whale.n.01",
-          "hooded_seal.n.01",
-          "secretariat.n.02",
-          "cape_buffalo.n.01",
-          "stone_marten.n.01",
-          "whippet.n.01",
-          "aoudad.n.01",
-          "bottlenose_dolphin.n.01",
-          "lechwe.n.01",
-          "lion.n.01",
-          "hartebeest.n.01",
-          "red_poll.n.01",
-          "eastern_cottontail.n.01",
-          "murine.n.01",
-          "sussex_spaniel.n.01",
-          "leopard_cat.n.01",
-          "coydog.n.01",
-          "big-eared_bat.n.01",
-          "java_man.n.01",
-          "coyote.n.01",
-          "macaque.n.01",
-          "american_harvest_mouse.n.01",
-          "belgian_sheepdog.n.01",
-          "eohippus.n.01",
-          "ibex.n.01",
-          "longwool.n.01",
-          "nilgai.n.01",
-          "quarter_horse.n.01",
-          "africander.n.01",
-          "stalking-horse.n.04",
-          "apar.n.01",
-          "three-toed_sloth.n.01",
-          "indian_elephant.n.01",
-          "gayal.n.01",
-          "malayan_tapir.n.01",
-          "cairn.n.02",
-          "chiacoan_peccary.n.01",
-          "rhinoceros.n.01",
-          "naked_mole_rat.n.01",
-          "loir.n.01",
-          "hack.n.08",
-          "eskimo_dog.n.01",
-          "queen.n.09",
-          "south_american_sea_lion.n.01",
-          "lippizan.n.01",
-          "smiledon_californicus.n.01",
-          "norwegian_elkhound.n.01",
-          "stirk.n.01",
-          "mustang.n.01",
-          "cayuse.n.01",
-          "courser.n.03",
-          "paranthropus.n.01",
-          "hominoid.n.01",
-          "napu.n.01",
-          "bushbuck.n.01",
-          "atlantic_bottlenose_dolphin.n.01",
-          "ayrshire.n.01",
-          "border_collie.n.01",
-          "norwich_terrier.n.01",
-          "liger.n.01",
-          "oryx.n.01",
-          "pine_vole.n.01",
-          "european_water_shrew.n.01",
-          "two-toed_sloth.n.02",
-          "sloth.n.02",
-          "bison.n.01",
-          "burmese_cat.n.01",
-          "wolverine.n.03",
-          "aye-aye.n.01",
-          "puku.n.01",
-          "mastiff.n.01",
-          "common_opossum.n.01",
-          "potto.n.02",
-          "typical_jerboa.n.01",
-          "house_mouse.n.01",
-          "tarsius_glis.n.01",
-          "tarsier.n.01",
-          "slender-tailed_meerkat.n.01",
-          "cow_pony.n.01",
-          "fox_squirrel.n.01",
-          "bay.n.07",
-          "madagascar_cat.n.01",
-          "australopithecus_afarensis.n.01",
-          "australopithecine.n.01",
-          "livestock.n.01",
-          "lerot.n.01",
-          "pole_horse.n.01",
-          "banded_palm_civet.n.01",
-          "american_red_squirrel.n.01",
-          "mara.n.02",
-          "pteropus_hypomelanus.n.01",
-          "meerkat.n.01",
-          "bernese_mountain_dog.n.01",
-          "przewalski's_horse.n.01",
-          "sea_lion.n.01",
-          "black_rhinoceros.n.01",
-          "onager.n.02",
-          "common_zebra.n.01",
-          "narwhal.n.01",
-          "pouched_mole.n.01",
-          "crab-eating_opossum.n.01",
-          "pollard.n.02",
-          "porpoise.n.01",
-          "brush-tailed_phalanger.n.01",
-          "arabian_camel.n.01",
-          "camel.n.01",
-          "sei_whale.n.01",
-          "field_mouse.n.02",
-          "packhorse.n.01",
-          "lapin.n.02",
-          "palomino.n.01",
-          "prosimian.n.01",
-          "greater_kudu.n.01",
-          "macrotus.n.01",
-          "horseshoe_bat.n.02",
-          "pademelon.n.01",
-          "indian_buffalo.n.01",
-          "new_world_beaver.n.01",
-          "charger.n.01",
-          "irish_setter.n.01",
-          "hog-nosed_skunk.n.01",
-          "cow.n.01",
-          "ox.n.01",
-          "workhorse.n.02",
-          "tree_wallaby.n.01",
-          "pygmy_mouse.n.01",
-          "bearded_seal.n.01",
-          "guenon.n.01",
-          "brood_bitch.n.01",
-          "masked_shrew.n.01",
-          "sir_barton.n.01",
-          "cheetah.n.01",
-          "columbian_mammoth.n.01",
-          "giant_panda.n.01",
-          "american_bison.n.01",
-          "round-tailed_muskrat.n.01",
-          "gerenuk.n.01",
-          "smooth-haired_fox_terrier.n.01",
-          "platypus.n.01",
-          "monotreme.n.01",
-          "feist.n.01",
-          "jaculus_jaculus.n.01",
-          "wolf.n.01",
-          "coypu.n.01",
-          "remount.n.01",
-          "kit_fox.n.02",
-          "jaguarundi.n.01",
-          "american_mink.n.01",
-          "arctic_ground_squirrel.n.01",
-          "weasel.n.02",
-          "abrocome.n.01",
-          "pteropus_capestratus.n.01",
-          "fissiped_mammal.n.01",
-          "eurasian_badger.n.01",
-          "little_chief_hare.n.01",
-          "fossorial_mammal.n.01",
-          "phenacomys.n.01",
-          "gee-gee.n.01",
-          "blacktail_prairie_dog.n.01",
-          "little_brown_bat.n.01",
-          "hog_badger.n.01",
-          "pointer.n.04",
-          "large_civet.n.01",
-          "roebuck.n.01",
-          "bandicoot_rat.n.01",
-          "common_lynx.n.01",
-          "razorback.n.01",
-          "puppy.n.01",
-          "kelpie.n.02",
-          "pipistrelle.n.01",
-          "sable.n.05",
-          "pacer.n.01",
-          "brocket.n.01",
-          "alaska_fur_seal.n.01",
-          "hoary_marmot.n.01",
-          "marmot.n.01",
-          "alpaca.n.03",
-          "spotted_lynx.n.01",
-          "setter.n.02",
-          "dusky-footed_woodrat.n.01",
-          "stag.n.02",
-          "dik-dik.n.01",
-          "hippopotamus.n.01",
-          "mountain_beaver.n.01",
-          "argali.n.01",
-          "impala.n.01",
-          "mastodon.n.01",
-          "dryopithecine.n.01",
-          "pole_horse.n.02",
-          "true_marmoset.n.01",
-          "royal.n.02",
-          "plains_pocket_gopher.n.01",
-          "rat_kangaroo.n.01",
-          "ferret.n.02",
-          "australopithecus_robustus.n.01",
-          "patas.n.01",
-          "orange_bat.n.01",
-          "harvest_mouse.n.02",
-          "dinoceras.n.01",
-          "siberian_husky.n.01",
-          "water_vole.n.01",
-          "wolf_pup.n.01",
-          "briard.n.01",
-          "plow_horse.n.01",
-          "ice_bear.n.01",
-          "siamese_cat.n.01",
-          "southeastern_pocket_gopher.n.01",
-          "kaffir_cat.n.01",
-          "grey_lemming.n.01",
-          "cob.n.02",
-          "white_whale.n.01",
-          "american_water_spaniel.n.01",
-          "leopardess.n.01",
-          "tamandua.n.01",
-          "longtail_weasel.n.01",
-          "springer_spaniel.n.01",
-          "spider_monkey.n.01",
-          "skunk.n.04",
-          "brush-tailed_porcupine.n.01",
-          "fallow_deer.n.01",
-          "white_rhinoceros.n.01",
-          "crowbait.n.01",
-          "bactrian_camel.n.01",
-          "brown_hyena.n.01",
-          "cynocephalus_variegatus.n.01",
-          "flying_lemur.n.01",
-          "galloway.n.02",
-          "tiger.n.02",
-          "welsh_springer_spaniel.n.01",
-          "valley_pocket_gopher.n.01",
-          "mesohippus.n.01",
-          "mammoth.n.01",
-          "okapi.n.01",
-          "moke.n.01",
-          "angora.n.04",
-          "cougar.n.01",
-          "humpback.n.03",
-          "river_otter.n.01",
-          "otter.n.02",
-          "bitch.n.04",
-          "maltese_dog.n.01",
-          "yellowbelly_marmot.n.01",
-          "housedog.n.01",
-          "miniature_schnauzer.n.01",
-          "aurochs.n.02",
-          "guadalupe_fur_seal.n.01",
-          "suricate.n.01",
-          "ground_sloth.n.01",
-          "american_water_shrew.n.01",
-          "old_world_least_weasel.n.01",
-          "agouti.n.01",
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\"tiger.n.02\", \"homo_sapiens.n.01\"], \"mode\": \"markers+text\", \"textposition\": \"bottom\", \"x\": [-0.05010500674502724, 0.11451627274425251, -0.756037492343237, 0.9372625138073847, -0.658548383207663, 0.9188692857569626, -0.9040980095816089, -0.17267991240321653, 0.11165685971397364, 0.19179824692125683, 0.8152021551925026, 0.4227710646614852, 0.49875044878193364, -0.14298519566678491], \"y\": [-0.1032154295293315, -0.054762350091481775, 0.5896586298883478, -0.1663959934735289, -0.7432746646633447, -0.3546995178707962, 0.4017775071927024, 0.9769155725982757, -0.9935877638397855, 0.9784790304472676, -0.5791081650976017, 0.9017731736266421, 0.866457977409469, -0.9890550501668381], \"marker\": {\"color\": \"rgb(200, 100, 200)\"}}], {\"showlegend\": false, \"hovermode\": \"closest\", \"height\": 800, \"width\": 800, \"title\": \"\\n<b>2-D Visualization of model trained on mammals subtree</b><br>\\n200 epochs, model is closer to convergence\"}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "model = PoincareModel.load(os.path.join(models_directory, 'gensim_mammals_epochs_200_dim_2'))\n",
-    "figure_title = \"\"\"\n",
-    "<b>2-D Visualization of model trained on mammals subtree</b><br>\n",
-    "200 epochs, model is closer to convergence\"\"\"\n",
-    "iplot(poincare_2d_visualization(model, tree, figure_title, show_node_labels=show_node_labels))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This is slightly different from the representation shown in the paper. Some key differences are - \n",
-    "1. Some of the nodes fairly high in the hierarchy (carnivore, canine, odd-toed ungulate) are much closer to the boundary than in the paper. As a result, it is likely that distances from them to certain positive nodes will be higher than they should be.\n",
-    "2. Some nodes very close to the boundary have an edge with a node which is also very close to the boundary, but on the other side of the disk. This is certainly an incorrect placement, as the distance between these two nodes is going be much higher.\n",
-    "\n",
-    "Note that the actual distance between two nodes is not the same as the \"naked-eye distance\" seen in the above representation. The actual distance is the Poincaré distance, whereas the \"naked-eye distance\" is the Euclidean distance.\n",
-    "\n",
-    "To get a better sense of Poincaré distance, a visualization is presented below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
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-   "metadata": {},
-   "source": [
-    "Some interesting things to note here are - \n",
-    "1. Distances increase much more quickly when the origin point is closer to the boundary - this can be seen visually from the fact that the darker region around the origin point grows smaller and smaller (though the changing scales make this slightly error-prone, the fact still holds).  \n",
-    "This is also borne out from the mathematical formula for Poincare distance.\n",
-    "\n",
-    "2. For points close to the boundary, distance to other points close to the boundary is relatively low to points with a low euclidean distance, but then increases significantly and saturates - this explains the positioning of children of parent node close to the boundary in a small arc around the parent node, and the presence of all negative nodes to the parent node (nodes without an edge to the parent node) outside of that small arc."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 6. Next steps"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "1. The model can be investigated further to understand why it doesn't produce results as good as the paper. It is possible that this might be due to training details not present in the paper, or due to us incorrectly interpreting some ambiguous parts of the paper. We have not been able to clarify all such ambiguitities in communication with the authors.\n",
-    "2. Optimizing the training process further - with a model size of 50 dimensions and a dataset with ~700k relations and ~80k nodes, the Gensim implementation takes around 70 seconds to complete an epoch (~10k relations per second), whereas the open source C++ implementation takes around 1/10th the time (~95k relations per second).\n",
-    "3. Implementing the variant of the model mentioned in the paper for symmetric graphs and evaluating on the scientific collaboration datasets described earlier in the report."
+    "1. [Original paper by Facebook AI Research](https://arxiv.org/pdf/1705.08039)\n",
+    "2. [Blog post describing technical challenges in implementation](https://rare-technologies.com/implementing-poincare-embeddings)\n",
+    "3. [Detailed evaluation notebook to reproduce results](https://github.com/RaRe-Technologies/gensim/blob/develop/docs/notebooks/Poincare%20Evaluation.ipynb)"
    ]
   }
  ],
@@ -107560,14 +689,14 @@
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.10"
+   "pygments_lexer": "ipython3",
+   "version": "3.5.4"
   }
  },
  "nbformat": 4,
diff --git a/docs/notebooks/doc2vec-IMDB.ipynb b/docs/notebooks/doc2vec-IMDB.ipynb
index f4a2ec2ae3..415f9ae837 100644
--- a/docs/notebooks/doc2vec-IMDB.ipynb
+++ b/docs/notebooks/doc2vec-IMDB.ipynb
@@ -905,7 +905,7 @@
     "# Download this file: https://github.com/nicholas-leonard/word2vec/blob/master/questions-words.txt\n",
     "# and place it in the local directory\n",
     "# Note: this takes many minutes\n",
-    "if os.path.isfile('question-words.txt'):\n",
+    "if os.path.isfile('questions-words.txt'):\n",
     "    for model in word_models:\n",
     "        sections = model.accuracy('questions-words.txt')\n",
     "        correct, incorrect = len(sections[-1]['correct']), len(sections[-1]['incorrect'])\n",
diff --git a/docs/notebooks/poincare/data/wordnet_mammal_hypernyms.tsv b/docs/notebooks/poincare/data/wordnet_mammal_hypernyms.tsv
new file mode 100644
index 0000000000..1cc2ffb55b
--- /dev/null
+++ b/docs/notebooks/poincare/data/wordnet_mammal_hypernyms.tsv
@@ -0,0 +1,7724 @@
+kangaroo.n.01	marsupial.n.01
+domestic_goat.n.01	even-toed_ungulate.n.01
+rock_squirrel.n.01	ground_squirrel.n.02
+vizsla.n.01	dog.n.01
+dandie_dinmont.n.01	mammal.n.01
+broodmare.n.01	horse.n.01
+spotted_skunk.n.01	spotted_skunk.n.01
+hispid_pocket_mouse.n.01	hispid_pocket_mouse.n.01
+lesser_kudu.n.01	placental.n.01
+water_shrew.n.01	insectivore.n.01
+silky_anteater.n.01	placental.n.01
+giant_kangaroo.n.01	metatherian.n.01
+bronco.n.01	bronco.n.01
+pekinese.n.01	pekinese.n.01
+seattle_slew.n.01	thoroughbred.n.02
+kinkajou.n.01	kinkajou.n.01
+boxer.n.04	mammal.n.01
+rabbit.n.01	placental.n.01
+longhorn.n.01	bovid.n.01
+blue_fox.n.01	fox.n.01
+woolly_monkey.n.01	new_world_monkey.n.01
+jungle_cat.n.01	jungle_cat.n.01
+vole.n.01	mammal.n.01
+western_big-eared_bat.n.01	long-eared_bat.n.01
+leopard.n.02	leopard.n.02
+hackney.n.02	hackney.n.02
+shetland_sheepdog.n.01	placental.n.01
+coati.n.01	carnivore.n.01
+wild_boar.n.01	mammal.n.01
+post_horse.n.01	placental.n.01
+porker.n.01	porker.n.01
+mouflon.n.01	mouflon.n.01
+australian_sea_lion.n.01	seal.n.09
+coondog.n.01	placental.n.01
+schipperke.n.01	mammal.n.01
+black_rat.n.01	rodent.n.01
+waterbuck.n.01	placental.n.01
+hack.n.06	odd-toed_ungulate.n.01
+central_chimpanzee.n.01	anthropoid_ape.n.01
+harrier.n.02	harrier.n.02
+lesser_panda.n.01	mammal.n.01
+wether.n.01	ruminant.n.01
+collie.n.01	shepherd_dog.n.01
+prancer.n.01	horse.n.01
+doberman.n.01	placental.n.01
+pygmy_marmoset.n.01	monkey.n.01
+phalanger.n.01	metatherian.n.01
+black-and-tan_coonhound.n.01	black-and-tan_coonhound.n.01
+woolly_monkey.n.01	primate.n.02
+ferret_badger.n.01	badger.n.02
+cave_myotis.n.01	placental.n.01
+desmodus_rotundus.n.01	vampire_bat.n.01
+malinois.n.01	shepherd_dog.n.01
+mexican_pocket_mouse.n.01	placental.n.01
+hackney.n.02	odd-toed_ungulate.n.01
+american_flying_squirrel.n.01	mammal.n.01
+syrian_bear.n.01	brown_bear.n.01
+dugong.n.01	sea_cow.n.01
+central_chimpanzee.n.01	mammal.n.01
+collared_pika.n.01	pika.n.01
+grey.n.07	odd-toed_ungulate.n.01
+domestic_llama.n.01	domestic_llama.n.01
+proboscidean.n.01	proboscidean.n.01
+gib.n.02	tom.n.02
+eurasian_otter.n.01	placental.n.01
+tree_squirrel.n.01	rodent.n.01
+flat-coated_retriever.n.01	flat-coated_retriever.n.01
+plantigrade_mammal.n.01	mammal.n.01
+cotswold.n.01	bovid.n.01
+welsh_pony.n.01	equine.n.01
+american_foxhound.n.01	carnivore.n.01
+addax.n.01	bovid.n.01
+aberdeen_angus.n.01	ruminant.n.01
+clydesdale.n.01	ungulate.n.01
+angora.n.02	goat.n.01
+taguan.n.01	taguan.n.01
+prototherian.n.01	mammal.n.01
+grade.n.09	cattle.n.01
+yearling.n.02	racehorse.n.01
+nyala.n.02	antelope.n.01
+springer.n.02	even-toed_ungulate.n.01
+seizure-alert_dog.n.01	seizure-alert_dog.n.01
+gib.n.02	cat.n.01
+longhorn.n.01	even-toed_ungulate.n.01
+striped_skunk.n.01	mammal.n.01
+coonhound.n.01	hunting_dog.n.01
+cur.n.01	canine.n.02
+exmoor.n.02	pony.n.05
+vicuna.n.03	vicuna.n.03
+white-tailed_jackrabbit.n.01	mammal.n.01
+kitty.n.04	feline.n.01
+pinscher.n.01	pinscher.n.01
+afghan_hound.n.01	afghan_hound.n.01
+fur_seal.n.01	fur_seal.n.01
+bull_mastiff.n.01	bull_mastiff.n.01
+hazel_mouse.n.01	dormouse.n.01
+ocelot.n.01	feline.n.01
+marco_polo_sheep.n.01	marco_polo_sheep.n.01
+baleen_whale.n.01	whale.n.02
+australian_sea_lion.n.01	australian_sea_lion.n.01
+porcupine.n.01	mammal.n.01
+whitetail_prairie_dog.n.01	whitetail_prairie_dog.n.01
+lesser_kudu.n.01	kudu.n.01
+rudapithecus.n.01	hominid.n.01
+boxer.n.04	carnivore.n.01
+blacktail_jackrabbit.n.01	lagomorph.n.01
+drill.n.02	baboon.n.01
+bluetick.n.01	dog.n.01
+vaquita.n.01	mammal.n.01
+tiger_cat.n.01	wildcat.n.03
+wheel_horse.n.01	equine.n.01
+flying_mouse.n.01	flying_mouse.n.01
+clydesdale.n.01	horse.n.01
+hound.n.01	dog.n.01
+bighorn.n.02	ruminant.n.01
+chinchilla.n.03	mammal.n.01
+raccoon_dog.n.01	placental.n.01
+palfrey.n.01	mammal.n.01
+rorqual.n.01	baleen_whale.n.01
+sassaby.n.01	even-toed_ungulate.n.01
+sloth_bear.n.01	carnivore.n.01
+rogue_elephant.n.01	proboscidean.n.01
+white_elephant.n.02	proboscidean.n.01
+virginia_deer.n.01	placental.n.01
+count_fleet.n.01	count_fleet.n.01
+lakeland_terrier.n.01	carnivore.n.01
+billy.n.02	ruminant.n.01
+omaha.n.04	equine.n.01
+soft-coated_wheaten_terrier.n.01	terrier.n.01
+bedlington_terrier.n.01	hunting_dog.n.01
+wisent.n.01	ungulate.n.01
+sable_antelope.n.01	even-toed_ungulate.n.01
+staffordshire_bullterrier.n.01	hunting_dog.n.01
+american_staffordshire_terrier.n.01	mammal.n.01
+belgian_hare.n.01	placental.n.01
+aquatic_mammal.n.01	mammal.n.01
+tibetan_terrier.n.01	mammal.n.01
+exmoor.n.01	even-toed_ungulate.n.01
+procyonid.n.01	placental.n.01
+canada_lynx.n.01	wildcat.n.03
+old_english_sheepdog.n.01	shepherd_dog.n.01
+raccoon.n.02	carnivore.n.01
+leafnose_bat.n.01	bat.n.01
+pygmy_sperm_whale.n.01	cetacean.n.01
+african_hunting_dog.n.01	carnivore.n.01
+spearnose_bat.n.01	placental.n.01
+pooch.n.01	pooch.n.01
+percheron.n.01	horse.n.01
+blenheim_spaniel.n.01	placental.n.01
+afghan_hound.n.01	mammal.n.01
+cur.n.01	cur.n.01
+persian_cat.n.01	mammal.n.01
+dun.n.01	horse.n.01
+nail-tailed_wallaby.n.01	mammal.n.01
+rock_squirrel.n.01	rock_squirrel.n.01
+tabby.n.01	tabby.n.01
+three-year-old_horse.n.01	horse.n.01
+broadtail.n.02	broadtail.n.02
+springbok.n.01	springbok.n.01
+sporting_dog.n.01	placental.n.01
+new_world_tapir.n.01	mammal.n.01
+muskrat.n.02	placental.n.01
+banteng.n.01	ungulate.n.01
+yak.n.02	even-toed_ungulate.n.01
+hog.n.03	hog.n.03
+basenji.n.01	basenji.n.01
+panther.n.02	leopard.n.02
+lesser_kudu.n.01	lesser_kudu.n.01
+gibbon.n.02	ape.n.01
+tamarisk_gerbil.n.01	mammal.n.01
+wheel_horse.n.01	ungulate.n.01
+beagle.n.01	carnivore.n.01
+small_civet.n.01	mammal.n.01
+hind.n.02	even-toed_ungulate.n.01
+blue_point_siamese.n.01	feline.n.01
+babirusa.n.01	swine.n.01
+wire-haired_fox_terrier.n.01	carnivore.n.01
+wood_rabbit.n.01	rabbit.n.01
+pacific_walrus.n.01	walrus.n.01
+long-tailed_porcupine.n.01	rodent.n.01
+hyrax.n.01	mammal.n.01
+airedale.n.01	carnivore.n.01
+silky_tamarin.n.01	monkey.n.01
+gomphothere.n.01	mammal.n.01
+nanny.n.02	mammal.n.01
+urial.n.01	bovid.n.01
+bullock.n.02	placental.n.01
+lapdog.n.01	placental.n.01
+german_short-haired_pointer.n.01	sporting_dog.n.01
+northern_flying_squirrel.n.01	northern_flying_squirrel.n.01
+eastern_chipmunk.n.01	placental.n.01
+warhorse.n.03	saddle_horse.n.01
+urial.n.01	ruminant.n.01
+domestic_llama.n.01	llama.n.01
+javanthropus.n.01	hominid.n.01
+northern_flying_squirrel.n.01	squirrel.n.01
+harness_horse.n.01	ungulate.n.01
+toy_manchester.n.01	mammal.n.01
+homo_habilis.n.01	hominid.n.01
+chimpanzee.n.01	mammal.n.01
+egyptian_cat.n.01	domestic_cat.n.01
+standard_poodle.n.01	carnivore.n.01
+sambar.n.01	ungulate.n.01
+silky_tamarin.n.01	mammal.n.01
+potoroo.n.01	marsupial.n.01
+chow.n.03	dog.n.01
+lincoln.n.03	ungulate.n.01
+black_squirrel.n.01	mammal.n.01
+hound.n.01	mammal.n.01
+common_raccoon.n.01	raccoon.n.02
+wild_sheep.n.01	even-toed_ungulate.n.01
+goat_antelope.n.01	bovid.n.01
+pygmy_chimpanzee.n.01	primate.n.02
+eared_seal.n.01	mammal.n.01
+lesser_rorqual.n.01	rorqual.n.01
+woodland_caribou.n.01	even-toed_ungulate.n.01
+common_shrew.n.01	common_shrew.n.01
+gemsbok.n.01	mammal.n.01
+tabby.n.02	domestic_cat.n.01
+marmoset.n.01	placental.n.01
+blenheim_spaniel.n.01	english_toy_spaniel.n.01
+fawn.n.02	mammal.n.01
+sassaby.n.01	bovid.n.01
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+sassaby.n.01	sassaby.n.01
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+beef.n.01	ungulate.n.01
+baboon.n.01	monkey.n.01
+cotswold.n.01	ruminant.n.01
+ox.n.02	ox.n.02
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+american_saddle_horse.n.01	american_saddle_horse.n.01
+bassarisk.n.01	carnivore.n.01
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+guereza.n.01	monkey.n.01
+collared_pika.n.01	placental.n.01
+vampire_bat.n.01	carnivorous_bat.n.01
+thomson's_gazelle.n.01	thomson's_gazelle.n.01
+stablemate.n.01	ungulate.n.01
+leveret.n.01	lagomorph.n.01
+eurasian_hamster.n.01	rodent.n.01
+steed.n.01	placental.n.01
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+harpy.n.03	fruit_bat.n.01
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+talapoin.n.01	mammal.n.01
+stallion.n.01	odd-toed_ungulate.n.01
+ermine.n.02	musteline_mammal.n.01
+leporid.n.01	leporid.n.01
+african_elephant.n.01	african_elephant.n.01
+wood_rat.n.01	rodent.n.01
+schipperke.n.01	placental.n.01
+megatherian.n.01	megatherian.n.01
+kangaroo_mouse.n.01	kangaroo_mouse.n.01
+dairy_cattle.n.01	ruminant.n.01
+boston_bull.n.01	carnivore.n.01
+harbor_porpoise.n.01	cetacean.n.01
+cat.n.01	placental.n.01
+pichiciago.n.01	armadillo.n.01
+domestic_ass.n.01	mammal.n.01
+homo_soloensis.n.01	placental.n.01
+harpy.n.03	placental.n.01
+kuvasz.n.01	watchdog.n.02
+gibbon.n.02	anthropoid_ape.n.01
+black-footed_ferret.n.01	placental.n.01
+german_shepherd.n.01	carnivore.n.01
+cavalry_horse.n.01	ungulate.n.01
+mylodontid.n.01	mylodontid.n.01
+nanny.n.02	placental.n.01
+wild_ass.n.01	wild_ass.n.01
+big_cat.n.01	mammal.n.01
+thomson's_gazelle.n.01	placental.n.01
+clumber.n.01	spaniel.n.01
+ant_bear.n.01	mammal.n.01
+tamarin.n.01	tamarin.n.01
+rat.n.01	rodent.n.01
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+charolais.n.01	cattle.n.01
+citation.n.06	thoroughbred.n.02
+liver-spotted_dalmatian.n.01	placental.n.01
+lapdog.n.01	lapdog.n.01
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+dun.n.01	odd-toed_ungulate.n.01
+flying_phalanger.n.01	phalanger.n.01
+atlantic_walrus.n.01	walrus.n.01
+gelding.n.01	mammal.n.01
+alaskan_brown_bear.n.01	placental.n.01
+jackass_bat.n.01	placental.n.01
+bull.n.01	bovid.n.01
+jaguar.n.01	mammal.n.01
+long-tailed_porcupine.n.01	mammal.n.01
+angora.n.02	ungulate.n.01
+desmodus_rotundus.n.01	desmodus_rotundus.n.01
+affirmed.n.01	equine.n.01
+false_vampire.n.01	false_vampire.n.01
+stud.n.04	male_horse.n.01
+seattle_slew.n.01	ungulate.n.01
+bird_dog.n.01	bird_dog.n.01
+cattalo.n.01	even-toed_ungulate.n.01
+two-year-old_horse.n.01	odd-toed_ungulate.n.01
+ewe.n.03	placental.n.01
+standard_schnauzer.n.01	mammal.n.01
+ox.n.02	even-toed_ungulate.n.01
+lemur.n.01	lemur.n.01
+zinjanthropus.n.01	placental.n.01
+genet.n.03	carnivore.n.01
+gordon_setter.n.01	canine.n.02
+groundhog.n.01	rodent.n.01
+doberman.n.01	carnivore.n.01
+earless_seal.n.01	seal.n.09
+killer_whale.n.01	toothed_whale.n.01
+jerboa_rat.n.01	rat.n.01
+beagle.n.01	mammal.n.01
+plains_pocket_mouse.n.01	mammal.n.01
+curly-coated_retriever.n.01	retriever.n.01
+broadtail.n.02	bovid.n.01
+flying_phalanger.n.01	metatherian.n.01
+fanaloka.n.01	viverrine.n.01
+muishond.n.01	muishond.n.01
+corgi.n.01	corgi.n.01
+grevy's_zebra.n.01	zebra.n.01
+homo_sapiens_sapiens.n.01	homo_sapiens.n.01
+english_setter.n.01	sporting_dog.n.01
+wombat.n.01	mammal.n.01
+whirlaway.n.01	placental.n.01
+heifer.n.01	mammal.n.01
+bedlington_terrier.n.01	bedlington_terrier.n.01
+uakari.n.01	new_world_monkey.n.01
+hognose_bat.n.01	carnivorous_bat.n.01
+northern_pocket_gopher.n.01	gopher.n.04
+crabeater_seal.n.01	placental.n.01
+northern_flying_squirrel.n.01	rodent.n.01
+kiang.n.01	odd-toed_ungulate.n.01
+sennenhunde.n.01	placental.n.01
+pony.n.02	pony.n.02
+chimpanzee.n.01	placental.n.01
+tatouay.n.01	placental.n.01
+white-footed_mouse.n.01	rodent.n.01
+false_vampire.n.01	mammal.n.01
+gnu.n.01	gnu.n.01
+tiglon.n.01	feline.n.01
+golden_hamster.n.01	rodent.n.01
+appaloosa.n.01	placental.n.01
+guereza.n.01	guereza.n.01
+pricket.n.02	pricket.n.02
+neandertal_man.n.01	mammal.n.01
+babirusa.n.01	mammal.n.01
+angora.n.03	rabbit.n.01
+cave_myotis.n.01	bat.n.01
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+elephant_seal.n.01	elephant_seal.n.01
+macrotus.n.01	bat.n.01
+dog.n.01	dog.n.01
+leopard_cat.n.01	leopard_cat.n.01
+house_mouse.n.01	mouse.n.01
+greyhound.n.01	canine.n.02
+white_wolf.n.01	placental.n.01
+whale.n.02	placental.n.01
+hartebeest.n.01	hartebeest.n.01
+southern_bog_lemming.n.01	rodent.n.01
+springer.n.02	placental.n.01
+sperm_whale.n.01	aquatic_mammal.n.01
+longhorn.n.01	bovine.n.01
+aardvark.n.01	mammal.n.01
+german_short-haired_pointer.n.01	placental.n.01
+plow_horse.n.01	placental.n.01
+grampus.n.02	grampus.n.02
+dingo.n.01	wild_dog.n.01
+arabian.n.02	equine.n.01
+shih-tzu.n.01	mammal.n.01
+western_grey_squirrel.n.01	placental.n.01
+african_wild_ass.n.01	ungulate.n.01
+tiger.n.02	tiger.n.02
+titi.n.03	titi.n.03
+wallaby.n.01	kangaroo.n.01
+gazelle.n.01	mammal.n.01
+cynopterus_sphinx.n.01	bat.n.01
+africander.n.01	mammal.n.01
+ice_bear.n.01	bear.n.01
+cro-magnon.n.01	hominid.n.01
+valley_pocket_gopher.n.01	valley_pocket_gopher.n.01
+eastern_lowland_gorilla.n.01	ape.n.01
+cob.n.02	mammal.n.01
+bezoar_goat.n.01	mammal.n.01
+sporting_dog.n.01	mammal.n.01
+cynocephalus_variegatus.n.01	cynocephalus_variegatus.n.01
+red_fox.n.02	red_fox.n.02
+burmese_cat.n.01	burmese_cat.n.01
+silky_tamarin.n.01	tamarin.n.01
+argali.n.01	bovid.n.01
+crab-eating_raccoon.n.01	mammal.n.01
+tarsier.n.01	placental.n.01
+spearnose_bat.n.01	mammal.n.01
+shetland_sheepdog.n.01	dog.n.01
+american_marten.n.01	marten.n.01
+stablemate.n.01	placental.n.01
+wild_horse.n.01	odd-toed_ungulate.n.01
+eohippus.n.01	ungulate.n.01
+pallid_bat.n.01	placental.n.01
+swamp_rabbit.n.02	mammal.n.01
+irish_wolfhound.n.01	hunting_dog.n.01
+american_bison.n.01	american_bison.n.01
+smooth-haired_fox_terrier.n.01	hunting_dog.n.01
+jersey.n.05	placental.n.01
+mastiff_bat.n.01	placental.n.01
+hare.n.01	placental.n.01
+wether.n.01	wether.n.01
+mouse.n.01	mouse.n.01
+percheron.n.01	mammal.n.01
+irish_water_spaniel.n.01	mammal.n.01
+glutton.n.02	carnivore.n.01
+buckskin.n.01	equine.n.01
+homo_sapiens_sapiens.n.01	homo.n.02
+airedale.n.01	placental.n.01
+asiatic_flying_squirrel.n.01	squirrel.n.01
+feline.n.01	feline.n.01
+mantled_ground_squirrel.n.01	placental.n.01
+elephant.n.01	placental.n.01
+hackney.n.02	horse.n.01
+frosted_bat.n.01	vespertilian_bat.n.01
+santa_gertrudis.n.01	cattle.n.01
+irish_wolfhound.n.01	carnivore.n.01
+pekinese.n.01	placental.n.01
+chesapeake_bay_retriever.n.01	mammal.n.01
+crab-eating_raccoon.n.01	raccoon.n.02
+white_elephant.n.02	pachyderm.n.01
+staghound.n.01	canine.n.02
+bloodhound.n.01	canine.n.02
+central_chimpanzee.n.01	great_ape.n.01
+gazella_subgutturosa.n.01	ruminant.n.01
+dun.n.01	saddle_horse.n.01
+jackrabbit.n.01	hare.n.01
+chevrotain.n.01	ungulate.n.01
+aberdeen_angus.n.01	beef.n.01
+mink.n.03	mink.n.03
+american_foxhound.n.01	mammal.n.01
+bullock.n.02	even-toed_ungulate.n.01
+saiga.n.01	antelope.n.01
+fox_squirrel.n.01	squirrel.n.01
+pinto.n.01	mammal.n.01
+little_chief_hare.n.01	pika.n.01
+miniature_pinscher.n.01	miniature_pinscher.n.01
+round-tailed_muskrat.n.01	round-tailed_muskrat.n.01
+crowbait.n.01	mammal.n.01
+damaraland_mole_rat.n.01	mammal.n.01
+kangaroo_mouse.n.02	jerboa_rat.n.01
+false_saber-toothed_tiger.n.01	mammal.n.01
+black_fox.n.01	fox.n.01
+green_monkey.n.01	old_world_monkey.n.01
+aberdeen_angus.n.01	bovine.n.01
+sled_dog.n.01	mammal.n.01
+little_chief_hare.n.01	placental.n.01
+springer_spaniel.n.01	sporting_dog.n.01
+hereford.n.01	mammal.n.01
+zebu.n.01	zebu.n.01
+schnauzer.n.01	dog.n.01
+samoyed.n.03	carnivore.n.01
+flickertail.n.01	squirrel.n.01
+cynocephalus_variegatus.n.01	mammal.n.01
+harpy.n.03	bat.n.01
+brush-tailed_phalanger.n.01	phalanger.n.01
+coach_horse.n.01	horse.n.01
+liger.n.01	feline.n.01
+margay.n.01	placental.n.01
+mountain_gorilla.n.01	anthropoid_ape.n.01
+longhorn.n.01	beef.n.01
+red_fox.n.02	mammal.n.01
+indian_rhinoceros.n.01	odd-toed_ungulate.n.01
+llama.n.01	even-toed_ungulate.n.01
+asiatic_flying_squirrel.n.01	rodent.n.01
+collie.n.01	working_dog.n.01
+kerry_blue_terrier.n.01	hunting_dog.n.01
+saddle_horse.n.01	saddle_horse.n.01
+new_world_beaver.n.01	beaver.n.07
+mountain_chinchilla.n.01	placental.n.01
+english_foxhound.n.01	english_foxhound.n.01
+leveret.n.01	leporid.n.01
+shetland_sheepdog.n.01	canine.n.02
+beagle.n.01	beagle.n.01
+tibetan_mastiff.n.01	tibetan_mastiff.n.01
+bouvier_des_flandres.n.01	canine.n.02
+wheel_horse.n.01	placental.n.01
+pocket_rat.n.01	rat.n.01
+malinois.n.01	working_dog.n.01
+white_elephant.n.02	white_elephant.n.02
+camel.n.01	camel.n.01
+mexican_pocket_mouse.n.01	rat.n.01
+vaquita.n.01	toothed_whale.n.01
+manchester_terrier.n.01	hunting_dog.n.01
+chacma.n.01	monkey.n.01
+binturong.n.01	viverrine.n.01
+mastiff_bat.n.01	mammal.n.01
+goat.n.01	mammal.n.01
+pembroke.n.01	canine.n.02
+steenbok.n.01	ungulate.n.01
+tarsius_syrichta.n.01	mammal.n.01
+maltese.n.03	domestic_cat.n.01
+pacific_bottlenose_dolphin.n.01	toothed_whale.n.01
+tamandua.n.01	tamandua.n.01
+murine.n.01	rodent.n.01
+coyote.n.01	canine.n.02
+king_charles_spaniel.n.01	placental.n.01
+basset.n.01	canine.n.02
+pygmy_mouse.n.01	pygmy_mouse.n.01
+toy_spaniel.n.01	carnivore.n.01
+cactus_mouse.n.01	mouse.n.01
+hart.n.03	ruminant.n.01
+broodmare.n.01	equine.n.01
+sussex_spaniel.n.01	sporting_dog.n.01
+omaha.n.04	odd-toed_ungulate.n.01
+alaska_fur_seal.n.01	placental.n.01
+cattalo.n.01	bovine.n.01
+soft-coated_wheaten_terrier.n.01	mammal.n.01
+harness_horse.n.01	horse.n.01
+banteng.n.01	even-toed_ungulate.n.01
diff --git a/docs/notebooks/word2vec.ipynb b/docs/notebooks/word2vec.ipynb
index 6e7845e1f8..a2e944420f 100644
--- a/docs/notebooks/word2vec.ipynb
+++ b/docs/notebooks/word2vec.ipynb
@@ -29,18 +29,11 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# import modules & set up logging\n",
     "import gensim, logging\n",
+    "\n",
     "logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)"
    ]
   },
@@ -53,23 +46,23 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2017-06-29 10:36:15,148 : INFO : collecting all words and their counts\n",
-      "2017-06-29 10:36:15,149 : INFO : PROGRESS: at sentence #0, processed 0 words, keeping 0 word types\n",
-      "2017-06-29 10:36:15,150 : INFO : collected 3 word types from a corpus of 4 raw words and 2 sentences\n",
-      "2017-06-29 10:36:15,152 : INFO : Loading a fresh vocabulary\n",
-      "2017-06-29 10:36:15,158 : INFO : min_count=1 retains 3 unique words (100% of original 3, drops 0)\n",
-      "2017-06-29 10:36:15,159 : INFO : min_count=1 leaves 4 word corpus (100% of original 4, drops 0)\n",
-      "2017-06-29 10:36:15,160 : INFO : deleting the raw counts dictionary of 3 items\n",
-      "2017-06-29 10:36:15,167 : INFO : sample=0.001 downsamples 3 most-common words\n",
-      "2017-06-29 10:36:15,168 : INFO : downsampling leaves estimated 0 word corpus (5.7% of prior 4)\n",
-      "2017-06-29 10:36:15,169 : INFO : estimated required memory for 3 words and 100 dimensions: 3900 bytes\n",
-      "2017-06-29 10:36:15,170 : INFO : resetting layer weights\n",
-      "2017-06-29 10:36:15,172 : INFO : training model with 3 workers on 3 vocabulary and 100 features, using sg=0 hs=0 sample=0.001 negative=5 window=5\n",
-      "2017-06-29 10:36:15,176 : INFO : worker thread finished; awaiting finish of 2 more threads\n",
-      "2017-06-29 10:36:15,179 : INFO : worker thread finished; awaiting finish of 1 more threads\n",
-      "2017-06-29 10:36:15,183 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
-      "2017-06-29 10:36:15,184 : INFO : training on 20 raw words (0 effective words) took 0.0s, 0 effective words/s\n",
-      "2017-06-29 10:36:15,185 : WARNING : under 10 jobs per worker: consider setting a smaller `batch_words' for smoother alpha decay\n"
+      "2017-12-21 16:25:19,117 : INFO : collecting all words and their counts\n",
+      "2017-12-21 16:25:19,119 : INFO : PROGRESS: at sentence #0, processed 0 words, keeping 0 word types\n",
+      "2017-12-21 16:25:19,120 : INFO : collected 3 word types from a corpus of 4 raw words and 2 sentences\n",
+      "2017-12-21 16:25:19,120 : INFO : Loading a fresh vocabulary\n",
+      "2017-12-21 16:25:19,121 : INFO : min_count=1 retains 3 unique words (100% of original 3, drops 0)\n",
+      "2017-12-21 16:25:19,122 : INFO : min_count=1 leaves 4 word corpus (100% of original 4, drops 0)\n",
+      "2017-12-21 16:25:19,123 : INFO : deleting the raw counts dictionary of 3 items\n",
+      "2017-12-21 16:25:19,124 : INFO : sample=0.001 downsamples 3 most-common words\n",
+      "2017-12-21 16:25:19,124 : INFO : downsampling leaves estimated 0 word corpus (5.7% of prior 4)\n",
+      "2017-12-21 16:25:19,125 : INFO : estimated required memory for 3 words and 100 dimensions: 3900 bytes\n",
+      "2017-12-21 16:25:19,126 : INFO : resetting layer weights\n",
+      "2017-12-21 16:25:19,127 : INFO : training model with 3 workers on 3 vocabulary and 100 features, using sg=0 hs=0 sample=0.001 negative=5 window=5\n",
+      "2017-12-21 16:25:19,130 : INFO : worker thread finished; awaiting finish of 2 more threads\n",
+      "2017-12-21 16:25:19,130 : INFO : worker thread finished; awaiting finish of 1 more threads\n",
+      "2017-12-21 16:25:19,131 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
+      "2017-12-21 16:25:19,131 : INFO : training on 20 raw words (0 effective words) took 0.0s, 0 effective words/s\n",
+      "2017-12-21 16:25:19,132 : WARNING : under 10 jobs per worker: consider setting a smaller `batch_words' for smoother alpha decay\n"
      ]
     }
    ],
@@ -93,9 +86,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# create some toy data to use with the following example\n",
@@ -115,9 +106,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "class MySentences(object):\n",
@@ -139,7 +128,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[['first'], ['sentence'], ['second'], ['sentence']]\n"
+      "[['second'], ['sentence'], ['first'], ['sentence']]\n"
      ]
     }
    ],
@@ -157,23 +146,23 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2017-06-29 10:36:18,159 : INFO : collecting all words and their counts\n",
-      "2017-06-29 10:36:18,161 : INFO : PROGRESS: at sentence #0, processed 0 words, keeping 0 word types\n",
-      "2017-06-29 10:36:18,162 : INFO : collected 3 word types from a corpus of 4 raw words and 4 sentences\n",
-      "2017-06-29 10:36:18,163 : INFO : Loading a fresh vocabulary\n",
-      "2017-06-29 10:36:18,164 : INFO : min_count=1 retains 3 unique words (100% of original 3, drops 0)\n",
-      "2017-06-29 10:36:18,165 : INFO : min_count=1 leaves 4 word corpus (100% of original 4, drops 0)\n",
-      "2017-06-29 10:36:18,166 : INFO : deleting the raw counts dictionary of 3 items\n",
-      "2017-06-29 10:36:18,167 : INFO : sample=0.001 downsamples 3 most-common words\n",
-      "2017-06-29 10:36:18,168 : INFO : downsampling leaves estimated 0 word corpus (5.7% of prior 4)\n",
-      "2017-06-29 10:36:18,169 : INFO : estimated required memory for 3 words and 100 dimensions: 3900 bytes\n",
-      "2017-06-29 10:36:18,170 : INFO : resetting layer weights\n",
-      "2017-06-29 10:36:18,171 : INFO : training model with 3 workers on 3 vocabulary and 100 features, using sg=0 hs=0 sample=0.001 negative=5 window=5\n",
-      "2017-06-29 10:36:18,183 : INFO : worker thread finished; awaiting finish of 2 more threads\n",
-      "2017-06-29 10:36:18,184 : INFO : worker thread finished; awaiting finish of 1 more threads\n",
-      "2017-06-29 10:36:18,185 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
-      "2017-06-29 10:36:18,186 : INFO : training on 20 raw words (0 effective words) took 0.0s, 0 effective words/s\n",
-      "2017-06-29 10:36:18,186 : WARNING : under 10 jobs per worker: consider setting a smaller `batch_words' for smoother alpha decay\n"
+      "2017-12-21 16:25:19,155 : INFO : collecting all words and their counts\n",
+      "2017-12-21 16:25:19,156 : INFO : PROGRESS: at sentence #0, processed 0 words, keeping 0 word types\n",
+      "2017-12-21 16:25:19,156 : INFO : collected 3 word types from a corpus of 4 raw words and 4 sentences\n",
+      "2017-12-21 16:25:19,157 : INFO : Loading a fresh vocabulary\n",
+      "2017-12-21 16:25:19,158 : INFO : min_count=1 retains 3 unique words (100% of original 3, drops 0)\n",
+      "2017-12-21 16:25:19,159 : INFO : min_count=1 leaves 4 word corpus (100% of original 4, drops 0)\n",
+      "2017-12-21 16:25:19,161 : INFO : deleting the raw counts dictionary of 3 items\n",
+      "2017-12-21 16:25:19,162 : INFO : sample=0.001 downsamples 3 most-common words\n",
+      "2017-12-21 16:25:19,164 : INFO : downsampling leaves estimated 0 word corpus (5.7% of prior 4)\n",
+      "2017-12-21 16:25:19,165 : INFO : estimated required memory for 3 words and 100 dimensions: 3900 bytes\n",
+      "2017-12-21 16:25:19,167 : INFO : resetting layer weights\n",
+      "2017-12-21 16:25:19,168 : INFO : training model with 3 workers on 3 vocabulary and 100 features, using sg=0 hs=0 sample=0.001 negative=5 window=5\n",
+      "2017-12-21 16:25:19,172 : INFO : worker thread finished; awaiting finish of 2 more threads\n",
+      "2017-12-21 16:25:19,173 : INFO : worker thread finished; awaiting finish of 1 more threads\n",
+      "2017-12-21 16:25:19,173 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
+      "2017-12-21 16:25:19,174 : INFO : training on 20 raw words (0 effective words) took 0.0s, 0 effective words/s\n",
+      "2017-12-21 16:25:19,174 : WARNING : under 10 jobs per worker: consider setting a smaller `batch_words' for smoother alpha decay\n"
      ]
     }
    ],
@@ -192,7 +181,7 @@
      "output_type": "stream",
      "text": [
       "Word2Vec(vocab=3, size=100, alpha=0.025)\n",
-      "{'second': <gensim.models.keyedvectors.Vocab object at 0x7f1c573ce9d0>, 'first': <gensim.models.keyedvectors.Vocab object at 0x7f1c0d056b50>, 'sentence': <gensim.models.keyedvectors.Vocab object at 0x7f1c0d0569d0>}\n"
+      "{'second': <gensim.models.keyedvectors.Vocab object at 0x7f20aa771a90>, 'sentence': <gensim.models.keyedvectors.Vocab object at 0x7f20852be3d0>, 'first': <gensim.models.keyedvectors.Vocab object at 0x7f20ba0a2d10>}\n"
      ]
     }
    ],
@@ -223,23 +212,23 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2017-06-29 10:36:19,845 : INFO : collecting all words and their counts\n",
-      "2017-06-29 10:36:19,847 : INFO : PROGRESS: at sentence #0, processed 0 words, keeping 0 word types\n",
-      "2017-06-29 10:36:19,848 : INFO : collected 3 word types from a corpus of 4 raw words and 4 sentences\n",
-      "2017-06-29 10:36:19,849 : INFO : Loading a fresh vocabulary\n",
-      "2017-06-29 10:36:19,849 : INFO : min_count=1 retains 3 unique words (100% of original 3, drops 0)\n",
-      "2017-06-29 10:36:19,850 : INFO : min_count=1 leaves 4 word corpus (100% of original 4, drops 0)\n",
-      "2017-06-29 10:36:19,851 : INFO : deleting the raw counts dictionary of 3 items\n",
-      "2017-06-29 10:36:19,851 : INFO : sample=0.001 downsamples 3 most-common words\n",
-      "2017-06-29 10:36:19,852 : INFO : downsampling leaves estimated 0 word corpus (5.7% of prior 4)\n",
-      "2017-06-29 10:36:19,853 : INFO : estimated required memory for 3 words and 100 dimensions: 3900 bytes\n",
-      "2017-06-29 10:36:19,853 : INFO : resetting layer weights\n",
-      "2017-06-29 10:36:19,854 : INFO : training model with 3 workers on 3 vocabulary and 100 features, using sg=0 hs=0 sample=0.001 negative=5 window=5\n",
-      "2017-06-29 10:36:19,856 : INFO : worker thread finished; awaiting finish of 2 more threads\n",
-      "2017-06-29 10:36:19,857 : INFO : worker thread finished; awaiting finish of 1 more threads\n",
-      "2017-06-29 10:36:19,858 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
-      "2017-06-29 10:36:19,858 : INFO : training on 20 raw words (0 effective words) took 0.0s, 0 effective words/s\n",
-      "2017-06-29 10:36:19,859 : WARNING : under 10 jobs per worker: consider setting a smaller `batch_words' for smoother alpha decay\n"
+      "2017-12-21 16:25:19,190 : INFO : collecting all words and their counts\n",
+      "2017-12-21 16:25:19,191 : INFO : PROGRESS: at sentence #0, processed 0 words, keeping 0 word types\n",
+      "2017-12-21 16:25:19,192 : INFO : collected 3 word types from a corpus of 4 raw words and 4 sentences\n",
+      "2017-12-21 16:25:19,192 : INFO : Loading a fresh vocabulary\n",
+      "2017-12-21 16:25:19,193 : INFO : min_count=1 retains 3 unique words (100% of original 3, drops 0)\n",
+      "2017-12-21 16:25:19,194 : INFO : min_count=1 leaves 4 word corpus (100% of original 4, drops 0)\n",
+      "2017-12-21 16:25:19,194 : INFO : deleting the raw counts dictionary of 3 items\n",
+      "2017-12-21 16:25:19,195 : INFO : sample=0.001 downsamples 3 most-common words\n",
+      "2017-12-21 16:25:19,195 : INFO : downsampling leaves estimated 0 word corpus (5.7% of prior 4)\n",
+      "2017-12-21 16:25:19,196 : INFO : estimated required memory for 3 words and 100 dimensions: 3900 bytes\n",
+      "2017-12-21 16:25:19,197 : INFO : resetting layer weights\n",
+      "2017-12-21 16:25:19,197 : INFO : training model with 3 workers on 3 vocabulary and 100 features, using sg=0 hs=0 sample=0.001 negative=5 window=5\n",
+      "2017-12-21 16:25:19,201 : INFO : worker thread finished; awaiting finish of 2 more threads\n",
+      "2017-12-21 16:25:19,202 : INFO : worker thread finished; awaiting finish of 1 more threads\n",
+      "2017-12-21 16:25:19,202 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
+      "2017-12-21 16:25:19,203 : INFO : training on 20 raw words (0 effective words) took 0.0s, 0 effective words/s\n",
+      "2017-12-21 16:25:19,203 : WARNING : under 10 jobs per worker: consider setting a smaller `batch_words' for smoother alpha decay\n"
      ]
     },
     {
@@ -271,7 +260,7 @@
      "output_type": "stream",
      "text": [
       "Word2Vec(vocab=3, size=100, alpha=0.025)\n",
-      "{'second': <gensim.models.keyedvectors.Vocab object at 0x7f1c573ce9d0>, 'first': <gensim.models.keyedvectors.Vocab object at 0x7f1c0d056b50>, 'sentence': <gensim.models.keyedvectors.Vocab object at 0x7f1c0d0569d0>}\n"
+      "{'second': <gensim.models.keyedvectors.Vocab object at 0x7f20aa771a90>, 'sentence': <gensim.models.keyedvectors.Vocab object at 0x7f20852be3d0>, 'first': <gensim.models.keyedvectors.Vocab object at 0x7f20ba0a2d10>}\n"
      ]
     }
    ],
@@ -291,9 +280,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Set file names for train and test data\n",
@@ -310,7 +297,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "<__main__.MyText object at 0x7f1c0cfe6a10>\n"
+      "<__main__.MyText object at 0x7f20852714d0>\n"
      ]
     }
    ],
@@ -346,22 +333,22 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2017-06-29 10:36:22,567 : INFO : collecting all words and their counts\n",
-      "2017-06-29 10:36:22,568 : INFO : PROGRESS: at sentence #0, processed 0 words, keeping 0 word types\n",
-      "2017-06-29 10:36:22,599 : INFO : collected 10186 word types from a corpus of 59890 raw words and 300 sentences\n",
-      "2017-06-29 10:36:22,601 : INFO : Loading a fresh vocabulary\n",
-      "2017-06-29 10:36:22,613 : INFO : min_count=10 retains 806 unique words (7% of original 10186, drops 9380)\n",
-      "2017-06-29 10:36:22,614 : INFO : min_count=10 leaves 40964 word corpus (68% of original 59890, drops 18926)\n",
-      "2017-06-29 10:36:22,621 : INFO : deleting the raw counts dictionary of 10186 items\n",
-      "2017-06-29 10:36:22,623 : INFO : sample=0.001 downsamples 54 most-common words\n",
-      "2017-06-29 10:36:22,625 : INFO : downsampling leaves estimated 26224 word corpus (64.0% of prior 40964)\n",
-      "2017-06-29 10:36:22,629 : INFO : estimated required memory for 806 words and 100 dimensions: 1047800 bytes\n",
-      "2017-06-29 10:36:22,634 : INFO : resetting layer weights\n",
-      "2017-06-29 10:36:22,651 : INFO : training model with 3 workers on 806 vocabulary and 100 features, using sg=0 hs=0 sample=0.001 negative=5 window=5\n",
-      "2017-06-29 10:36:22,791 : INFO : worker thread finished; awaiting finish of 2 more threads\n",
-      "2017-06-29 10:36:22,792 : INFO : worker thread finished; awaiting finish of 1 more threads\n",
-      "2017-06-29 10:36:22,793 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
-      "2017-06-29 10:36:22,795 : INFO : training on 299450 raw words (130961 effective words) took 0.1s, 928696 effective words/s\n"
+      "2017-12-21 16:25:19,238 : INFO : collecting all words and their counts\n",
+      "2017-12-21 16:25:19,239 : INFO : PROGRESS: at sentence #0, processed 0 words, keeping 0 word types\n",
+      "2017-12-21 16:25:19,260 : INFO : collected 10186 word types from a corpus of 59890 raw words and 300 sentences\n",
+      "2017-12-21 16:25:19,262 : INFO : Loading a fresh vocabulary\n",
+      "2017-12-21 16:25:19,269 : INFO : min_count=10 retains 806 unique words (7% of original 10186, drops 9380)\n",
+      "2017-12-21 16:25:19,270 : INFO : min_count=10 leaves 40964 word corpus (68% of original 59890, drops 18926)\n",
+      "2017-12-21 16:25:19,274 : INFO : deleting the raw counts dictionary of 10186 items\n",
+      "2017-12-21 16:25:19,276 : INFO : sample=0.001 downsamples 54 most-common words\n",
+      "2017-12-21 16:25:19,277 : INFO : downsampling leaves estimated 26224 word corpus (64.0% of prior 40964)\n",
+      "2017-12-21 16:25:19,278 : INFO : estimated required memory for 806 words and 100 dimensions: 1047800 bytes\n",
+      "2017-12-21 16:25:19,284 : INFO : resetting layer weights\n",
+      "2017-12-21 16:25:19,295 : INFO : training model with 3 workers on 806 vocabulary and 100 features, using sg=0 hs=0 sample=0.001 negative=5 window=5\n",
+      "2017-12-21 16:25:19,425 : INFO : worker thread finished; awaiting finish of 2 more threads\n",
+      "2017-12-21 16:25:19,427 : INFO : worker thread finished; awaiting finish of 1 more threads\n",
+      "2017-12-21 16:25:19,429 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
+      "2017-12-21 16:25:19,430 : INFO : training on 299450 raw words (130961 effective words) took 0.1s, 984255 effective words/s\n"
      ]
     }
    ],
@@ -389,22 +376,22 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2017-06-29 10:36:25,482 : INFO : collecting all words and their counts\n",
-      "2017-06-29 10:36:25,484 : INFO : PROGRESS: at sentence #0, processed 0 words, keeping 0 word types\n",
-      "2017-06-29 10:36:25,505 : INFO : collected 10186 word types from a corpus of 59890 raw words and 300 sentences\n",
-      "2017-06-29 10:36:25,506 : INFO : Loading a fresh vocabulary\n",
-      "2017-06-29 10:36:25,517 : INFO : min_count=5 retains 1723 unique words (16% of original 10186, drops 8463)\n",
-      "2017-06-29 10:36:25,519 : INFO : min_count=5 leaves 46858 word corpus (78% of original 59890, drops 13032)\n",
-      "2017-06-29 10:36:25,531 : INFO : deleting the raw counts dictionary of 10186 items\n",
-      "2017-06-29 10:36:25,533 : INFO : sample=0.001 downsamples 49 most-common words\n",
-      "2017-06-29 10:36:25,534 : INFO : downsampling leaves estimated 32849 word corpus (70.1% of prior 46858)\n",
-      "2017-06-29 10:36:25,535 : INFO : estimated required memory for 1723 words and 200 dimensions: 3618300 bytes\n",
-      "2017-06-29 10:36:25,542 : INFO : resetting layer weights\n",
-      "2017-06-29 10:36:25,587 : INFO : training model with 3 workers on 1723 vocabulary and 200 features, using sg=0 hs=0 sample=0.001 negative=5 window=5\n",
-      "2017-06-29 10:36:25,797 : INFO : worker thread finished; awaiting finish of 2 more threads\n",
-      "2017-06-29 10:36:25,802 : INFO : worker thread finished; awaiting finish of 1 more threads\n",
-      "2017-06-29 10:36:25,809 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
-      "2017-06-29 10:36:25,813 : INFO : training on 299450 raw words (164316 effective words) took 0.2s, 748937 effective words/s\n"
+      "2017-12-21 16:25:19,438 : INFO : collecting all words and their counts\n",
+      "2017-12-21 16:25:19,439 : INFO : PROGRESS: at sentence #0, processed 0 words, keeping 0 word types\n",
+      "2017-12-21 16:25:19,455 : INFO : collected 10186 word types from a corpus of 59890 raw words and 300 sentences\n",
+      "2017-12-21 16:25:19,456 : INFO : Loading a fresh vocabulary\n",
+      "2017-12-21 16:25:19,464 : INFO : min_count=5 retains 1723 unique words (16% of original 10186, drops 8463)\n",
+      "2017-12-21 16:25:19,465 : INFO : min_count=5 leaves 46858 word corpus (78% of original 59890, drops 13032)\n",
+      "2017-12-21 16:25:19,473 : INFO : deleting the raw counts dictionary of 10186 items\n",
+      "2017-12-21 16:25:19,474 : INFO : sample=0.001 downsamples 49 most-common words\n",
+      "2017-12-21 16:25:19,475 : INFO : downsampling leaves estimated 32849 word corpus (70.1% of prior 46858)\n",
+      "2017-12-21 16:25:19,476 : INFO : estimated required memory for 1723 words and 200 dimensions: 3618300 bytes\n",
+      "2017-12-21 16:25:19,482 : INFO : resetting layer weights\n",
+      "2017-12-21 16:25:19,504 : INFO : training model with 3 workers on 1723 vocabulary and 200 features, using sg=0 hs=0 sample=0.001 negative=5 window=5\n",
+      "2017-12-21 16:25:19,699 : INFO : worker thread finished; awaiting finish of 2 more threads\n",
+      "2017-12-21 16:25:19,707 : INFO : worker thread finished; awaiting finish of 1 more threads\n",
+      "2017-12-21 16:25:19,709 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
+      "2017-12-21 16:25:19,710 : INFO : training on 299450 raw words (164316 effective words) took 0.2s, 807544 effective words/s\n"
      ]
     }
    ],
@@ -430,24 +417,24 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2017-06-29 10:36:27,068 : INFO : collecting all words and their counts\n",
-      "2017-06-29 10:36:27,069 : INFO : PROGRESS: at sentence #0, processed 0 words, keeping 0 word types\n",
-      "2017-06-29 10:36:27,090 : INFO : collected 10186 word types from a corpus of 59890 raw words and 300 sentences\n",
-      "2017-06-29 10:36:27,091 : INFO : Loading a fresh vocabulary\n",
-      "2017-06-29 10:36:27,109 : INFO : min_count=5 retains 1723 unique words (16% of original 10186, drops 8463)\n",
-      "2017-06-29 10:36:27,110 : INFO : min_count=5 leaves 46858 word corpus (78% of original 59890, drops 13032)\n",
-      "2017-06-29 10:36:27,118 : INFO : deleting the raw counts dictionary of 10186 items\n",
-      "2017-06-29 10:36:27,122 : INFO : sample=0.001 downsamples 49 most-common words\n",
-      "2017-06-29 10:36:27,123 : INFO : downsampling leaves estimated 32849 word corpus (70.1% of prior 46858)\n",
-      "2017-06-29 10:36:27,125 : INFO : estimated required memory for 1723 words and 100 dimensions: 2239900 bytes\n",
-      "2017-06-29 10:36:27,130 : INFO : resetting layer weights\n",
-      "2017-06-29 10:36:27,162 : INFO : training model with 4 workers on 1723 vocabulary and 100 features, using sg=0 hs=0 sample=0.001 negative=5 window=5\n",
-      "2017-06-29 10:36:27,323 : INFO : worker thread finished; awaiting finish of 3 more threads\n",
-      "2017-06-29 10:36:27,325 : INFO : worker thread finished; awaiting finish of 2 more threads\n",
-      "2017-06-29 10:36:27,330 : INFO : worker thread finished; awaiting finish of 1 more threads\n",
-      "2017-06-29 10:36:27,331 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
-      "2017-06-29 10:36:27,334 : INFO : training on 299450 raw words (163999 effective words) took 0.2s, 963210 effective words/s\n",
-      "2017-06-29 10:36:27,337 : WARNING : under 10 jobs per worker: consider setting a smaller `batch_words' for smoother alpha decay\n"
+      "2017-12-21 16:25:19,717 : INFO : collecting all words and their counts\n",
+      "2017-12-21 16:25:19,718 : INFO : PROGRESS: at sentence #0, processed 0 words, keeping 0 word types\n",
+      "2017-12-21 16:25:19,738 : INFO : collected 10186 word types from a corpus of 59890 raw words and 300 sentences\n",
+      "2017-12-21 16:25:19,739 : INFO : Loading a fresh vocabulary\n",
+      "2017-12-21 16:25:19,787 : INFO : min_count=5 retains 1723 unique words (16% of original 10186, drops 8463)\n",
+      "2017-12-21 16:25:19,788 : INFO : min_count=5 leaves 46858 word corpus (78% of original 59890, drops 13032)\n",
+      "2017-12-21 16:25:19,796 : INFO : deleting the raw counts dictionary of 10186 items\n",
+      "2017-12-21 16:25:19,798 : INFO : sample=0.001 downsamples 49 most-common words\n",
+      "2017-12-21 16:25:19,799 : INFO : downsampling leaves estimated 32849 word corpus (70.1% of prior 46858)\n",
+      "2017-12-21 16:25:19,800 : INFO : estimated required memory for 1723 words and 100 dimensions: 2239900 bytes\n",
+      "2017-12-21 16:25:19,807 : INFO : resetting layer weights\n",
+      "2017-12-21 16:25:19,831 : INFO : training model with 4 workers on 1723 vocabulary and 100 features, using sg=0 hs=0 sample=0.001 negative=5 window=5\n",
+      "2017-12-21 16:25:19,957 : INFO : worker thread finished; awaiting finish of 3 more threads\n",
+      "2017-12-21 16:25:19,959 : INFO : worker thread finished; awaiting finish of 2 more threads\n",
+      "2017-12-21 16:25:19,962 : INFO : worker thread finished; awaiting finish of 1 more threads\n",
+      "2017-12-21 16:25:19,965 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
+      "2017-12-21 16:25:19,966 : INFO : training on 299450 raw words (164263 effective words) took 0.1s, 1234170 effective words/s\n",
+      "2017-12-21 16:25:19,968 : WARNING : under 10 jobs per worker: consider setting a smaller `batch_words' for smoother alpha decay\n"
      ]
     }
    ],
@@ -505,15 +492,15 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2017-06-29 10:36:30,524 : INFO : precomputing L2-norms of word weight vectors\n",
-      "2017-06-29 10:36:30,529 : INFO : family: 0.0% (0/2)\n",
-      "2017-06-29 10:36:30,583 : INFO : gram3-comparative: 0.0% (0/12)\n",
-      "2017-06-29 10:36:30,618 : INFO : gram4-superlative: 0.0% (0/12)\n",
-      "2017-06-29 10:36:30,653 : INFO : gram5-present-participle: 5.0% (1/20)\n",
-      "2017-06-29 10:36:30,701 : INFO : gram6-nationality-adjective: 0.0% (0/20)\n",
-      "2017-06-29 10:36:30,741 : INFO : gram7-past-tense: 0.0% (0/20)\n",
-      "2017-06-29 10:36:30,781 : INFO : gram8-plural: 0.0% (0/12)\n",
-      "2017-06-29 10:36:30,792 : INFO : total: 1.0% (1/98)\n"
+      "2017-12-21 16:25:20,022 : INFO : precomputing L2-norms of word weight vectors\n",
+      "2017-12-21 16:25:20,037 : INFO : family: 0.0% (0/2)\n",
+      "2017-12-21 16:25:20,064 : INFO : gram3-comparative: 0.0% (0/12)\n",
+      "2017-12-21 16:25:20,077 : INFO : gram4-superlative: 0.0% (0/12)\n",
+      "2017-12-21 16:25:20,090 : INFO : gram5-present-participle: 0.0% (0/20)\n",
+      "2017-12-21 16:25:20,108 : INFO : gram6-nationality-adjective: 0.0% (0/20)\n",
+      "2017-12-21 16:25:20,125 : INFO : gram7-past-tense: 0.0% (0/20)\n",
+      "2017-12-21 16:25:20,142 : INFO : gram8-plural: 0.0% (0/12)\n",
+      "2017-12-21 16:25:20,151 : INFO : total: 0.0% (0/98)\n"
      ]
     },
     {
@@ -557,13 +544,14 @@
        "   (u'LARGE', u'LARGEST', u'GOOD', u'BEST'),\n",
        "   (u'LARGE', u'LARGEST', u'GREAT', u'GREATEST')],\n",
        "  'section': u'gram4-superlative'},\n",
-       " {'correct': [(u'LOOK', u'LOOKING', u'SAY', u'SAYING')],\n",
+       " {'correct': [],\n",
        "  'incorrect': [(u'GO', u'GOING', u'LOOK', u'LOOKING'),\n",
        "   (u'GO', u'GOING', u'PLAY', u'PLAYING'),\n",
        "   (u'GO', u'GOING', u'RUN', u'RUNNING'),\n",
        "   (u'GO', u'GOING', u'SAY', u'SAYING'),\n",
        "   (u'LOOK', u'LOOKING', u'PLAY', u'PLAYING'),\n",
        "   (u'LOOK', u'LOOKING', u'RUN', u'RUNNING'),\n",
+       "   (u'LOOK', u'LOOKING', u'SAY', u'SAYING'),\n",
        "   (u'LOOK', u'LOOKING', u'GO', u'GOING'),\n",
        "   (u'PLAY', u'PLAYING', u'RUN', u'RUNNING'),\n",
        "   (u'PLAY', u'PLAYING', u'SAY', u'SAYING'),\n",
@@ -637,7 +625,7 @@
        "   (u'MAN', u'MEN', u'CHILD', u'CHILDREN')],\n",
        "  'section': u'gram8-plural'},\n",
        " {'correct': [], 'incorrect': [], 'section': u'gram9-plural-verbs'},\n",
-       " {'correct': [(u'LOOK', u'LOOKING', u'SAY', u'SAYING')],\n",
+       " {'correct': [],\n",
        "  'incorrect': [(u'HE', u'SHE', u'HIS', u'HER'),\n",
        "   (u'HIS', u'HER', u'HE', u'SHE'),\n",
        "   (u'GOOD', u'BETTER', u'GREAT', u'GREATER'),\n",
@@ -670,6 +658,7 @@
        "   (u'GO', u'GOING', u'SAY', u'SAYING'),\n",
        "   (u'LOOK', u'LOOKING', u'PLAY', u'PLAYING'),\n",
        "   (u'LOOK', u'LOOKING', u'RUN', u'RUNNING'),\n",
+       "   (u'LOOK', u'LOOKING', u'SAY', u'SAYING'),\n",
        "   (u'LOOK', u'LOOKING', u'GO', u'GOING'),\n",
        "   (u'PLAY', u'PLAYING', u'RUN', u'RUNNING'),\n",
        "   (u'PLAY', u'PLAYING', u'SAY', u'SAYING'),\n",
@@ -775,16 +764,18 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2017-06-29 10:36:32,406 : INFO : Pearson correlation coefficient against /home/chinmaya/GSOC/Gensim/gensim/gensim/test/test_data/wordsim353.tsv: 0.1480\n",
-      "2017-06-29 10:36:32,407 : INFO : Spearman rank-order correlation coefficient against /home/chinmaya/GSOC/Gensim/gensim/gensim/test/test_data/wordsim353.tsv: 0.1820\n",
-      "2017-06-29 10:36:32,408 : INFO : Pairs with unknown words ratio: 85.6%\n"
+      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:1: DeprecationWarning: Call to deprecated `evaluate_word_pairs` (Method will be removed in 4.0.0, use self.wv.evaluate_word_pairs() instead).\n",
+      "  \"\"\"Entry point for launching an IPython kernel.\n",
+      "2017-12-21 16:25:20,205 : INFO : Pearson correlation coefficient against /usr/local/lib/python2.7/dist-packages/gensim-3.2.0-py2.7-linux-x86_64.egg/gensim/test/test_data/wordsim353.tsv: 0.1020\n",
+      "2017-12-21 16:25:20,208 : INFO : Spearman rank-order correlation coefficient against /usr/local/lib/python2.7/dist-packages/gensim-3.2.0-py2.7-linux-x86_64.egg/gensim/test/test_data/wordsim353.tsv: 0.0816\n",
+      "2017-12-21 16:25:20,210 : INFO : Pairs with unknown words ratio: 85.6%\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "((0.14802303571188502, 0.29991609812618542),\n",
-       " SpearmanrResult(correlation=0.18196628619816893, pvalue=0.20125520403388769),\n",
+       "((0.10198070183634717, 0.47640746107165499),\n",
+       " SpearmanrResult(correlation=0.081592940565853908, pvalue=0.56922382052578302),\n",
        " 85.55240793201133)"
       ]
      },
@@ -821,10 +812,10 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2017-06-29 10:36:33,533 : INFO : saving Word2Vec object under /tmp/tmpNVnhzsgensim_temp, separately None\n",
-      "2017-06-29 10:36:33,535 : INFO : not storing attribute syn0norm\n",
-      "2017-06-29 10:36:33,535 : INFO : not storing attribute cum_table\n",
-      "2017-06-29 10:36:33,550 : INFO : saved /tmp/tmpNVnhzsgensim_temp\n"
+      "2017-12-21 16:25:20,225 : INFO : saving Word2Vec object under /tmp/tmpot5iTxgensim_temp, separately None\n",
+      "2017-12-21 16:25:20,228 : INFO : not storing attribute syn0norm\n",
+      "2017-12-21 16:25:20,230 : INFO : not storing attribute cum_table\n",
+      "2017-12-21 16:25:20,242 : INFO : saved /tmp/tmpot5iTxgensim_temp\n"
      ]
     }
    ],
@@ -845,11 +836,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2017-06-29 10:36:34,033 : INFO : loading Word2Vec object from /tmp/tmpNVnhzsgensim_temp\n",
-      "2017-06-29 10:36:34,041 : INFO : loading wv recursively from /tmp/tmpNVnhzsgensim_temp.wv.* with mmap=None\n",
-      "2017-06-29 10:36:34,042 : INFO : setting ignored attribute syn0norm to None\n",
-      "2017-06-29 10:36:34,044 : INFO : setting ignored attribute cum_table to None\n",
-      "2017-06-29 10:36:34,048 : INFO : loaded /tmp/tmpNVnhzsgensim_temp\n"
+      "2017-12-21 16:25:20,247 : INFO : loading Word2Vec object from /tmp/tmpot5iTxgensim_temp\n",
+      "2017-12-21 16:25:20,254 : INFO : loading wv recursively from /tmp/tmpot5iTxgensim_temp.wv.* with mmap=None\n",
+      "2017-12-21 16:25:20,255 : INFO : setting ignored attribute syn0norm to None\n",
+      "2017-12-21 16:25:20,255 : INFO : setting ignored attribute cum_table to None\n",
+      "2017-12-21 16:25:20,256 : INFO : loaded /tmp/tmpot5iTxgensim_temp\n"
      ]
     }
    ],
@@ -888,29 +879,28 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2017-06-29 10:36:35,557 : INFO : loading Word2Vec object from /tmp/tmpNVnhzsgensim_temp\n",
-      "2017-06-29 10:36:35,566 : INFO : loading wv recursively from /tmp/tmpNVnhzsgensim_temp.wv.* with mmap=None\n",
-      "2017-06-29 10:36:35,567 : INFO : setting ignored attribute syn0norm to None\n",
-      "2017-06-29 10:36:35,572 : INFO : setting ignored attribute cum_table to None\n",
-      "2017-06-29 10:36:35,573 : INFO : loaded /tmp/tmpNVnhzsgensim_temp\n",
-      "2017-06-29 10:36:35,581 : INFO : collecting all words and their counts\n",
-      "2017-06-29 10:36:35,582 : INFO : PROGRESS: at sentence #0, processed 0 words, keeping 0 word types\n",
-      "2017-06-29 10:36:35,586 : INFO : collected 13 word types from a corpus of 13 raw words and 1 sentences\n",
-      "2017-06-29 10:36:35,587 : INFO : Updating model with new vocabulary\n",
-      "2017-06-29 10:36:35,589 : INFO : New added 0 unique words (0% of original 13)\n",
-      "                        and increased the count of 0 pre-existing words (0% of original 13)\n",
-      "2017-06-29 10:36:35,590 : INFO : deleting the raw counts dictionary of 13 items\n",
-      "2017-06-29 10:36:35,591 : INFO : sample=0.001 downsamples 0 most-common words\n",
-      "2017-06-29 10:36:35,592 : INFO : downsampling leaves estimated 0 word corpus (0.0% of prior 0)\n",
-      "2017-06-29 10:36:35,594 : INFO : estimated required memory for 1723 words and 100 dimensions: 2239900 bytes\n",
-      "2017-06-29 10:36:35,599 : INFO : updating layer weights\n",
-      "2017-06-29 10:36:35,601 : INFO : training model with 4 workers on 1723 vocabulary and 100 features, using sg=0 hs=0 sample=0.001 negative=5 window=5\n",
-      "2017-06-29 10:36:35,606 : INFO : worker thread finished; awaiting finish of 3 more threads\n",
-      "2017-06-29 10:36:35,607 : INFO : worker thread finished; awaiting finish of 2 more threads\n",
-      "2017-06-29 10:36:35,609 : INFO : worker thread finished; awaiting finish of 1 more threads\n",
-      "2017-06-29 10:36:35,613 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
-      "2017-06-29 10:36:35,614 : INFO : training on 65 raw words (28 effective words) took 0.0s, 3413 effective words/s\n",
-      "2017-06-29 10:36:35,615 : WARNING : under 10 jobs per worker: consider setting a smaller `batch_words' for smoother alpha decay\n"
+      "2017-12-21 16:25:20,266 : INFO : loading Word2Vec object from /tmp/tmpot5iTxgensim_temp\n",
+      "2017-12-21 16:25:20,273 : INFO : loading wv recursively from /tmp/tmpot5iTxgensim_temp.wv.* with mmap=None\n",
+      "2017-12-21 16:25:20,273 : INFO : setting ignored attribute syn0norm to None\n",
+      "2017-12-21 16:25:20,274 : INFO : setting ignored attribute cum_table to None\n",
+      "2017-12-21 16:25:20,275 : INFO : loaded /tmp/tmpot5iTxgensim_temp\n",
+      "2017-12-21 16:25:20,279 : INFO : collecting all words and their counts\n",
+      "2017-12-21 16:25:20,280 : INFO : PROGRESS: at sentence #0, processed 0 words, keeping 0 word types\n",
+      "2017-12-21 16:25:20,280 : INFO : collected 13 word types from a corpus of 13 raw words and 1 sentences\n",
+      "2017-12-21 16:25:20,281 : INFO : Updating model with new vocabulary\n",
+      "2017-12-21 16:25:20,281 : INFO : New added 0 unique words (0% of original 13) and increased the count of 0 pre-existing words (0% of original 13)\n",
+      "2017-12-21 16:25:20,282 : INFO : deleting the raw counts dictionary of 13 items\n",
+      "2017-12-21 16:25:20,282 : INFO : sample=0.001 downsamples 0 most-common words\n",
+      "2017-12-21 16:25:20,283 : INFO : downsampling leaves estimated 0 word corpus (0.0% of prior 0)\n",
+      "2017-12-21 16:25:20,283 : INFO : estimated required memory for 1723 words and 100 dimensions: 2239900 bytes\n",
+      "2017-12-21 16:25:20,287 : INFO : updating layer weights\n",
+      "2017-12-21 16:25:20,288 : INFO : training model with 4 workers on 1723 vocabulary and 100 features, using sg=0 hs=0 sample=0.001 negative=5 window=5\n",
+      "2017-12-21 16:25:20,291 : INFO : worker thread finished; awaiting finish of 3 more threads\n",
+      "2017-12-21 16:25:20,292 : INFO : worker thread finished; awaiting finish of 2 more threads\n",
+      "2017-12-21 16:25:20,292 : INFO : worker thread finished; awaiting finish of 1 more threads\n",
+      "2017-12-21 16:25:20,293 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
+      "2017-12-21 16:25:20,293 : INFO : training on 65 raw words (30 effective words) took 0.0s, 10799 effective words/s\n",
+      "2017-12-21 16:25:20,294 : WARNING : under 10 jobs per worker: consider setting a smaller `batch_words' for smoother alpha decay\n"
      ]
     }
    ],
@@ -946,13 +936,15 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2017-06-29 10:36:36,558 : INFO : precomputing L2-norms of word weight vectors\n"
+      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:1: DeprecationWarning: Call to deprecated `most_similar` (Method will be removed in 4.0.0, use self.wv.most_similar() instead).\n",
+      "  \"\"\"Entry point for launching an IPython kernel.\n",
+      "2017-12-21 16:25:20,299 : INFO : precomputing L2-norms of word weight vectors\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "[('longer', 0.9916001558303833)]"
+       "[('longer', 0.9912284016609192)]"
       ]
      },
      "execution_count": 20,
@@ -973,7 +965,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2017-06-29 10:36:37,587 : WARNING : vectors for words set(['lunch', 'input', 'cat']) are not present in the model, ignoring these words\n"
+      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:1: DeprecationWarning: Call to deprecated `doesnt_match` (Method will be removed in 4.0.0, use self.wv.doesnt_match() instead).\n",
+      "  \"\"\"Entry point for launching an IPython kernel.\n",
+      "2017-12-21 16:25:20,311 : WARNING : vectors for words set(['lunch', 'input', 'cat']) are not present in the model, ignoring these words\n"
      ]
     },
     {
@@ -1000,8 +994,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.99914980326\n",
-      "0.995203599363\n"
+      "0.999182520993\n",
+      "0.995758952957\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:1: DeprecationWarning: Call to deprecated `similarity` (Method will be removed in 4.0.0, use self.wv.similarity() instead).\n",
+      "  \"\"\"Entry point for launching an IPython kernel.\n",
+      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:2: DeprecationWarning: Call to deprecated `similarity` (Method will be removed in 4.0.0, use self.wv.similarity() instead).\n",
+      "  \n"
      ]
     }
    ],
@@ -1026,7 +1030,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[('more', 0.0010031241), ('can', 0.00092728506), ('continue', 0.00087752187), ('training', 0.00080867682), ('there', 0.00075810013), ('three', 0.00075742434), ('australia', 0.00075092859), ('government', 0.0007505166), ('their', 0.00074284436), ('us', 0.00073618308)]\n"
+      "[('more', 0.0010738152), ('continue', 0.00097854261), ('can', 0.00088260754), ('training', 0.00085614622), ('it', 0.00077729771), ('there', 0.00076619512), ('australia', 0.00075446483), ('government', 0.00074923009), ('three', 0.00074201578), ('say', 0.00073336047)]\n"
      ]
     }
    ],
@@ -1053,29 +1057,51 @@
    "execution_count": 24,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:1: DeprecationWarning: Call to deprecated `__getitem__` (Method will be removed in 4.0.0, use self.wv.__getitem__() instead).\n",
+      "  \"\"\"Entry point for launching an IPython kernel.\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "array([ 0.00313283,  0.02270725, -0.0271953 , -0.00758541, -0.03084832,\n",
-       "       -0.03540374,  0.00492509, -0.08605313, -0.00946797, -0.01907749,\n",
-       "        0.0190183 , -0.02690557, -0.0665649 , -0.01858037, -0.02188841,\n",
-       "        0.04523174, -0.03034727, -0.00294408,  0.02323552, -0.02585541,\n",
-       "       -0.06424622, -0.00719615,  0.06513051,  0.04760417,  0.02894664,\n",
-       "        0.01104737, -0.00513022,  0.01997521, -0.0142725 ,  0.00513998,\n",
-       "        0.00099208,  0.07234117,  0.01262072, -0.00151589,  0.02293177,\n",
-       "        0.02008283, -0.03751098,  0.01848946,  0.01339256, -0.06704903,\n",
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-       "       -0.01792447,  0.03566624, -0.01676619,  0.00202644,  0.01494004,\n",
-       "        0.00792563, -0.08858279, -0.08187189,  0.0634894 , -0.02159132,\n",
-       "       -0.05633228, -0.04459627, -0.04756012, -0.0024067 , -0.02443214,\n",
-       "       -0.02618414, -0.0249261 ,  0.02130016, -0.05084078,  0.00092178], dtype=float32)"
+       "array([  5.09738876e-03,   2.24877093e-02,  -2.87369397e-02,\n",
+       "        -7.89474510e-03,  -3.53376977e-02,  -4.06462997e-02,\n",
+       "         8.37074965e-03,  -9.41743553e-02,  -1.49697680e-02,\n",
+       "        -2.52366997e-02,   2.65039261e-02,  -2.40766741e-02,\n",
+       "        -6.85459673e-02,  -2.18667872e-02,  -2.14908309e-02,\n",
+       "         4.93087023e-02,  -2.92534381e-02,  -1.88865454e-03,\n",
+       "         2.26040259e-02,  -2.84134373e-02,  -6.83278441e-02,\n",
+       "        -6.75840862e-03,   7.07488284e-02,   5.21996431e-02,\n",
+       "         2.36152187e-02,   1.46892993e-02,  -4.65966389e-03,\n",
+       "         1.81521084e-02,  -1.66943893e-02,  -5.45500545e-04,\n",
+       "         9.81825942e-05,   7.39010796e-02,   8.24716035e-03,\n",
+       "        -3.30754719e-03,   2.59200167e-02,   2.25928240e-02,\n",
+       "        -4.78062779e-02,   1.68881603e-02,   1.27557423e-02,\n",
+       "        -7.06009716e-02,  -8.09376314e-02,   5.74318040e-03,\n",
+       "        -4.43559177e-02,  -3.11263874e-02,   3.13786902e-02,\n",
+       "        -5.85887060e-02,   4.01994959e-02,   4.16668272e-03,\n",
+       "        -1.61651354e-02,   4.03134711e-02,   1.64259598e-02,\n",
+       "         6.99962350e-03,  -3.78169157e-02,   7.13254418e-03,\n",
+       "         1.50061641e-02,  -2.01686379e-02,   5.82966506e-02,\n",
+       "        -2.78297253e-02,   1.81606133e-02,  -3.56090963e-02,\n",
+       "        -2.89950594e-02,  -4.97125871e-02,   1.93165317e-02,\n",
+       "        -2.90847234e-02,  -1.07406462e-02,  -6.75966665e-02,\n",
+       "        -8.05926248e-02,   3.87299024e-02,   5.84914126e-02,\n",
+       "        -2.87338771e-04,  -1.47654228e-02,   8.10218137e-03,\n",
+       "        -5.38245104e-02,   1.52460849e-02,  -4.90099788e-02,\n",
+       "        -5.80144748e-02,   3.85234654e-02,  -4.70485678e-03,\n",
+       "         4.69632484e-02,   4.56776805e-02,  -2.43359935e-02,\n",
+       "         3.39893550e-02,  -1.67205688e-02,   2.83701695e-04,\n",
+       "         1.79673471e-02,   1.03446953e-02,  -9.53995809e-02,\n",
+       "        -8.30710083e-02,   6.81236908e-02,  -3.47741581e-02,\n",
+       "        -6.30266443e-02,  -4.59022224e-02,  -4.83927876e-02,\n",
+       "        -5.33403922e-03,  -2.84888912e-02,  -2.93440577e-02,\n",
+       "        -2.53614448e-02,   2.14495976e-02,  -5.01872450e-02,\n",
+       "        -2.60770670e-03], dtype=float32)"
       ]
      },
      "execution_count": 24,
@@ -1112,29 +1138,29 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2017-06-29 10:36:42,297 : INFO : collecting all words and their counts\n",
-      "2017-06-29 10:36:42,300 : INFO : PROGRESS: at sentence #0, processed 0 words, keeping 0 word types\n",
-      "2017-06-29 10:36:42,333 : INFO : collected 10186 word types from a corpus of 59890 raw words and 300 sentences\n",
-      "2017-06-29 10:36:42,335 : INFO : Loading a fresh vocabulary\n",
-      "2017-06-29 10:36:42,372 : INFO : min_count=1 retains 10186 unique words (100% of original 10186, drops 0)\n",
-      "2017-06-29 10:36:42,372 : INFO : min_count=1 leaves 59890 word corpus (100% of original 59890, drops 0)\n",
-      "2017-06-29 10:36:42,406 : INFO : deleting the raw counts dictionary of 10186 items\n",
-      "2017-06-29 10:36:42,407 : INFO : sample=0.001 downsamples 37 most-common words\n",
-      "2017-06-29 10:36:42,408 : INFO : downsampling leaves estimated 47231 word corpus (78.9% of prior 59890)\n",
-      "2017-06-29 10:36:42,409 : INFO : estimated required memory for 10186 words and 100 dimensions: 13241800 bytes\n",
-      "2017-06-29 10:36:42,452 : INFO : resetting layer weights\n",
-      "2017-06-29 10:36:42,568 : INFO : training model with 3 workers on 10186 vocabulary and 100 features, using sg=1 hs=0 sample=0.001 negative=5 window=5\n",
-      "2017-06-29 10:36:43,357 : INFO : worker thread finished; awaiting finish of 2 more threads\n",
-      "2017-06-29 10:36:43,369 : INFO : worker thread finished; awaiting finish of 1 more threads\n",
-      "2017-06-29 10:36:43,392 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
-      "2017-06-29 10:36:43,393 : INFO : training on 299450 raw words (235935 effective words) took 0.8s, 286433 effective words/s\n"
+      "2017-12-21 16:25:20,341 : INFO : collecting all words and their counts\n",
+      "2017-12-21 16:25:20,343 : INFO : PROGRESS: at sentence #0, processed 0 words, keeping 0 word types\n",
+      "2017-12-21 16:25:20,361 : INFO : collected 10186 word types from a corpus of 59890 raw words and 300 sentences\n",
+      "2017-12-21 16:25:20,362 : INFO : Loading a fresh vocabulary\n",
+      "2017-12-21 16:25:20,392 : INFO : min_count=1 retains 10186 unique words (100% of original 10186, drops 0)\n",
+      "2017-12-21 16:25:20,393 : INFO : min_count=1 leaves 59890 word corpus (100% of original 59890, drops 0)\n",
+      "2017-12-21 16:25:20,428 : INFO : deleting the raw counts dictionary of 10186 items\n",
+      "2017-12-21 16:25:20,429 : INFO : sample=0.001 downsamples 37 most-common words\n",
+      "2017-12-21 16:25:20,429 : INFO : downsampling leaves estimated 47231 word corpus (78.9% of prior 59890)\n",
+      "2017-12-21 16:25:20,430 : INFO : estimated required memory for 10186 words and 100 dimensions: 13241800 bytes\n",
+      "2017-12-21 16:25:20,454 : INFO : resetting layer weights\n",
+      "2017-12-21 16:25:20,556 : INFO : training model with 3 workers on 10186 vocabulary and 100 features, using sg=1 hs=0 sample=0.001 negative=5 window=5\n",
+      "2017-12-21 16:25:21,174 : INFO : worker thread finished; awaiting finish of 2 more threads\n",
+      "2017-12-21 16:25:21,185 : INFO : worker thread finished; awaiting finish of 1 more threads\n",
+      "2017-12-21 16:25:21,191 : INFO : worker thread finished; awaiting finish of 0 more threads\n",
+      "2017-12-21 16:25:21,192 : INFO : training on 299450 raw words (235935 effective words) took 0.6s, 372397 effective words/s\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1348399.25\n"
+      "1483200.25\n"
      ]
     }
    ],
@@ -1158,14 +1184,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "['/home/chinmaya/GSOC/Gensim/gensim/docs/notebooks/../../gensim/test/test_data/lee_background.cor', '/home/chinmaya/GSOC/Gensim/gensim/docs/notebooks/text8_1000000', '/home/chinmaya/GSOC/Gensim/gensim/docs/notebooks/text8_10000000', '/home/chinmaya/GSOC/Gensim/gensim/docs/notebooks/text8_50000000', '/home/chinmaya/GSOC/Gensim/gensim/docs/notebooks/text8']\n"
+      "['/home/markroxor/Documents/gensim/docs/notebooks/../../gensim/test/test_data/lee_background.cor', '/home/markroxor/Documents/gensim/docs/notebooks/text8_1000000', '/home/markroxor/Documents/gensim/docs/notebooks/text8_10000000', '/home/markroxor/Documents/gensim/docs/notebooks/text8_50000000', '/home/markroxor/Documents/gensim/docs/notebooks/text8']\n"
      ]
     }
    ],
@@ -1214,49 +1240,60 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 38,
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "logging.getLogger().setLevel(logging.ERROR)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "['/home/chinmaya/GSOC/Gensim/gensim/docs/notebooks/../../gensim/test/test_data/lee_background.cor', '/home/chinmaya/GSOC/Gensim/gensim/docs/notebooks/text8_50000000']\n",
-      "    compute_loss  hs        mean  sg       std  \\\n",
-      "12          True   0  125.242767   1  0.442522   \n",
-      "13         False   0  124.090732   1  0.432895   \n",
-      "14          True   1  252.800164   1  1.140344   \n",
-      "15         False   1  245.065643   1  2.657392   \n",
-      "8           True   0   43.812430   0  1.216697   \n",
-      "9          False   0   42.815214   0  0.142814   \n",
-      "10          True   1   74.801153   0  0.300728   \n",
-      "11         False   1   74.236441   0  0.126426   \n",
-      "4           True   0    0.560387   1  0.005805   \n",
-      "5          False   0    0.687179   1  0.143629   \n",
-      "6           True   1    1.126855   1  0.004407   \n",
-      "7          False   1    1.135358   1  0.059161   \n",
-      "0           True   0    0.316948   0  0.005148   \n",
-      "1          False   0    0.319236   0  0.005792   \n",
-      "2           True   1    0.429879   0  0.005373   \n",
-      "3          False   1    0.429489   0  0.000756   \n",
+      "['/home/markroxor/Documents/gensim/docs/notebooks/../../gensim/test/test_data/lee_background.cor', '/home/markroxor/Documents/gensim/docs/notebooks/../../gensim/test/test_data/lee_background.cor']\n",
+      "    compute_loss  hs      mean  sg       std  \\\n",
+      "4           True   0  0.610034   1  0.022937   \n",
+      "12          True   0  0.593313   1  0.037120   \n",
+      "5          False   0  0.588043   1  0.027460   \n",
+      "13         False   0  0.578279   1  0.019227   \n",
+      "6           True   1  1.318451   1  0.100453   \n",
+      "14          True   1  1.309022   1  0.026008   \n",
+      "7          False   1  1.144407   1  0.120123   \n",
+      "15         False   1  1.362300   1  0.049005   \n",
+      "0           True   0  0.492458   0  0.079990   \n",
+      "8           True   0  0.452251   0  0.024318   \n",
+      "1          False   0  0.499348   0  0.130881   \n",
+      "9          False   0  0.469124   0  0.048385   \n",
+      "2           True   1  0.553494   0  0.033808   \n",
+      "10          True   1  0.604576   0  0.128907   \n",
+      "3          False   1  0.514230   0  0.019456   \n",
+      "11         False   1  0.477603   0  0.046651   \n",
       "\n",
       "                                           train_data  \n",
-      "12  /home/chinmaya/GSOC/Gensim/gensim/docs/noteboo...  \n",
-      "13  /home/chinmaya/GSOC/Gensim/gensim/docs/noteboo...  \n",
-      "14  /home/chinmaya/GSOC/Gensim/gensim/docs/noteboo...  \n",
-      "15  /home/chinmaya/GSOC/Gensim/gensim/docs/noteboo...  \n",
-      "8   /home/chinmaya/GSOC/Gensim/gensim/docs/noteboo...  \n",
-      "9   /home/chinmaya/GSOC/Gensim/gensim/docs/noteboo...  \n",
-      "10  /home/chinmaya/GSOC/Gensim/gensim/docs/noteboo...  \n",
-      "11  /home/chinmaya/GSOC/Gensim/gensim/docs/noteboo...  \n",
-      "4   /home/chinmaya/GSOC/Gensim/gensim/docs/noteboo...  \n",
-      "5   /home/chinmaya/GSOC/Gensim/gensim/docs/noteboo...  \n",
-      "6   /home/chinmaya/GSOC/Gensim/gensim/docs/noteboo...  \n",
-      "7   /home/chinmaya/GSOC/Gensim/gensim/docs/noteboo...  \n",
-      "0   /home/chinmaya/GSOC/Gensim/gensim/docs/noteboo...  \n",
-      "1   /home/chinmaya/GSOC/Gensim/gensim/docs/noteboo...  \n",
-      "2   /home/chinmaya/GSOC/Gensim/gensim/docs/noteboo...  \n",
-      "3   /home/chinmaya/GSOC/Gensim/gensim/docs/noteboo...  \n"
+      "4   /home/markroxor/Documents/gensim/docs/notebook...  \n",
+      "12  /home/markroxor/Documents/gensim/docs/notebook...  \n",
+      "5   /home/markroxor/Documents/gensim/docs/notebook...  \n",
+      "13  /home/markroxor/Documents/gensim/docs/notebook...  \n",
+      "6   /home/markroxor/Documents/gensim/docs/notebook...  \n",
+      "14  /home/markroxor/Documents/gensim/docs/notebook...  \n",
+      "7   /home/markroxor/Documents/gensim/docs/notebook...  \n",
+      "15  /home/markroxor/Documents/gensim/docs/notebook...  \n",
+      "0   /home/markroxor/Documents/gensim/docs/notebook...  \n",
+      "8   /home/markroxor/Documents/gensim/docs/notebook...  \n",
+      "1   /home/markroxor/Documents/gensim/docs/notebook...  \n",
+      "9   /home/markroxor/Documents/gensim/docs/notebook...  \n",
+      "2   /home/markroxor/Documents/gensim/docs/notebook...  \n",
+      "10  /home/markroxor/Documents/gensim/docs/notebook...  \n",
+      "3   /home/markroxor/Documents/gensim/docs/notebook...  \n",
+      "11  /home/markroxor/Documents/gensim/docs/notebook...  \n"
      ]
     }
    ],
@@ -1299,24 +1336,5482 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Conclusion\n",
+    "### Adding Word2Vec \"model to dict\" method to production pipeline\n",
+    "Suppose, we still want more performance improvement in production. \n",
+    "One good way is to cache all the similar words in a dictionary.\n",
+    "So that next time when we get the similar query word, we'll search it first in the dict.\n",
+    "And if it's a hit then we will show the result directly from the dictionary.\n",
+    "otherwise we will query the word and then cache it so that it doesn't miss next time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "logging.getLogger().setLevel(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "four [('over', 0.9999380111694336), ('world', 0.9999357461929321), ('after', 0.9999346137046814), ('local', 0.9999341368675232), ('on', 0.9999333024024963), ('last', 0.9999300241470337), ('into', 0.9999298453330994), ('a', 0.9999290704727173), ('at', 0.9999290108680725), ('from', 0.99992835521698)]\n",
+      "jihad [('on', 0.9995617866516113), ('gaza', 0.9995616674423218), ('and', 0.9995588064193726), ('palestinian', 0.9995542168617249), ('in', 0.999552845954895), ('former', 0.9995408654212952), ('from', 0.999538242816925), ('were', 0.9995372295379639), ('security', 0.9995347857475281), ('an', 0.9995279908180237)]\n",
+      "captain [('are', 0.9998434782028198), ('any', 0.9998313784599304), ('out', 0.9998288154602051), ('now', 0.9998278617858887), ('over', 0.999825656414032), ('have', 0.9998254776000977), ('australian', 0.999824583530426), ('is', 0.999819815158844), ('including', 0.9998170137405396), ('at', 0.99981689453125)]\n"
+     ]
+    }
+   ],
+   "source": [
+    "most_similars_precalc = {word : model.wv.most_similar(word) for word in model.wv.index2word}\n",
+    "for i, (key, value) in enumerate(most_similars_precalc.iteritems()):\n",
+    "    if i==3:\n",
+    "        break\n",
+    "    print key, value"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Comparison with and without caching"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "for time being lets take 4 words randomly"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "words = ['voted','few','their','around']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Without caching"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[('action', 0.998902440071106), ('would', 0.998866081237793), ('could', 0.9988579154014587), ('need', 0.9988564848899841), ('will', 0.9988539218902588), ('it', 0.9988477826118469), ('expected', 0.998836874961853), ('legal', 0.9988292455673218), ('we', 0.9988272786140442), ('called', 0.9988245368003845)]\n",
+      "[('from', 0.9997947216033936), ('which', 0.9997938871383667), ('australian', 0.9997910261154175), ('a', 0.9997884035110474), ('police', 0.9997864365577698), ('told', 0.9997841119766235), ('his', 0.9997839331626892), ('with', 0.9997835159301758), ('if', 0.999783456325531), ('be', 0.999782383441925)]\n",
+      "[('up', 0.9999563097953796), ('last', 0.9999551177024841), ('on', 0.9999517798423767), ('over', 0.9999500513076782), ('are', 0.9999494552612305), ('also', 0.9999493956565857), ('and', 0.9999493360519409), ('had', 0.9999492168426514), ('as', 0.9999483227729797), ('an', 0.9999469518661499)]\n",
+      "[('over', 0.9999304413795471), ('their', 0.9999291896820068), ('three', 0.9999284148216248), ('on', 0.9999280571937561), ('two', 0.9999274015426636), ('last', 0.9999268054962158), ('have', 0.9999215602874756), ('as', 0.999921441078186), ('us', 0.9999210834503174), ('from', 0.9999209046363831)]\n",
+      "0.00567579269409\n"
+     ]
+    }
+   ],
+   "source": [
+    "start = time.time()\n",
+    "for word in words:\n",
+    "    result = model.wv.most_similar(word)\n",
+    "    print(result)\n",
+    "end = time.time()\n",
+    "print(end-start)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now with caching"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[('action', 0.998902440071106), ('would', 0.998866081237793), ('could', 0.9988579154014587), ('need', 0.9988564848899841), ('will', 0.9988539218902588), ('it', 0.9988477826118469), ('expected', 0.998836874961853), ('legal', 0.9988292455673218), ('we', 0.9988272786140442), ('called', 0.9988245368003845)]\n",
+      "[('from', 0.9997947216033936), ('which', 0.9997938871383667), ('australian', 0.9997910261154175), ('a', 0.9997884035110474), ('police', 0.9997864365577698), ('told', 0.9997841119766235), ('his', 0.9997839331626892), ('with', 0.9997835159301758), ('if', 0.999783456325531), ('be', 0.999782383441925)]\n",
+      "[('up', 0.9999563097953796), ('last', 0.9999551177024841), ('on', 0.9999517798423767), ('over', 0.9999500513076782), ('are', 0.9999494552612305), ('also', 0.9999493956565857), ('and', 0.9999493360519409), ('had', 0.9999492168426514), ('as', 0.9999483227729797), ('an', 0.9999469518661499)]\n",
+      "[('over', 0.9999304413795471), ('their', 0.9999291896820068), ('three', 0.9999284148216248), ('on', 0.9999280571937561), ('two', 0.9999274015426636), ('last', 0.9999268054962158), ('have', 0.9999215602874756), ('as', 0.999921441078186), ('us', 0.9999210834503174), ('from', 0.9999209046363831)]\n",
+      "0.000878810882568\n"
+     ]
+    }
+   ],
+   "source": [
+    "start = time.time()\n",
+    "for word in words:\n",
+    "    if 'voted' in most_similars_precalc:\n",
+    "        result = most_similars_precalc[word]\n",
+    "        print(result)\n",
+    "    else:\n",
+    "        result = model.wv.most_similar(word)\n",
+    "        most_similars_precalc[word] = result\n",
+    "        print(result)\n",
+    "    \n",
+    "end = time.time()\n",
+    "print(end-start)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Clearly you can see the improvement but this difference will be even larger when we take more words in the consideration."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Visualising the Word Embeddings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The word embeddings made by the model can be visualised by reducing dimensionality of the words to 2 dimensions using tSNE.\n",
     "\n",
-    "In this tutorial we learned how to train word2vec models on your custom data and also how to evaluate it. Hope that you too will find this popular tool useful in your Machine Learning tasks!\n",
+    "Visualisations can be used to notice semantic and syntactic trends in the data.\n",
     "\n",
-    "## Links\n",
+    "Example: Semantic- words like cat, dog, cow, etc. have a tendency to lie close by\n",
+    "         Syntactic- words like run, running or cut, cutting lie close together.\n",
+    "Vector relations like vKing - vMan = vQueen - vWoman can also be noticed.\n",
     "\n",
+    "Additional dependencies : \n",
+    "- sklearn\n",
+    "- numpy\n",
+    "- plotly\n",
     "\n",
-    "Full `word2vec` API docs [here](http://radimrehurek.com/gensim/models/word2vec.html); get [gensim](http://radimrehurek.com/gensim/) here. Original C toolkit and `word2vec` papers by Google [here](https://code.google.com/archive/p/word2vec/)."
+    "The function below can be used to plot the embeddings in an ipython notebook.\n",
+    "It requires the model as the necessary parameter. If you don't have the model, you can load it by\n",
+    "\n",
+    "`model = gensim.models.Word2Vec.load('path/to/model')`\n",
+    "\n",
+    "If you don't want to plot inside a notebook, set the `plot_in_notebook` parameter to `False`.\n",
+    "\n",
+    "Note: the model used for the visualisation is trained on a small corpus. Thus some of the relations might not be so clear\n",
+    "\n",
+    "Beware : This sort dimension reduction comes at the cost of loss of information."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 43,
+   "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "from sklearn.decomposition import IncrementalPCA    # inital reduction\n",
+    "from sklearn.manifold import TSNE                   # final reduction\n",
+    "import numpy as np                                  # array handling\n",
+    "\n",
+    "from plotly.offline import init_notebook_mode, iplot, plot\n",
+    "import plotly.graph_objs as go\n",
+    "\n",
+    "def reduce_dimensions(model, plot_in_notebook = True):\n",
+    "\n",
+    "    num_dimensions = 2  # final num dimensions (2D, 3D, etc)\n",
+    "\n",
+    "    vectors = []        # positions in vector space\n",
+    "    labels = []         # keep track of words to label our data again later\n",
+    "    for word in model.wv.vocab:\n",
+    "        vectors.append(model[word])\n",
+    "        labels.append(word)\n",
+    "\n",
+    "\n",
+    "    # convert both lists into numpy vectors for reduction\n",
+    "    vectors = np.asarray(vectors)\n",
+    "    labels = np.asarray(labels)\n",
+    "    \n",
+    "    # reduce using t-SNE\n",
+    "    vectors = np.asarray(vectors)\n",
+    "    logging.info('starting tSNE dimensionality reduction. This may take some time.')\n",
+    "    tsne = TSNE(n_components=num_dimensions, random_state=0)\n",
+    "    vectors = tsne.fit_transform(vectors)\n",
+    "\n",
+    "    x_vals = [v[0] for v in vectors]\n",
+    "    y_vals = [v[1] for v in vectors]\n",
+    "        \n",
+    "    # Create a trace\n",
+    "    trace = go.Scatter(\n",
+    "        x=x_vals,\n",
+    "        y=y_vals,\n",
+    "        mode='text',\n",
+    "        text=labels\n",
+    "        )\n",
+    "    \n",
+    "    data = [trace]\n",
+    "    \n",
+    "    logging.info('All done. Plotting.')\n",
+    "    \n",
+    "    if plot_in_notebook:\n",
+    "        init_notebook_mode(connected=True)\n",
+    "        iplot(data, filename='word-embedding-plot')\n",
+    "    else:\n",
+    "        plot(data, filename='word-embedding-plot.html')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:15: DeprecationWarning:\n",
+      "\n",
+      "Call to deprecated `__getitem__` (Method will be removed in 4.0.0, use self.wv.__getitem__() instead).\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
+      ],
+      "text/vnd.plotly.v1+html": [
+       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
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-14.143150329589844, 29.10352897644043, -13.07437801361084, -16.42338752746582, 12.041471481323242, 3.1221883296966553, 3.815453052520752, 9.741093635559082, 26.253925323486328, 5.667520523071289, 34.39023208618164, 15.848939895629883, -30.43429946899414, 27.46099090576172, 5.473353862762451, 4.344359874725342, -35.79289245605469, -42.538272857666016, 31.83509635925293, -14.375887870788574, 24.117300033569336, 35.076812744140625, -14.425128936767578, -14.133423805236816, -19.14742660522461, 5.863757610321045, -32.75071334838867, 3.4637153148651123, -10.95847225189209, -41.66207504272461, -0.480092853307724, -15.161439895629883, 12.9270601272583, -5.732761383056641, -9.612813949584961, -15.846092224121094, -42.96913528442383, 20.70514488220215, -20.909164428710938, 1.848104476928711, -41.80708694458008, 13.627616882324219, -3.9661667346954346, 23.242279052734375, 24.843175888061523, -24.877058029174805, -40.528377532958984, 13.4859037399292, 12.088449478149414, -7.190152645111084, 3.1484739780426025, -37.387699127197266, 1.023643136024475, 34.17557144165039, -6.9046735763549805, -14.83070182800293, 19.23175811767578, 33.86227035522461, -21.879568099975586, 5.495102882385254, 6.615853786468506, -37.47934341430664, -33.968875885009766, -36.7241096496582, -22.772266387939453, -12.762992858886719, -14.93494987487793, -8.788796424865723, 3.272709369659424, 28.125947952270508, 6.018498420715332, -16.063077926635742, -2.853231906890869, -6.535486221313477, -32.00550842285156, 28.933439254760742, 33.62485885620117, -35.81085968017578, 26.165321350097656, 19.277671813964844, -8.734709739685059, 35.36589813232422, -41.85238265991211, 12.574785232543945, -1.6294138431549072, 7.020055294036865, -1.627120852470398, -37.00338363647461, -11.142776489257812, 24.59052276611328, 33.717525482177734, -32.18458938598633, -40.08710479736328, 36.612762451171875, 1.2783867120742798, -40.73082733154297, 11.493650436401367, 11.884822845458984, 24.37686538696289, -26.874631881713867, -9.136053085327148, 17.196853637695312, 35.42644500732422, 0.5330314040184021, -5.65974760055542, -14.576261520385742, -36.21036148071289, -6.362594127655029, -41.792240142822266, -39.324485778808594, -39.350860595703125, -28.345518112182617, -13.672765731811523, 0.7410794496536255, -15.97929859161377, 20.714847564697266, -7.553438186645508, 30.073041915893555, 8.99191951751709, 19.152435302734375, 34.27243423461914, -31.310897827148438, -20.683889389038086, -10.809209823608398, 14.062344551086426, 18.750089645385742, 13.959158897399902, -8.645380973815918, 23.788658142089844, 22.035282135009766, 18.044673919677734, -41.75737380981445, -41.31781768798828, 32.19172286987305, -12.388429641723633, 24.06170654296875, -16.11063003540039, -16.608327865600586, 35.45128631591797, -21.5465145111084, -41.050270080566406, -42.05463790893555, -0.6157612800598145, 23.858266830444336, 4.945340633392334, 23.73905372619629, -37.99441146850586, 15.15748405456543, 9.55198860168457, 14.732538223266602, -40.78012466430664, 16.501951217651367, 19.81125259399414, -38.802276611328125, 3.4446568489074707, -31.811925888061523, -11.924962997436523, -34.01521301269531, -11.77328109741211, 29.429662704467773, 23.282684326171875, 22.76887321472168, -13.897069931030273, 24.63739585876465, 24.547758102416992, -6.784965991973877, 3.5353200435638428, 29.946332931518555, 32.52227783203125, 14.524889945983887, 31.11566162109375, 6.500742435455322, 10.821308135986328, 24.635740280151367, 9.537750244140625, -17.91515350341797, 19.76047134399414, -41.76491165161133, -2.7922191619873047, -7.154927730560303, -22.69426918029785, 32.132354736328125, -19.798263549804688, -22.878387451171875, -1.3629356622695923, 15.9620943069458, 4.919292449951172, -19.76166343688965, 33.27949142456055, -37.418617248535156, -3.033407688140869, 25.00918197631836, 36.10431671142578, -11.827169418334961, 29.70917320251465, -30.47922134399414, -20.954191207885742, 12.5928316116333, 16.05235481262207, 35.00455093383789, 20.658048629760742, -13.705899238586426, -14.050765037536621, -14.206786155700684, -12.490314483642578, -39.77098846435547, 34.621368408203125, -1.2785608768463135, -15.189940452575684, 1.8724192380905151, -5.914083480834961, -15.96410083770752, -8.066412925720215, 17.20330810546875, -1.940897822380066, -22.14067268371582, -13.54328441619873, -6.960697174072266, -1.442679762840271, -17.10752296447754, 21.484874725341797, 26.125211715698242, 9.664216995239258, -42.02814865112305, 35.31327819824219, 16.39208984375, 18.59504508972168, -34.12205123901367, -40.42576599121094, -41.52042007446289, 34.008644104003906, 7.176661491394043, 16.850587844848633, -29.375213623046875, -7.718750476837158, 31.245471954345703, -3.4010841846466064, 8.111698150634766, 32.321468353271484, -32.772762298583984, -7.296995639801025, -17.48038101196289, -35.90983200073242, -9.048584938049316, 31.69009017944336, 13.938096046447754, -37.818260192871094, -11.163557052612305, 34.60731506347656, -4.933875560760498, 17.35405731201172, -21.62071418762207, -21.587984085083008, -2.0981905460357666, 24.754350662231445, 13.314946174621582, -31.041582107543945, 3.6371617317199707, -1.0978262424468994, 36.462642669677734, 23.4324893951416, 27.699182510375977, -13.913179397583008, -41.21064758300781, -15.399881362915039, 16.2537899017334, 19.833614349365234, -8.575786590576172, -19.841386795043945, 34.48988342285156, -20.53133773803711, 19.870676040649414, 32.118934631347656, -2.723710536956787, 22.04938316345215, -7.72926664352417, 1.873970627784729, -40.167564392089844, 3.8291873931884766, -32.44989776611328, 15.004900932312012, 17.3597412109375, 14.511397361755371, -39.21103286743164, 36.02815628051758, -9.423357009887695, -25.085813522338867, -34.54728317260742, -6.558016777038574, -37.112754821777344, -26.574220657348633, -36.531761169433594, 33.27244186401367, 7.190450191497803, 22.446924209594727, 16.5830078125, 2.8010201454162598, 33.05457305908203, 29.691181182861328, -41.19761657714844, -6.274199962615967, -28.96893882751465, 23.621461868286133, 12.337835311889648, -9.87059497833252, -33.4103889465332, -13.509648323059082, 1.9265937805175781, 34.01472091674805, 2.942348003387451, -6.067962169647217, 12.946046829223633, -19.032007217407227, 21.527111053466797, 16.587913513183594, -6.044271945953369, 4.195749282836914, -4.166802883148193, -38.27409362792969, -40.676902770996094, -30.03792381286621, 33.24101638793945, -13.414498329162598, 34.26243591308594, 5.034850120544434, 11.35783863067627, 19.286258697509766, -23.48755645751953, -7.0145158767700195, 24.38704490661621, 35.95271301269531, 33.504432678222656, 11.488479614257812, -37.8311767578125, 0.8079329133033752]}], {}, {\"linkText\": \"Export to plot.ly\", \"showLink\": true})});</script>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "reduce_dimensions(model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Conclusion\n",
+    "\n",
+    "In this tutorial we learned how to train word2vec models on your custom data and also how to evaluate it. Hope that you too will find this popular tool useful in your Machine Learning tasks!\n",
+    "\n",
+    "## Links\n",
+    "\n",
+    "\n",
+    "Full `word2vec` API docs [here](http://radimrehurek.com/gensim/models/word2vec.html); get [gensim](http://radimrehurek.com/gensim/) here. Original C toolkit and `word2vec` papers by Google [here](https://code.google.com/archive/p/word2vec/)."
+   ]
   }
  ],
  "metadata": {
@@ -1336,7 +6831,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.13"
+   "version": "2.7.12"
   }
  },
  "nbformat": 4,
diff --git a/docs/src/apiref.rst b/docs/src/apiref.rst
index c4587f14f9..d67d27a16a 100644
--- a/docs/src/apiref.rst
+++ b/docs/src/apiref.rst
@@ -88,6 +88,7 @@ Modules:
     summarization/commons
     summarization/graph
     summarization/keywords
+    summarization/mz_entropy
     summarization/pagerank_weighted
     summarization/summariser
     summarization/syntactic_unit
diff --git a/docs/src/models/rpmodel.rst b/docs/src/models/rpmodel.rst
index 47eba01262..91ef71872a 100644
--- a/docs/src/models/rpmodel.rst
+++ b/docs/src/models/rpmodel.rst
@@ -5,4 +5,6 @@
     :synopsis: Random Projections
     :members:
     :inherited-members:
-
+    :undoc-members:
+    :show-inheritance:
+    :special-members: __getitem__
diff --git a/docs/src/summarization/graph.rst b/docs/src/summarization/graph.rst
index 909b15cf5e..eb3588077d 100644
--- a/docs/src/summarization/graph.rst
+++ b/docs/src/summarization/graph.rst
@@ -1,8 +1,8 @@
-:mod:`summarization.graph` -- TextRank graph
-=========================================================
+:mod:`summarization.graph` -- Graph
+===================================
 
 .. automodule:: gensim.summarization.graph
-    :synopsis: TextRank graph
+    :synopsis: Graph
     :members:
     :inherited-members:
     :undoc-members:
diff --git a/docs/src/summarization/mz_entropy.rst b/docs/src/summarization/mz_entropy.rst
new file mode 100644
index 0000000000..31222ca6ab
--- /dev/null
+++ b/docs/src/summarization/mz_entropy.rst
@@ -0,0 +1,9 @@
+:mod:`summarization.mz_entropy` -- Keywords for the Montemurro and Zanette entropy algorithm
+============================================================================================
+
+.. automodule:: gensim.summarization.mz_entropy
+    :synopsis: Keywords for the Montemurro and Zanette entropy algorithm
+    :members:
+    :inherited-members:
+    :undoc-members:
+    :show-inheritance:
diff --git a/gensim/models/__init__.py b/gensim/models/__init__.py
index c25e8e7795..88e0abf28d 100644
--- a/gensim/models/__init__.py
+++ b/gensim/models/__init__.py
@@ -20,6 +20,7 @@
 from .atmodel import AuthorTopicModel  # noqa:F401
 from .ldaseqmodel import LdaSeqModel  # noqa:F401
 from .fasttext import FastText  # noqa:F401
+from .translation_matrix import TranslationMatrix, BackMappingTranslationMatrix  # noqa:F401
 
 from . import wrappers  # noqa:F401
 
diff --git a/gensim/models/fasttext.py b/gensim/models/fasttext.py
index d7e8598b8d..2210c4e0ed 100644
--- a/gensim/models/fasttext.py
+++ b/gensim/models/fasttext.py
@@ -188,7 +188,7 @@ def __init__(
             If you don't supply `sentences`, the model is left uninitialized -- use if you plan to initialize it
             in some other way.
         sg : int {1, 0}
-            Defines the training algorithm. If 1, CBOW is used, otherwise, skip-gram is employed.
+            Defines the training algorithm. If 1, skip-gram is used, otherwise, CBOW is employed.
         size : int
             Dimensionality of the feature vectors.
         window : int
diff --git a/gensim/models/rpmodel.py b/gensim/models/rpmodel.py
index 0c8f7c8b26..0826a7c359 100644
--- a/gensim/models/rpmodel.py
+++ b/gensim/models/rpmodel.py
@@ -5,6 +5,35 @@
 # Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html
 
 
+"""Random Projections (also known as Random Indexing).
+
+For theoretical background on Random Projections, see [1]_.
+
+
+Examples
+--------
+>>> from gensim.models import RpModel
+>>> from gensim.corpora import Dictionary
+>>> from gensim.test.utils import common_texts, temporary_file
+>>>
+>>> dictionary = Dictionary(common_texts)  # fit dictionary
+>>> corpus = [dictionary.doc2bow(text) for text in common_texts]  # convert texts to BoW format
+>>>
+>>> model = RpModel(corpus, id2word=dictionary)  # fit model
+>>> result = model[corpus[3]]  # apply model to document, result is vector in BoW format
+>>>
+>>> with temporary_file("model_file") as fname:
+...     model.save(fname)  # save model to file
+...     loaded_model = RpModel.load(fname)  # load model
+
+
+References
+----------
+.. [1] Kanerva et al., 2000, Random indexing of text samples for Latent Semantic Analysis,
+       https://cloudfront.escholarship.org/dist/prd/content/qt5644k0w6/qt5644k0w6.pdf
+
+"""
+
 import logging
 
 import numpy as np
@@ -16,30 +45,21 @@
 
 
 class RpModel(interfaces.TransformationABC):
-    """
-    Objects of this class allow building and maintaining a model for Random Projections
-    (also known as Random Indexing). For theoretical background on RP, see:
-
-      Kanerva et al.: "Random indexing of text samples for Latent Semantic Analysis."
 
-    The main methods are:
+    def __init__(self, corpus, id2word=None, num_topics=300):
+        """
 
-    1. constructor, which creates the random projection matrix
-    2. the [] method, which transforms a simple count representation into the TfIdf
-       space.
+        Parameters
+        ----------
+        corpus : iterable of iterable of (int, int)
+            Input corpus.
 
-    >>> rp = RpModel(corpus)
-    >>> print(rp[some_doc])
-    >>> rp.save('/tmp/foo.rp_model')
+        id2word : {dict of (int, str), :class:`~gensim.corpora.dictionary.Dictionary`}, optional
+            Mapping `token_id` -> `token`, will be determine from corpus if `id2word == None`.
 
-    Model persistency is achieved via its load/save methods.
-    """
+        num_topics : int, optional
+            Number of topics.
 
-    def __init__(self, corpus, id2word=None, num_topics=300):
-        """
-        `id2word` is a mapping from word ids (integers) to words (strings). It is
-        used to determine the vocabulary size, as well as for debugging and topic
-        printing. If not set, it will be determined from the corpus.
         """
         self.id2word = id2word
         self.num_topics = num_topics
@@ -50,8 +70,13 @@ def __str__(self):
         return "RpModel(num_terms=%s, num_topics=%s)" % (self.num_terms, self.num_topics)
 
     def initialize(self, corpus):
-        """
-        Initialize the random projection matrix.
+        """Initialize the random projection matrix.
+
+        Parameters
+        ----------
+        corpus : iterable of iterable of (int, int)
+          Input corpus.
+
         """
         if self.id2word is None:
             logger.info("no word id mapping provided; initializing from corpus, assuming identity")
@@ -73,8 +98,32 @@ def initialize(self, corpus):
         # are smarter and this is no longer needed?
 
     def __getitem__(self, bow):
-        """
-        Return RP representation of the input vector and/or corpus.
+        """Get random-projection representation of the input vector or corpus.
+
+        Parameters
+        ----------
+        bow : {list of (int, int), iterable of list of (int, int)}
+            Input document or corpus.
+
+        Returns
+        -------
+        list of (int, float)
+            if `bow` is document OR
+        :class:`~gensim.interfaces.TransformedCorpus`
+            if `bow` is corpus.
+
+        Examples
+        ----------
+        >>> from gensim.models import RpModel
+        >>> from gensim.corpora import Dictionary
+        >>> from gensim.test.utils import common_texts
+        >>>
+        >>> dictionary = Dictionary(common_texts)  # fit dictionary
+        >>> corpus = [dictionary.doc2bow(text) for text in common_texts]  # convert texts to BoW format
+        >>>
+        >>> model = RpModel(corpus, id2word=dictionary)  # fit model
+        >>> result = model[corpus[0]]  # apply model to document, result is vector in BoW format, i.e. [(1, 0.3), ... ]
+
         """
         # if the input vector is in fact a corpus, return a transformed corpus as result
         is_corpus, bow = utils.is_corpus(bow)
@@ -96,5 +145,13 @@ def __getitem__(self, bow):
         ]
 
     def __setstate__(self, state):
+        """Sets the internal state and updates freshly_loaded to True, called when unpicked.
+
+        Parameters
+        ----------
+        state : dict
+           State of the class.
+
+        """
         self.__dict__ = state
         self.freshly_loaded = True
diff --git a/gensim/models/translation_matrix.py b/gensim/models/translation_matrix.py
index 78233136d2..1dd7cd6b25 100644
--- a/gensim/models/translation_matrix.py
+++ b/gensim/models/translation_matrix.py
@@ -1,60 +1,105 @@
 #!/usr/bin/env python
 # encoding: utf-8
-import warnings
-import numpy as np
 
-from collections import OrderedDict
-from gensim import utils
-from six import string_types
-
-
-"""
-Produce translation matrix to translate the word from one language to another language, using either
-standard nearest neighbour method or globally corrected neighbour retrieval method [1].
+"""Produce translation matrix to translate the word from one language to another language, using either
+standard nearest neighbour method or globally corrected neighbour retrieval method [1]_.
 
 This method can be used to augment the existing phrase tables with more candidate translations, or
-filter out errors from the translation tables and known dictionaries [2]. What's more, It also work
+filter out errors from the translation tables and known dictionaries [2]_. What's more, It also work
 for any two sets of named-vectors where there are some paired-guideposts to learn the transformation.
 
-Initialize a model with e.g.::
+Examples
+--------
+**How to make translation between two set of word-vectors**
+
+Initialize a word-vector models
+
+>>> from gensim.models import KeyedVectors
+>>> from gensim.test.utils import datapath, temporary_file
+>>> from gensim.models import TranslationMatrix
+>>>
+>>> model_en = KeyedVectors.load_word2vec_format(datapath("EN.1-10.cbow1_wind5_hs0_neg10_size300_smpl1e-05.txt"))
+>>> model_it = KeyedVectors.load_word2vec_format(datapath("IT.1-10.cbow1_wind5_hs0_neg10_size300_smpl1e-05.txt"))
+
+Define word pairs (that will be used for construction of translation matrix
+
+>>> word_pairs = [
+...     ("one", "uno"), ("two", "due"), ("three", "tre"), ("four", "quattro"), ("five", "cinque"),
+...     ("seven", "sette"), ("eight", "otto"),
+...     ("dog", "cane"), ("pig", "maiale"), ("fish", "cavallo"), ("birds", "uccelli"),
+...     ("apple", "mela"), ("orange", "arancione"), ("grape", "acino"), ("banana", "banana")
+... ]
+
+Fit :class:`~gensim.models.translation_matrix.TranslationMatrix`
+
+>>> trans_model = TranslationMatrix(model_en, model_it, word_pairs=word_pairs)
+
+Apply model (translate words "dog" and "one")
+
+>>> trans_model.translate(["dog", "one"], topn=3)
+OrderedDict([('dog', [u'cane', u'gatto', u'cavallo']), ('one', [u'uno', u'due', u'tre'])])
+
 
-    >>> transmat = TranslationMatrix(word_pair, source_word_vec, target_word_vec)
+Save / load model
 
-Train a model with e.g.::
+>>> with temporary_file("model_file") as fname:
+...     trans_model.save(fname)  # save model to file
+...     loaded_trans_model = TranslationMatrix.load(fname)  # load model
 
-    >>> transmat.train(word_pair)
 
-Persist a model to disk with::
+**How to make translation between two :class:`~gensim.models.doc2vec.Doc2Vec` models**
 
-    >>> transmat.save(fname)
-    >>> transmat  = TranslationMatrix.load(fname)
+Prepare data and models
 
-Translate the source words to target words, for example
+>>> from gensim.test.utils import datapath
+>>> from gensim.test.test_translation_matrix import read_sentiment_docs
+>>> from gensim.models import Doc2Vec, BackMappingTranslationMatrix
+>>>
+>>> data = read_sentiment_docs(datapath("alldata-id-10.txt"))[:5]
+>>> src_model = Doc2Vec.load(datapath("small_tag_doc_5_iter50"))
+>>> dst_model = Doc2Vec.load(datapath("large_tag_doc_10_iter50"))
 
-    >>> transmat.translate(["one", "two", "three"], topn=3)
+Train backward translation
 
+>>> model_trans = BackMappingTranslationMatrix(data, src_model, dst_model)
+>>> trans_matrix = model_trans.train(data)
+
+
+Apply model
+
+>>> result = model_trans.infer_vector(dst_model.docvecs[data[3].tags])
+
+
+References
+----------
 .. [1] Dinu, Georgiana, Angeliki Lazaridou, and Marco Baroni. "Improving zero-shot learning by mitigating the
-        hubness problem." arXiv preprint arXiv:1412.6568 (2014).
+       hubness problem", https://arxiv.org/abs/1412.6568
 .. [2] Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean.
-       Distributed Representations of Words and Phrases and their Compositionality.
-       In Proceedings of NIPS, 2013.
+       "Distributed Representations of Words and Phrases and their Compositionality", https://arxiv.org/abs/1310.4546
+
 """
 
+import warnings
+import numpy as np
+
+from collections import OrderedDict
+from gensim import utils
+from six import string_types
+
 
 class Space(object):
-    """
-    An auxiliary class for storing the the words space
+    """An auxiliary class for storing the the words space."""
 
-    Attributes:
-    `mat` (ndarray): each row is the word vector of the lexicon
-    `index2word` (list): a list of words in the `Space` object
-    `word2index` (dict): map the word to index
-    """
     def __init__(self, matrix, index2word):
         """
-        `matrix`: N * length_of_word_vec, which store the word's vector
-        `index2word`: a list of words in the `Space` object
-        `word2index`: a dict which for word indexing
+
+        Parameters
+        ----------
+        matrix : iterable of numpy.ndarray
+            Matrix that contains word-vectors.
+        index2word : list of str
+            Words which correspond to the `matrix`.
+
         """
         self.mat = matrix
         self.index2word = index2word
@@ -66,14 +111,20 @@ def __init__(self, matrix, index2word):
 
     @classmethod
     def build(cls, lang_vec, lexicon=None):
-        """
-        Construct a space class for the lexicon, if it's provided.
-        Args:
-            `lang_vec`: word2vec model that extract word vector for lexicon
-            `lexicon`: the default is None, if it is not provided, the lexicon is all the lang_vec's word,
-            i.e. lang_vec.vocab.keys()
-        Returns:
-            `Space` object for the lexicon
+        """Construct a space class for the lexicon, if it's provided.
+
+        Parameters
+        ----------
+        lang_vec : :class:`~gensim.models.keyedvectors.KeyedVectors`
+            Model from which the vectors will be extracted.
+        lexicon : list of str, optional
+            Words which contains in the `lang_vec`, if `lexicon = None`, the lexicon is all the lang_vec's word.
+
+        Returns
+        -------
+        :class:`~gensim.models.translation_matrix.Space`
+            Object that stored word-vectors
+
         """
         # `words` to store all the word that
         # `mat` to store all the word vector for the word in 'words' list
@@ -93,41 +144,58 @@ def build(cls, lang_vec, lexicon=None):
         return Space(mat, words)
 
     def normalize(self):
-        """ Normalize the word vector's matrix """
+        """Normalize the word vector's matrix."""
         self.mat = self.mat / np.sqrt(np.sum(np.multiply(self.mat, self.mat), axis=1, keepdims=True))
 
 
 class TranslationMatrix(utils.SaveLoad):
-    """
-    Objects of this class realize the translation matrix which map the source language
-    to the target language.
+    """Objects of this class realize the translation matrix which map the source language to the target language.
     The main methods are:
 
-    1. constructor,
-    2. the `train` method, which initialize everything needed to build a translation matrix
-    3. the `translate` method, which given new word and its vector representation.
-
     We map it to the other language space by computing z = Wx, then return the
     word whose representation is close to z.
 
-    The details use seen the notebook (translation_matrix.ipynb)
-
-    >>> transmat = TranslationMatrix(source_lang_vec, target_lang_vec, word_pair)
-    >>> transmat.train(word_pair)
-    >>> translated_word = transmat.translate(words, topn=3)
+    The details use seen the notebook [3]_
+
+    Examples
+    --------
+    >>> from gensim.models import KeyedVectors
+    >>> from gensim.test.utils import datapath, temporary_file
+    >>>
+    >>> model_en = KeyedVectors.load_word2vec_format(datapath("EN.1-10.cbow1_wind5_hs0_neg10_size300_smpl1e-05.txt"))
+    >>> model_it = KeyedVectors.load_word2vec_format(datapath("IT.1-10.cbow1_wind5_hs0_neg10_size300_smpl1e-05.txt"))
+    >>>
+    >>> word_pairs = [
+    ...     ("one", "uno"), ("two", "due"), ("three", "tre"), ("four", "quattro"), ("five", "cinque"),
+    ...     ("seven", "sette"), ("eight", "otto"),
+    ...     ("dog", "cane"), ("pig", "maiale"), ("fish", "cavallo"), ("birds", "uccelli"),
+    ...     ("apple", "mela"), ("orange", "arancione"), ("grape", "acino"), ("banana", "banana")
+    ... ]
+    >>>
+    >>> trans_model = TranslationMatrix(model_en, model_it)
+    >>> trans_model.train(word_pairs)
+    >>> trans_model.translate(["dog", "one"], topn=3)
+    OrderedDict([('dog', [u'cane', u'gatto', u'cavallo']), ('one', [u'uno', u'due', u'tre'])])
+
+
+    References
+    ----------
+    .. [3] https://github.com/RaRe-Technologies/gensim/blob/3.2.0/docs/notebooks/translation_matrix.ipynb
 
     """
     def __init__(self, source_lang_vec, target_lang_vec, word_pairs=None, random_state=None):
         """
-        Initialize the model from a list pair of `word_pair`. Each word_pair is tupe
-         with source language word and target language word.
+        Parameters
+        ----------
+        source_lang_vec : :class:`~gensim.models.keyedvectors.KeyedVectors`
+            Word vectors for source language.
+        target_lang_vec : :class:`~gensim.models.keyedvectors.KeyedVectors`
+            Word vectors for target language.
+        word_pairs : list of (str, str), optional
+            Pairs of words that will be used for training.
+        random_state : {None, int, array_like}, optional
+            Seed for random state.
 
-        Examples: [("one", "uno"), ("two", "due")]
-
-        Args:
-            `word_pair` (list): a list pair of words
-            `source_lang_vec` (KeyedVectors): a set of word vector of source language
-            `target_lang_vec` (KeyedVectors): a set of word vector of target language
         """
 
         self.source_word = None
@@ -146,14 +214,13 @@ def __init__(self, source_lang_vec, target_lang_vec, word_pairs=None, random_sta
             self.train(word_pairs)
 
     def train(self, word_pairs):
-        """
-        Build the translation matrix that mapping from source space to target space.
+        """Build the translation matrix that mapping from source space to target space.
 
-        Args:
-            `word_pairs` (list): a list pair of words
+        Parameters
+        ----------
+        word_pairs : list of (str, str), optional
+            Pairs of words that will be used for training.
 
-        Returns:
-            `translation matrix` that mapping from the source language to target language
         """
         self.source_word, self.target_word = zip(*word_pairs)
 
@@ -169,50 +236,50 @@ def train(self, word_pairs):
         self.translation_matrix = np.linalg.lstsq(m1, m2, -1)[0]
 
     def save(self, *args, **kwargs):
-        """
-        Save the model to file but ignoring the souce_space and target_space
-        """
+        """Save the model to file but ignoring the `source_space` and `target_space`"""
         kwargs['ignore'] = kwargs.get('ignore', ['source_space', 'target_space'])
-
         super(TranslationMatrix, self).save(*args, **kwargs)
 
-    @classmethod
-    def load(cls, *args, **kwargs):
-        """ Load the pre-trained translation matrix model"""
-        model = super(TranslationMatrix, cls).load(*args, **kwargs)
-        return model
-
     def apply_transmat(self, words_space):
-        """
-        Map the source word vector to the target word vector using translation matrix
-        Args:
-            `words_space`: the `Space` object that constructed for those words to be translate
+        """Map the source word vector to the target word vector using translation matrix.
+
+        Parameters
+        ----------
+        words_space : :class:`~gensim.models.translation_matrix.Space`
+            Object that constructed for those words to be translate.
+
+        Returns
+        -------
+        :class:`~gensim.models.translation_matrix.Space`
+            Object that constructed for those mapped words.
 
-        Returns:
-            A `Space` object that constructed for those mapped words
         """
         return Space(np.dot(words_space.mat, self.translation_matrix), words_space.index2word)
 
     def translate(self, source_words, topn=5, gc=0, sample_num=None, source_lang_vec=None, target_lang_vec=None):
-        """
-        Translate the word from the source language to the target language, and return the topn
-        most similar words.
-        Args:
-            `source_words`(str/list): single word or a list of words to be translated
-            `topn`: return the top N similar words. By default (`topn=5`)
-            `gc`: defines the training algorithm. By default (`gc=0`), use standard NN retrieval.
-            Otherwise use globally corrected neighbour retrieval method(as described in[1]).
-            `sample_num`:  an int parameter that specify the number of word to sample from the source lexicon.
-            if `gc=1`, then `sample_num` must be provided.
-            `source_lang_vec`: you can specify the source language vector for translation, the default is to use
-            the model's source language vector.
-            `target_lang_vec`: you can specify the target language vector for retrieving the most similar word,
-            the default is to use the model's target language vector.
-        Returns:
-            A OrderedDict object, each item is (word : `topn` translated words)
-
-        [1] Dinu, Georgiana, Angeliki Lazaridou, and Marco Baroni. "Improving zero-shot learning by mitigating the
-        hubness problem." arXiv preprint arXiv:1412.6568 (2014).
+        """Translate the word from the source language to the target language.
+
+        Parameters
+        ----------
+        source_words : {str, list of str}
+            Single word or a list of words to be translated
+        topn : int, optional
+            Number of words than will be returned as translation for each `source_words`
+        gc : int, optional
+            Define translation algorithm, if `gc == 0` - use standard NN retrieval,
+            otherwise, use globally corrected neighbour retrieval method (as described in [1]_).
+        sample_num : int, optional
+            Number of word to sample from the source lexicon, if `gc == 1`, then `sample_num` **must** be provided.
+        source_lang_vec : :class:`~gensim.models.keyedvectors.KeyedVectors`, optional
+            New source language vectors for translation, by default, used the model's source language vector.
+        target_lang_vec : :class:`~gensim.models.keyedvectors.KeyedVectors`, optional
+            New target language vectors for translation, by default, used the model's target language vector.
+
+        Returns
+        -------
+        :class:`collections.OrderedDict`
+            Ordered dict where each item is `word`: [`translated_word_1`, `translated_word_2`, ...]
+
         """
 
         if isinstance(source_words, string_types):
@@ -280,38 +347,45 @@ def translate(self, source_words, topn=5, gc=0, sample_num=None, source_lang_vec
 
 
 class BackMappingTranslationMatrix(utils.SaveLoad):
-    """
-        Objects of this class realize the BackMapping translation matrix which map the
-        source model's document vector to the target model's document vector(old model).
-        The main methods are:
-
-        1. constructor, initializing
-        2. the `train` method, which build a translation matrix
-        3. the `infer_vector` method, which given the target model's document vector
-
-        We map it to the other language space by computing z = Wx, then return the
-        word whose representation is close to z.
+    """Realize the BackMapping translation matrix which map the source model's document vector
+    to the target model's document vector(old model).
 
-        the details use seen the notebook (translation matrix revist.ipynb)
+    We map it to the other language space by computing z = Wx, then return the
+    word whose representation is close to z.
 
-        >>> transmat = BackMappingTranslationMatrix(tagged, source_lang_vec, target_lang_vec)
-        >>> transmat.train(word_pair)
-        >>> infered_vec = transmat.infer_vector(tagged_doc)
+    the details use seen the notebook [3]_.
+
+    Examples
+    --------
+    >>> from gensim.test.utils import datapath
+    >>> from gensim.test.test_translation_matrix import read_sentiment_docs
+    >>> from gensim.models import Doc2Vec, BackMappingTranslationMatrix
+    >>>
+    >>> data = read_sentiment_docs(datapath("alldata-id-10.txt"))[:5]
+    >>> src_model = Doc2Vec.load(datapath("small_tag_doc_5_iter50"))
+    >>> dst_model = Doc2Vec.load(datapath("large_tag_doc_10_iter50"))
+    >>>
+    >>> model_trans = BackMappingTranslationMatrix(data, src_model, dst_model)
+    >>> trans_matrix = model_trans.train(data)
+    >>>
+    >>> result = model_trans.infer_vector(dst_model.docvecs[data[3].tags])
 
-        """
+    """
     def __init__(self, tagged_docs, source_lang_vec, target_lang_vec, random_state=None):
         """
-        Initialize the model from a list of `tagged_docs`. Each word_pair is tupe
-         with source language word and target language word.
 
-        Examples: [("one", "uno"), ("two", "due")]
+        Parameters
+        ----------
+        tagged_docs : list of :class:`~gensim.models.doc2vec.TaggedDocument`, optional
+            Documents that will be used for training
+        source_lang_vec : :class:`~gensim.models.doc2vec.Doc2Vec`
+            Source Doc2Vec model.
+        target_lang_vec : :class:`~gensim.models.doc2vec.Doc2Vec`
+            Target Doc2Vec model.
+        random_state : {None, int, array_like}, optional
+            Seed for random state.
 
-        Args:
-            `tagged_docs` (list): a list of tagged document
-            `source_lang_vec` (Doc2vec): provide the document vector
-            `target_lang_vec` (Doc2vec): provide the document vector
         """
-
         self.tagged_docs = tagged_docs
         self.source_lang_vec = source_lang_vec
         self.target_lang_vec = target_lang_vec
@@ -320,13 +394,19 @@ def __init__(self, tagged_docs, source_lang_vec, target_lang_vec, random_state=N
         self.translation_matrix = None
 
     def train(self, tagged_docs):
-        """
-        Build the translation matrix that mapping from the source model's vector to target model's vector
+        """Build the translation matrix that mapping from the source model's vector to target model's vector
 
-        Returns:
-            `translation matrix` that mapping from the source model's vector to target model's vector
-        """
+        Parameters
+        ----------
+        tagged_docs : list of :class:`~gensim.models.doc2vec.TaggedDocument`, optional
+            THIS ARGUMENT WILL BE IGNORED.
+
+        Returns
+        -------
+        numpy.ndarray
+            Translation matrix that mapping from the source model's vector to target model's vector.
 
+        """
         m1 = [self.source_lang_vec.docvecs[item.tags].flatten() for item in self.tagged_docs]
         m2 = [self.target_lang_vec.docvecs[item.tags].flatten() for item in self.tagged_docs]
 
@@ -334,11 +414,17 @@ def train(self, tagged_docs):
         return self.translation_matrix
 
     def infer_vector(self, target_doc_vec):
-        """
-        Translate the target model's document vector to the source model's document vector
+        """Translate the target model's document vector to the source model's document vector
+
+        Parameters
+        ----------
+        target_doc_vec : numpy.ndarray
+            Document vector
+
+        Returns
+        -------
+        numpy.ndarray
+            Vector `target_doc_vec` in the source model.
 
-        Returns:
-            `infered_vec` the tagged_doc's document vector in the source model
         """
-        infered_vec = np.dot(target_doc_vec, self.translation_matrix)
-        return infered_vec
+        return np.dot(target_doc_vec, self.translation_matrix)
diff --git a/gensim/parsing/preprocessing.py b/gensim/parsing/preprocessing.py
index f0cf22a6e8..cc15b4665b 100644
--- a/gensim/parsing/preprocessing.py
+++ b/gensim/parsing/preprocessing.py
@@ -363,7 +363,7 @@ def preprocess_documents(docs):
 
     Returns
     -------
-    list of (list of str)
+    list of list of str
         Processed documents split by whitespace.
 
     Examples
diff --git a/gensim/similarities/index.py b/gensim/similarities/index.py
index e9323f6998..10a79dc8b7 100644
--- a/gensim/similarities/index.py
+++ b/gensim/similarities/index.py
@@ -3,6 +3,33 @@
 #
 # Copyright (C) 2013 Radim Rehurek <me@radimrehurek.com>
 # Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html
+
+"""
+Intro
+-----
+This module contains integration Annoy with :class:`~gensim.models.word2vec.Word2Vec`,
+:class:`~gensim.models.doc2vec.Doc2Vec` and :class:`~gensim.models.keyedvectors.KeyedVectors`.
+
+
+What is Annoy
+-------------
+Annoy (Approximate Nearest Neighbors Oh Yeah) is a C++ library with Python bindings to search for points in space
+that are close to a given query point. It also creates large read-only file-based data structures that are mmapped
+into memory so that many processes may share the same data.
+
+
+How it works
+------------
+Using `random projections <https://en.wikipedia.org/wiki/Locality-sensitive_hashing#Random_projection>`_
+and by building up a tree. At every intermediate node in the tree, a random hyperplane is chosen,
+which divides the space into two subspaces. This hyperplane is chosen by sampling two points from the subset
+and taking the hyperplane equidistant from them.
+
+More information about Annoy: `github repository <https://github.com/spotify/annoy>`_,
+`author in twitter <https://twitter.com/fulhack>`_
+and `annoy-user maillist <https://groups.google.com/forum/#!forum/annoy-user>`_.
+
+"""
 import os
 
 from smart_open import smart_open
@@ -23,8 +50,34 @@
 
 
 class AnnoyIndexer(object):
+    """This class allows to use `Annoy <https://github.com/spotify/annoy>`_ as indexer for ``most_similar`` method
+    from :class:`~gensim.models.word2vec.Word2Vec`, :class:`~gensim.models.doc2vec.Doc2Vec`
+    and :class:`~gensim.models.keyedvectors.KeyedVectors` classes.
+    """
 
     def __init__(self, model=None, num_trees=None):
+        """
+        Parameters
+        ----------
+        model : :class:`~gensim.models.word2vec.Word2Vec`, :class:`~gensim.models.doc2vec.Doc2Vec` or
+                :class:`~gensim.models.keyedvectors.KeyedVectors`, optional
+            Model, that will be used as source for index.
+        num_trees : int, optional
+            Number of trees for Annoy indexer.
+
+        Examples
+        --------
+        >>> from gensim.similarities.index import AnnoyIndexer
+        >>> from gensim.models import Word2Vec
+        >>>
+        >>> sentences = [['cute', 'cat', 'say', 'meow'], ['cute', 'dog', 'say', 'woof']]
+        >>> model = Word2Vec(sentences, min_count=1, seed=1)
+        >>>
+        >>> indexer = AnnoyIndexer(model, 2)
+        >>> model.most_similar("cat", topn=2, indexer=indexer)
+        [('cat', 1.0), ('dog', 0.32011348009109497)]
+
+        """
         self.index = None
         self.labels = None
         self.model = model
@@ -41,6 +94,20 @@ def __init__(self, model=None, num_trees=None):
                 raise ValueError("Only a Word2Vec, Doc2Vec or KeyedVectors instance can be used")
 
     def save(self, fname, protocol=2):
+        """Save AnnoyIndexer instance.
+
+        Parameters
+        ----------
+        fname : str
+            Path to output file, will produce 2 files: `fname` - parameters and `fname`.d - :class:`~annoy.AnnoyIndex`.
+        protocol : int, optional
+            Protocol for pickle.
+
+        Notes
+        -----
+        This method save **only** index (**model isn't preserved**).
+
+        """
         fname_dict = fname + '.d'
         self.index.save(fname)
         d = {'f': self.model.vector_size, 'num_trees': self.num_trees, 'labels': self.labels}
@@ -48,6 +115,31 @@ def save(self, fname, protocol=2):
             _pickle.dump(d, fout, protocol=protocol)
 
     def load(self, fname):
+        """Load AnnoyIndexer instance
+
+        Parameters
+        ----------
+        fname : str
+            Path to dump with AnnoyIndexer.
+
+        Examples
+        --------
+        >>> from gensim.similarities.index import AnnoyIndexer
+        >>> from gensim.models import Word2Vec
+        >>> from tempfile import mkstemp
+        >>>
+        >>> sentences = [['cute', 'cat', 'say', 'meow'], ['cute', 'dog', 'say', 'woof']]
+        >>> model = Word2Vec(sentences, min_count=1, seed=1, iter=10)
+        >>>
+        >>> indexer = AnnoyIndexer(model, 2)
+        >>> _, temp_fn = mkstemp()
+        >>> indexer.save(temp_fn)
+        >>>
+        >>> new_indexer = AnnoyIndexer()
+        >>> new_indexer.load(temp_fn)
+        >>> new_indexer.model = model
+
+        """
         fname_dict = fname + '.d'
         if not (os.path.exists(fname) and os.path.exists(fname_dict)):
             raise IOError(
@@ -62,13 +154,13 @@ def load(self, fname):
             self.labels = d['labels']
 
     def build_from_word2vec(self):
-        """Build an Annoy index using word vectors from a Word2Vec model"""
+        """Build an Annoy index using word vectors from a Word2Vec model."""
 
         self.model.init_sims()
         return self._build_from_model(self.model.wv.syn0norm, self.model.wv.index2word, self.model.vector_size)
 
     def build_from_doc2vec(self):
-        """Build an Annoy index using document vectors from a Doc2Vec model"""
+        """Build an Annoy index using document vectors from a Doc2Vec model."""
 
         docvecs = self.model.docvecs
         docvecs.init_sims()
@@ -76,7 +168,7 @@ def build_from_doc2vec(self):
         return self._build_from_model(docvecs.doctag_syn0norm, labels, self.model.vector_size)
 
     def build_from_keyedvectors(self):
-        """Build an Annoy index using word vectors from a KeyedVectors model"""
+        """Build an Annoy index using word vectors from a KeyedVectors model."""
 
         self.model.init_sims()
         return self._build_from_model(self.model.syn0norm, self.model.index2word, self.model.vector_size)
@@ -92,7 +184,21 @@ def _build_from_model(self, vectors, labels, num_features):
         self.labels = labels
 
     def most_similar(self, vector, num_neighbors):
-        """Find the top-N most similar items"""
+        """Find the approximate `num_neighbors` most similar items.
+
+        Parameters
+        ----------
+        vector : numpy.array
+            Vector for word/document.
+        num_neighbors : int
+            Number of most similar items
+
+        Returns
+        -------
+        list of (str, float)
+            List of most similar items in format [(`item`, `cosine_distance`), ... ]
+
+        """
 
         ids, distances = self.index.get_nns_by_vector(
             vector, num_neighbors, include_distances=True)
diff --git a/gensim/summarization/bm25.py b/gensim/summarization/bm25.py
index bd4d70911a..ec484949cf 100644
--- a/gensim/summarization/bm25.py
+++ b/gensim/summarization/bm25.py
@@ -3,20 +3,75 @@
 #
 # Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html
 
+"""This module contains function of computing rank scores for documents in
+corpus and helper class `BM25` used in calculations. Original alhorithm
+descibed in [1]_, also you may check Wikipedia page [2]_.
+
+
+.. [1] Robertson, Stephen; Zaragoza, Hugo (2009).  The Probabilistic Relevance Framework: BM25 and Beyond,
+       http://www.staff.city.ac.uk/~sb317/papers/foundations_bm25_review.pdf
+.. [2] Okapi BM25 on Wikipedia, https://en.wikipedia.org/wiki/Okapi_BM25
+
+
+
+Examples
+--------
+>>> from gensim.summarization.bm25 import get_bm25_weights
+>>> corpus = [
+...     ["black", "cat", "white", "cat"],
+...     ["cat", "outer", "space"],
+...     ["wag", "dog"]
+... ]
+>>> result = get_bm25_weights(corpus)
+
+
+Data:
+-----
+.. data:: PARAM_K1 - Free smoothing parameter for BM25.
+.. data:: PARAM_B - Free smoothing parameter for BM25.
+.. data:: EPSILON - Constant used for negative idf of document in corpus.
+
+"""
+
+
 import math
 from six import iteritems
 from six.moves import xrange
 
 
-# BM25 parameters.
 PARAM_K1 = 1.5
 PARAM_B = 0.75
 EPSILON = 0.25
 
 
 class BM25(object):
+    """Implementation of Best Matching 25 ranking function.
+
+    Attributes
+    ----------
+    corpus_size : int
+        Size of corpus (number of documents).
+    avgdl : float
+        Average length of document in `corpus`.
+    corpus : list of list of str
+        Corpus of documents.
+    f : list of dicts of int
+        Dictionary with terms frequencies for each document in `corpus`. Words used as keys and frequencies as values.
+    df : dict
+        Dictionary with terms frequencies for whole `corpus`. Words used as keys and frequencies as values.
+    idf : dict
+        Dictionary with inversed terms frequencies for whole `corpus`. Words used as keys and frequencies as values.
+
+    """
 
     def __init__(self, corpus):
+        """
+        Parameters
+        ----------
+        corpus : list of list of str
+            Given corpus.
+
+        """
         self.corpus_size = len(corpus)
         self.avgdl = sum(float(len(x)) for x in corpus) / self.corpus_size
         self.corpus = corpus
@@ -26,6 +81,7 @@ def __init__(self, corpus):
         self.initialize()
 
     def initialize(self):
+        """Calculates frequencies of terms in documents and in corpus. Also computes inverse document frequencies."""
         for document in self.corpus:
             frequencies = {}
             for word in document:
@@ -43,6 +99,23 @@ def initialize(self):
             self.idf[word] = math.log(self.corpus_size - freq + 0.5) - math.log(freq + 0.5)
 
     def get_score(self, document, index, average_idf):
+        """Computes BM25 score of given `document` in relation to item of corpus selected by `index`.
+
+        Parameters
+        ----------
+        document : list of str
+            Document to be scored.
+        index : int
+            Index of document in corpus selected to score with `document`.
+        average_idf : float
+            Average idf in corpus.
+
+        Returns
+        -------
+        float
+            BM25 score.
+
+        """
         score = 0
         for word in document:
             if word not in self.f[index]:
@@ -53,6 +126,22 @@ def get_score(self, document, index, average_idf):
         return score
 
     def get_scores(self, document, average_idf):
+        """Computes and returns BM25 scores of given `document` in relation to
+        every item in corpus.
+
+        Parameters
+        ----------
+        document : list of str
+            Document to be scored.
+        average_idf : float
+            Average idf in corpus.
+
+        Returns
+        -------
+        list of float
+            BM25 scores.
+
+        """
         scores = []
         for index in xrange(self.corpus_size):
             score = self.get_score(document, index, average_idf)
@@ -61,6 +150,30 @@ def get_scores(self, document, average_idf):
 
 
 def get_bm25_weights(corpus):
+    """Returns BM25 scores (weights) of documents in corpus.
+    Each document has to be weighted with every document in given corpus.
+
+    Parameters
+    ----------
+    corpus : list of list of str
+        Corpus of documents.
+
+    Returns
+    -------
+    list of list of float
+        BM25 scores.
+
+    Examples
+    --------
+    >>> from gensim.summarization.bm25 import get_bm25_weights
+    >>> corpus = [
+    ...     ["black", "cat", "white", "cat"],
+    ...     ["cat", "outer", "space"],
+    ...     ["wag", "dog"]
+    ... ]
+    >>> result = get_bm25_weights(corpus)
+
+    """
     bm25 = BM25(corpus)
     average_idf = sum(float(val) for val in bm25.idf.values()) / len(bm25.idf)
 
diff --git a/gensim/summarization/commons.py b/gensim/summarization/commons.py
index 1c467098f9..f1a2264e46 100644
--- a/gensim/summarization/commons.py
+++ b/gensim/summarization/commons.py
@@ -3,10 +3,45 @@
 #
 # Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html
 
+"""This module provides functions of creating graph from sequence of values and removing of unreachable nodes.
+
+
+Examples
+--------
+
+Create simple graph and add edges. Let's take a look at nodes.
+
+>>> gg = build_graph(['Felidae', 'Lion', 'Tiger', 'Wolf'])
+>>> gg.add_edge(("Felidae", "Lion"))
+>>> gg.add_edge(("Felidae", "Tiger"))
+>>> sorted(gg.nodes())
+['Felidae', 'Lion', 'Tiger', 'Wolf']
+
+Remove nodes with no edges.
+
+>>> remove_unreachable_nodes(gg)
+>>> sorted(gg.nodes())
+['Felidae', 'Lion', 'Tiger']
+
+"""
+
 from gensim.summarization.graph import Graph
 
 
 def build_graph(sequence):
+    """Creates and returns undirected graph with given sequence of values.
+
+    Parameters
+    ----------
+    sequence : list of hashable
+        Sequence of values.
+
+    Returns
+    -------
+    :class:`~gensim.summarization.graph.Graph`
+        Created graph.
+
+    """
     graph = Graph()
     for item in sequence:
         if not graph.has_node(item):
@@ -15,6 +50,15 @@ def build_graph(sequence):
 
 
 def remove_unreachable_nodes(graph):
+    """Removes unreachable nodes (nodes with no edges), inplace.
+
+    Parameters
+    ----------
+    graph : :class:`~gensim.summarization.graph.Graph`
+        Given graph.
+
+    """
+
     for node in graph.nodes():
         if sum(graph.edge_weight((node, other)) for other in graph.neighbors(node)) == 0:
             graph.del_node(node)
diff --git a/gensim/summarization/graph.py b/gensim/summarization/graph.py
index c35a59a25d..79cd1a160f 100644
--- a/gensim/summarization/graph.py
+++ b/gensim/summarization/graph.py
@@ -3,141 +3,203 @@
 #
 # Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html
 
+"""This module contains abstract class IGraph represents graphs interface and
+class Graph (based on IGraph) which implements undirected graph.
+
+Examples
+--------
+
+Create simple graph with 4 nodes.
+
+>>> g = Graph()
+>>> g.add_node('Felidae')
+>>> g.add_node('Lion')
+>>> g.add_node('Tiger')
+>>> g.add_node('Wolf')
+>>> sorted(g.nodes())
+['Felidae', 'Lion', 'Tiger', 'Wolf']
+
+Add some edges and check neighbours.
+
+>>> g.add_edge(("Felidae", "Lion"))
+>>> g.add_edge(("Felidae", "Tiger"))
+>>> g.neighbors("Felidae")
+['Lion', 'Tiger']
+
+One node has no neighbours.
+
+>>> g.neighbors("Wolf")
+[]
+
+"""
+
 from abc import ABCMeta, abstractmethod
 
 
 class IGraph(object):
-    """ Represents the interface or contract that the graph for TextRank
+    """Represents the interface or contract that the graph for TextRank
     should implement.
     """
     __metaclass__ = ABCMeta
 
     @abstractmethod
     def nodes(self):
-        """
-        Return node list.
+        """Returns all nodes of graph.
+
+        Returns
+        -------
+        list of hashable
+            Nodes of graph.
 
-        @rtype:  list
-        @return: Node list.
         """
         pass
 
     @abstractmethod
     def edges(self):
-        """
-        Return all edges in the graph.
+        """Returns all edges of graph.
+
+        Returns
+        -------
+        list of (hashable, hashable)
+            Edges of graph.
 
-        @rtype:  list
-        @return: List of all edges in the graph.
         """
         pass
 
     @abstractmethod
     def neighbors(self, node):
-        """
-        Return all nodes that are directly accessible from given node.
+        """Return all nodes that are directly accessible from given node.
+
+        Parameters
+        ----------
+        node : hashable
+            Given node identifier.
 
-        @type  node: node
-        @param node: Node identifier
+        Returns
+        -------
+        list of hashable
+            Nodes directly accessible from given `node`.
 
-        @rtype:  list
-        @return: List of nodes directly accessible from given node.
         """
         pass
 
     @abstractmethod
     def has_node(self, node):
-        """
-        Return whether the requested node exists.
+        """Returns whether the requested node exists.
+
+        Parameters
+        ----------
+        node : hashable
+            Given node identifier.
 
-        @type  node: node
-        @param node: Node identifier
+        Returns
+        -------
+        bool
+            True if `node` exists, False otherwise.
 
-        @rtype:  boolean
-        @return: Truth-value for node existence.
         """
         pass
 
     @abstractmethod
     def add_node(self, node, attrs=None):
-        """
-        Add given node to the graph.
+        """Adds given node to the graph.
 
-        @attention: While nodes can be of any type, it's strongly recommended
-        to use only numbers and single-line strings as node identifiers if you
-        intend to use write().
+        Note
+        ----
+        While nodes can be of any type, it's strongly recommended to use only numbers and single-line strings
+        as node identifiers if you intend to use write().
 
-        @type  node: node
-        @param node: Node identifier.
+        Parameters
+        ----------
+        node : hashable
+            Given node
+        attrs : list, optional
+            Node attributes specified as (attribute, value)
 
-        @type  attrs: list
-        @param attrs: List of node attributes specified as (attribute, value)
-        tuples.
         """
         pass
 
     @abstractmethod
     def add_edge(self, edge, wt=1, label='', attrs=None):
-        """
-        Add an edge to the graph connecting two nodes.
-
-        An edge, here, is a pair of nodes like C{(n, m)}.
-
-        @type  edge: tuple
-        @param edge: Edge.
-
-        @type  wt: number
-        @param wt: Edge weight.
+        """Adds an edge to the graph connecting two nodes. An edge, here,
+        is a tuple of two nodes.
+
+        Parameters
+        ----------
+        edge : (hashable, hashable)
+            Given edge.
+        wt : float, optional
+            Weight of new edge.
+        label : str, optional
+            Edge label.
+        attrs : list, optional
+            Node attributes specified as (attribute, value)
 
-        @type  label: string
-        @param label: Edge label.
-
-        @type  attrs: list
-        @param attrs: List of node attributes specified as (attribute, value)
-        tuples.
         """
         pass
 
     @abstractmethod
     def has_edge(self, edge):
-        """
-        Return whether an edge exists.
+        """Returns whether an edge exists.
+
+        Parameters
+        ----------
+        edge : (hashable, hashable)
+            Given edge.
 
-        @type  edge: tuple
-        @param edge: Edge.
+        Returns
+        -------
+        bool
+            True if `edge` exists, False otherwise.
 
-        @rtype:  boolean
-        @return: Truth-value for edge existence.
         """
         pass
 
     @abstractmethod
     def edge_weight(self, edge):
-        """
-        Get the weight of an edge.
+        """Returns weigth of given edge.
+
+        Parameters
+        ----------
+        edge : (hashable, hashable)
+            Given edge.
 
-        @type  edge: edge
-        @param edge: One edge.
+        Returns
+        -------
+        float
+            Edge weight.
 
-        @rtype:  number
-        @return: Edge weight.
         """
         pass
 
     @abstractmethod
     def del_node(self, node):
-        """
-        Remove a node from the graph.
+        """Removes node and its edges from the graph.
+
+        Parameters
+        ----------
+        node : hashable
+            Node to delete.
 
-        @type  node: node
-        @param node: Node identifier.
         """
         pass
 
 
 class Graph(IGraph):
     """
-    Implementation of an undirected graph, based on Pygraph
+    Implementation of an undirected graph, based on IGraph.
+
+    Attributes
+    ----------
+    Graph.WEIGHT_ATTRIBUTE_NAME : str
+        Name of weight attribute in graph.
+    Graph.DEFAULT_WEIGHT : float
+        Weight set by default.
+    Graph.LABEL_ATTRIBUTE_NAME : str
+        Default name of attribute. Not used.
+    Graph.DEFAULT_LABEL : str
+        Label set by default. Not used.
+
     """
 
     WEIGHT_ATTRIBUTE_NAME = "weight"
@@ -147,6 +209,8 @@ class Graph(IGraph):
     DEFAULT_LABEL = ""
 
     def __init__(self):
+        """Initializes object."""
+
         # Metadata about edges
         # Mapping: Edge -> Dict mapping, lablel-> str, wt->num
         self.edge_properties = {}
@@ -160,19 +224,90 @@ def __init__(self):
         self.node_neighbors = {}
 
     def has_edge(self, edge):
+        """Returns whether an edge exists.
+
+        Parameters
+        ----------
+        edge : (hashable, hashable)
+            Given edge.
+
+        Returns
+        -------
+        bool
+            True if `edge` exists, False otherwise.
+
+        """
         u, v = edge
         return (u, v) in self.edge_properties and (v, u) in self.edge_properties
 
     def edge_weight(self, edge):
+        """Returns weight of given edge.
+
+        Parameters
+        ----------
+        edge : (hashable, hashable)
+            Given edge.
+
+        Returns
+        -------
+        float
+            Edge weight.
+
+        """
         return self.get_edge_properties(edge).setdefault(self.WEIGHT_ATTRIBUTE_NAME, self.DEFAULT_WEIGHT)
 
     def neighbors(self, node):
+        """Returns all nodes that are directly accessible from given node.
+
+        Parameters
+        ----------
+        node : hashable
+            Given node identifier.
+
+        Returns
+        -------
+        list of hashable
+            Nodes directly accessible from given `node`.
+
+        """
         return self.node_neighbors[node]
 
     def has_node(self, node):
+        """Returns whether the requested node exists.
+
+        Parameters
+        ----------
+        node : hashable
+            Given node.
+
+        Returns
+        -------
+        bool
+            True if `node` exists, False otherwise.
+
+        """
         return node in self.node_neighbors
 
     def add_edge(self, edge, wt=1, label='', attrs=None):
+        """Adds an edge to the graph connecting two nodes.
+
+        Parameters
+        ----------
+        edge : (hashable, hashable)
+            Given edge.
+        wt : float, optional
+            Weight of new edge.
+        label : str, optional
+            Edge label.
+        attrs : list, optional
+            Node attributes specified as (attribute, value).
+
+        Raises
+        ------
+        ValueError
+            If `edge` already exists in graph.
+
+        """
         if attrs is None:
             attrs = []
         u, v = edge
@@ -187,6 +322,27 @@ def add_edge(self, edge, wt=1, label='', attrs=None):
             raise ValueError("Edge (%s, %s) already in graph" % (u, v))
 
     def add_node(self, node, attrs=None):
+        """Adds given node to the graph.
+
+        Note
+        ----
+        While nodes can be of any type, it's strongly recommended
+        to use only numbers and single-line strings as node identifiers if you
+        intend to use write().
+
+        Parameters
+        ----------
+        node : hashable
+            Given node.
+        attrs : list of (hashable, hashable), optional
+            Node attributes specified as (attribute, value)
+
+        Raises
+        ------
+        ValueError
+            If `node` already exists in graph.
+
+        """
         if attrs is None:
             attrs = []
         if node not in self.node_neighbors:
@@ -196,44 +352,138 @@ def add_node(self, node, attrs=None):
             raise ValueError("Node %s already in graph" % node)
 
     def nodes(self):
+        """Returns all nodes of the graph.
+
+        Returns
+        -------
+        list of hashable
+            Nodes of graph.
+
+        """
         return list(self.node_neighbors.keys())
 
     def edges(self):
+        """Returns all edges of the graph.
+
+        Returns
+        -------
+        list of (hashable, hashable)
+            Edges of graph.
+
+        """
         return [a for a in self.edge_properties.keys()]
 
     def del_node(self, node):
+        """Removes given node and its edges from the graph.
+
+        Parameters
+        ----------
+        node : hashable
+            Given node.
+
+        """
         for each in list(self.neighbors(node)):
             if each != node:
                 self.del_edge((each, node))
         del self.node_neighbors[node]
         del self.node_attr[node]
 
-    # Helper methods
     def get_edge_properties(self, edge):
+        """Returns properties of given given edge. If edge doesn't exist
+        empty dictionary will be returned.
+
+        Parameters
+        ----------
+        edge : (hashable, hashable)
+            Given edge.
+
+        Returns
+        -------
+        dict
+            Properties of graph.
+
+        """
         return self.edge_properties.setdefault(edge, {})
 
     def add_edge_attributes(self, edge, attrs):
+        """Adds attributes `attrs` to given edge, order of nodes in edge doesn't matter.
+
+        Parameters
+        ----------
+        edge : (hashable, hashable)
+            Given edge.
+        attrs : list
+            Provided attributes to add.
+
+        """
         for attr in attrs:
             self.add_edge_attribute(edge, attr)
 
     def add_edge_attribute(self, edge, attr):
+        """Adds attribute `attr` to given edge, order of nodes in edge doesn't matter.
+
+        Parameters
+        ----------
+        edge : (hashable, hashable)
+            Given edge.
+
+        attr : object
+            Provided attribute to add.
+
+        """
         self.edge_attr[edge] = self.edge_attributes(edge) + [attr]
 
         if edge[0] != edge[1]:
             self.edge_attr[(edge[1], edge[0])] = self.edge_attributes((edge[1], edge[0])) + [attr]
 
     def edge_attributes(self, edge):
+        """Returns attributes of given edge.
+
+        Note
+        ----
+        In case of non existing edge returns empty list.
+
+        Parameters
+        ----------
+        edge : (hashable, hashable)
+            Given edge.
+
+        Returns
+        -------
+        list
+            Attributes of given edge.
+
+        """
         try:
             return self.edge_attr[edge]
         except KeyError:
             return []
 
     def set_edge_properties(self, edge, **properties):
+        """Adds `properties` to given edge, order of nodes in edge doesn't matter.
+
+        Parameters
+        ----------
+        edge : (hashable, hashable)
+            Given edge.
+
+        properties : dict
+            Properties to add.
+
+        """
         self.edge_properties.setdefault(edge, {}).update(properties)
         if edge[0] != edge[1]:
             self.edge_properties.setdefault((edge[1], edge[0]), {}).update(properties)
 
     def del_edge(self, edge):
+        """Removes given edges from the graph.
+
+        Parameters
+        ----------
+        edge : (hashable, hashable)
+            Given edge.
+
+        """
         u, v = edge
         self.node_neighbors[u].remove(v)
         self.del_edge_labeling((u, v))
@@ -242,6 +492,14 @@ def del_edge(self, edge):
             self.del_edge_labeling((v, u))
 
     def del_edge_labeling(self, edge):
+        """Removes attributes and properties of given edge.
+
+        Parameters
+        ----------
+        edge : (hashable, hashable)
+            Given edge.
+
+        """
         keys = [edge, edge[::-1]]
 
         for key in keys:
diff --git a/gensim/summarization/keywords.py b/gensim/summarization/keywords.py
index 1630c9389d..4074088a04 100644
--- a/gensim/summarization/keywords.py
+++ b/gensim/summarization/keywords.py
@@ -3,6 +3,35 @@
 #
 # Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html
 
+"""This module contains functions to find keywords of the text and building graph on tokens from text.
+
+Examples
+--------
+Extract keywords from text
+
+>>> from gensim.summarization import keywords
+>>> text='''Challenges in natural language processing frequently involve
+... speech recognition, natural language understanding, natural language
+... generation (frequently from formal, machine-readable logical forms),
+... connecting language and machine perception, dialog systems, or some
+... combination thereof.'''
+>>> keywords(text).split('\\n')
+[u'natural language', u'machine', u'frequently']
+
+
+Notes
+-----
+Check tags in http://www.clips.ua.ac.be/pages/mbsp-tags and use only first two letters
+for `INCLUDING_FILTER` and `EXCLUDING_FILTER`
+
+Data:
+-----
+.. data:: WINDOW_SIZE - Size of window, number of consecutive tokens in processing.
+.. data:: INCLUDING_FILTER - Including part of speech filters.
+.. data:: EXCLUDING_FILTER - Excluding part of speech filters.
+
+"""
+
 from gensim.summarization.pagerank_weighted import pagerank_weighted as _pagerank
 from gensim.summarization.textcleaner import clean_text_by_word as _clean_text_by_word
 from gensim.summarization.textcleaner import tokenize_by_word as _tokenize_by_word
@@ -17,20 +46,43 @@
 
 WINDOW_SIZE = 2
 
-"""
-Check tags in http://www.clips.ua.ac.be/pages/mbsp-tags and use only first two letters
-Example: filter for nouns and adjectives:
-INCLUDING_FILTER = ['NN', 'JJ']
-"""
 INCLUDING_FILTER = ['NN', 'JJ']
 EXCLUDING_FILTER = []
 
 
 def _get_pos_filters():
+    """Get default including and excluding filters as frozen sets.
+
+    Returns
+    -------
+    (frozenset of str, frozenset of str)
+        Including and excluding filters.
+
+    """
     return frozenset(INCLUDING_FILTER), frozenset(EXCLUDING_FILTER)
 
 
 def _get_words_for_graph(tokens, pos_filter=None):
+    """Filters given dictionary of tokens using provided part of speech filters.
+
+    Parameters
+    ----------
+    tokens : dict
+        Original units (words) as keys and processed units (tokens) as values.
+    pos_filter : iterable
+        Part of speech filters, optional. If `None` - using :func:`_get_pos_filters`.
+
+    Returns
+    -------
+    list of str
+        Filtered tokens.
+
+    Raises
+    ------
+    ValueError
+        If include and exclude filters ar not empty at the same time.
+
+    """
     if pos_filter is None:
         include_filters, exclude_filters = _get_pos_filters()
     else:
@@ -49,10 +101,37 @@ def _get_words_for_graph(tokens, pos_filter=None):
 
 
 def _get_first_window(split_text):
+    """Get first :const:`~gensim.parsing.keywords.WINDOW_SIZE` tokens from given `split_text`.
+
+    Parameters
+    ----------
+    split_text : list of str
+        Splitted text.
+
+    Returns
+    -------
+    list of str
+        First :const:`~gensim.parsing.keywords.WINDOW_SIZE` tokens.
+
+    """
     return split_text[:WINDOW_SIZE]
 
 
 def _set_graph_edge(graph, tokens, word_a, word_b):
+    """Sets an edge between nodes named word_a and word_b if they exists in `tokens` and `graph`, inplace.
+
+    Parameters
+    ----------
+    graph : :class:~gensim.summarization.graph.Graph
+        Given graph.
+    tokens : dict
+        Original units (words) as keys and processed units (tokens) as values.
+    word_a : str
+        First word, name of first node.
+    word_b : str
+        Second word, name of second node.
+
+    """
     if word_a in tokens and word_b in tokens:
         lemma_a = tokens[word_a].token
         lemma_b = tokens[word_b].token
@@ -63,12 +142,38 @@ def _set_graph_edge(graph, tokens, word_a, word_b):
 
 
 def _process_first_window(graph, tokens, split_text):
+    """Sets an edges between nodes taken from first :const:`~gensim.parsing.keywords.WINDOW_SIZE`
+    words of `split_text` if they exist in `tokens` and `graph`, inplace.
+
+    Parameters
+    ----------
+    graph : :class:~gensim.summarization.graph.Graph
+        Given graph.
+    tokens : dict
+        Original units (words) as keys and processed units (tokens) as values.
+    split_text : list of str
+        Splitted text.
+
+    """
     first_window = _get_first_window(split_text)
     for word_a, word_b in _combinations(first_window, 2):
         _set_graph_edge(graph, tokens, word_a, word_b)
 
 
 def _init_queue(split_text):
+    """Initialize queue by first words from `split_text`.
+
+    Parameters
+    ----------
+    split_text : list of str
+        Splitted text.
+
+    Returns
+    -------
+    Queue
+        Initialized queue.
+
+    """
     queue = _Queue()
     first_window = _get_first_window(split_text)
     for word in first_window[1:]:
@@ -77,17 +182,56 @@ def _init_queue(split_text):
 
 
 def _process_word(graph, tokens, queue, word):
+    """Sets edge between `word` and each element in queue in `graph` if such nodes
+    exist in `tokens` and `graph`.
+
+    Parameters
+    ----------
+    graph : :class:`~gensim.summarization.graph.Graph`
+        Given graph.
+    tokens : dict
+        Original units (words) as keys and processed units (tokens) as values.
+    queue : Queue
+        Given queue.
+    word : str
+        Word, possible `node` in graph and item in `tokens`.
+
+    """
     for word_to_compare in _queue_iterator(queue):
         _set_graph_edge(graph, tokens, word, word_to_compare)
 
 
 def _update_queue(queue, word):
+    """Updates given `queue` (removes last item and puts `word`).
+
+    Parameters
+    ----------
+    queue : Queue
+        Given queue.
+    word : str
+        Word to be added to queue.
+
+    """
     queue.get()
     queue.put(word)
     assert queue.qsize() == (WINDOW_SIZE - 1)
 
 
 def _process_text(graph, tokens, split_text):
+    """Process `split_text` by updating given `graph` with new eges between nodes
+    if they exists in `tokens` and `graph`.
+    Words are taken from `split_text` with window size :const:`~gensim.parsing.keywords.WINDOW_SIZE`.
+
+    Parameters
+    ----------
+    graph : :class:`~gensim.summarization.graph.Graph`
+        Given graph.
+    tokens : dict
+        Original units (words) as keys and processed units (tokens) as values.
+    split_text : list of str
+        Splitted text.
+
+    """
     queue = _init_queue(split_text)
     for i in xrange(WINDOW_SIZE, len(split_text)):
         word = split_text[i]
@@ -96,6 +240,19 @@ def _process_text(graph, tokens, split_text):
 
 
 def _queue_iterator(queue):
+    """Represents iterator of the given queue.
+
+    Parameters
+    ----------
+    queue : Queue
+        Given queue.
+
+    Yields
+    ------
+    str
+        Current item of queue.
+
+    """
     iterations = queue.qsize()
     for _ in xrange(iterations):
         var = queue.get()
@@ -104,20 +261,63 @@ def _queue_iterator(queue):
 
 
 def _set_graph_edges(graph, tokens, split_text):
+    """Updates given `graph` by setting eges between nodes if they exists in `tokens` and `graph`.
+    Words are taken from `split_text` with window size :const:`~gensim.parsing.keywords.WINDOW_SIZE`.
+
+    Parameters
+    ----------
+    graph : :class:~gensim.summarization.graph.Graph
+        Given graph.
+    tokens : dict
+        Original units (words) as keys and processed units (tokens) as values.
+    split_text : list of str
+        Splitted text.
+
+    """
     _process_first_window(graph, tokens, split_text)
     _process_text(graph, tokens, split_text)
 
 
 def _extract_tokens(lemmas, scores, ratio, words):
-    lemmas.sort(key=lambda s: scores[s], reverse=True)
+    """Extracts tokens from provided lemmas. Most scored lemmas are used if `words` not provided.
+
+    Parameters
+    ----------
+    lemmas : list of str
+        Given lemmas.
+    scores : dict
+        Dictionary with lemmas and its scores.
+    ratio : float
+        Proportion of lemmas used for final result.
+    words : int
+        Number of used words. If no "words" option is selected, the number of
+        sentences is reduced by the provided ratio, else, the ratio is ignored.
+
+    Returns
+    -------
+    list of (float, str)
+        Scores and corresponded lemmas.
 
-    # If no "words" option is selected, the number of sentences is
-    # reduced by the provided ratio, else, the ratio is ignored.
+    """
+    lemmas.sort(key=lambda s: scores[s], reverse=True)
     length = len(lemmas) * ratio if words is None else words
     return [(scores[lemmas[i]], lemmas[i],) for i in range(int(length))]
 
 
 def _lemmas_to_words(tokens):
+    """Get words and lemmas from given tokens. Produces "reversed" `tokens`.
+
+    Parameters
+    ----------
+    tokens : dict
+        Original units (words) as keys and processed units (tokens) as values.
+
+    Returns
+    -------
+    dict
+        Lemmas as keys and lists corresponding words as values.
+
+    """
     lemma_to_word = {}
     for word, unit in iteritems(tokens):
         lemma = unit.token
@@ -129,11 +329,22 @@ def _lemmas_to_words(tokens):
 
 
 def _get_keywords_with_score(extracted_lemmas, lemma_to_word):
+    """Get words of `extracted_lemmas` and its scores, words contains in `lemma_to_word`.
+
+    Parameters
+    ----------
+    extracted_lemmas : list of (float, str)
+        Given lemmas with scores
+    lemma_to_word : dict
+        Lemmas and corresponding words.
+
+    Returns
+    -------
+    dict
+        Keywords as keys and its scores as values.
+
     """
-    :param extracted_lemmas:list of tuples
-    :param lemma_to_word: dict of {lemma:list of words}
-    :return: dict of {keyword:score}
-    """
+
     keywords = {}
     for score, lemma in extracted_lemmas:
         keyword_list = lemma_to_word[lemma]
@@ -143,15 +354,37 @@ def _get_keywords_with_score(extracted_lemmas, lemma_to_word):
 
 
 def _strip_word(word):
+    """Get cleaned `word`.
+
+    Parameters
+    ----------
+    word : str
+        Given word.
+
+    Returns
+    -------
+    str
+        Cleaned word.
+    """
     stripped_word_list = list(_tokenize_by_word(word))
     return stripped_word_list[0] if stripped_word_list else ""
 
 
 def _get_combined_keywords(_keywords, split_text):
-    """
-    :param keywords:dict of keywords:scores
-    :param split_text: list of strings
-    :return: combined_keywords:list
+    """Get most scored words (`_keywords`) contained in `split_text` and it's combinations.
+
+    Parameters
+    ----------
+    _keywords : dict
+        Keywords as keys and its scores as values.
+    split_text : list of str
+        Splitted text.
+
+    Returns
+    -------
+    list of str
+        Keywords and/or its combinations.
+
     """
     result = []
     _keywords = _keywords.copy()
@@ -175,6 +408,21 @@ def _get_combined_keywords(_keywords, split_text):
 
 
 def _get_average_score(concept, _keywords):
+    """Get average score of words in `concept`.
+
+    Parameters
+    ----------
+    concept : str
+        Input text.
+    _keywords : dict
+        Keywords as keys and its scores as values.
+
+    Returns
+    -------
+    float
+        Average score.
+
+    """
     word_list = concept.split()
     word_counter = 0
     total = 0
@@ -185,9 +433,29 @@ def _get_average_score(concept, _keywords):
 
 
 def _format_results(_keywords, combined_keywords, split, scores):
-    """
-    :param keywords:dict of keywords:scores
-    :param combined_keywords:list of word/s
+    """Formats, sorts and returns `combined_keywords` in desired format.
+
+    Parameters
+    ----------
+    _keywords : dict
+        Keywords as keys and its scores as values.
+    combined_keywords : list of str
+        Most ranked words and/or its combinations.
+    split : bool
+        Split result if True or return string otherwise, optional.
+    scores : bool
+        Whether return `combined_keywords` with scores, optional. If True
+        `split` is ignored.
+
+    Returns
+    -------
+    result: list of (str, float)
+        If `scores`, keywords with scores **OR**
+    result: list of str
+        If `split`, keywords only **OR**
+    result: str
+        Keywords, joined by endl.
+
     """
     combined_keywords.sort(key=lambda w: _get_average_score(w, _keywords), reverse=True)
     if scores:
@@ -199,6 +467,39 @@ def _format_results(_keywords, combined_keywords, split, scores):
 
 def keywords(text, ratio=0.2, words=None, split=False, scores=False, pos_filter=('NN', 'JJ'),
              lemmatize=False, deacc=True):
+    """Get most ranked words of provided text and/or its combinations.
+
+    Parameters
+    ----------
+
+    text : str
+        Input text.
+    ratio : float, optional
+        If no "words" option is selected, the number of sentences is reduced by the provided ratio,
+        else, the ratio is ignored.
+    words : int, optional
+        Number of returned words.
+    split : bool, optional
+        Whether split keywords if True.
+    scores : bool, optional
+        Whether score of keyword.
+    pos_filter : tuple, optional
+        Part of speech filters.
+    lemmatize : bool, optional
+        If True - lemmatize words.
+    deacc : bool, optional
+        If True - remove accentuation.
+
+    Returns
+    -------
+    result: list of (str, float)
+        If `scores`, keywords with scores **OR**
+    result: list of str
+        If `split`, keywords only **OR**
+    result: str
+        Keywords, joined by endl.
+
+    """
     # Gets a dict of word -> lemma
     text = to_unicode(text)
     tokens = _clean_text_by_word(text, deacc=deacc)
@@ -233,6 +534,19 @@ def keywords(text, ratio=0.2, words=None, split=False, scores=False, pos_filter=
 
 
 def get_graph(text):
+    """Creates and returns graph from given text, cleans and tokenize text before building graph.
+
+    Parameters
+    ----------
+    text : str
+        Sequence of values.
+
+    Returns
+    -------
+    :class:`~gensim.summarization.graph.Graph`
+        Created graph.
+
+    """
     tokens = _clean_text_by_word(text)
     split_text = list(_tokenize_by_word(text))
 
diff --git a/gensim/summarization/mz_entropy.py b/gensim/summarization/mz_entropy.py
index b9c5c02f33..11437f5c86 100644
--- a/gensim/summarization/mz_entropy.py
+++ b/gensim/summarization/mz_entropy.py
@@ -11,52 +11,47 @@
 
 
 def mz_keywords(text, blocksize=1024, scores=False, split=False, weighted=True, threshold=0.0):
-    """Extract keywords from text using the Montemurro and Zanette entropy
-    algorithm. [1]_
+    """Extract keywords from text using the Montemurro and Zanette entropy algorithm. [1]_
 
     Parameters
     ----------
     text: str
-        document to summarize
+        Document for summarization.
     blocksize: int, optional
-        size of blocks to use in analysis, default is 1024
+        Size of blocks to use in analysis.
     scores: bool, optional
-        Whether to return score with keywords, default is False
+        Whether to return score with keywords.
     split: bool, optional
-        Whether to return results as list, default is False
+        Whether to return results as list.
     weighted: bool, optional
-        Whether to weight scores by word frequency. Default is True.
-        False can useful for shorter texts, and allows automatic thresholding
+        Whether to weight scores by word frequency.
+        False can useful for shorter texts, and allows automatic thresholding.
     threshold: float or 'auto', optional
-        minimum score for returned keywords, default 0.0
-        'auto' calculates the threshold as n_blocks / (n_blocks + 1.0) + 1.0e-8
-        Use 'auto' with weighted=False)
+        Minimum score for returned keywords,  'auto' calculates the threshold as n_blocks / (n_blocks + 1.0) + 1e-8,
+        use 'auto' with `weighted=False`.
 
     Returns
     -------
     results: str
-        newline separated keywords if `split` == False OR
+        newline separated keywords if `split` == False **OR**
     results: list(str)
-        list of keywords if `scores` == False OR
+        list of keywords if `scores` == False **OR**
     results: list(tuple(str, float))
         list of (keyword, score) tuples if `scores` == True
 
     Results are returned in descending order of score regardless of the format.
 
-    Notes
-    -----
+    Note
+    ----
     This algorithm looks for keywords that contribute to the structure of the
-    text on scales of blocksize words of larger. It is suitable for extracting
+    text on scales of `blocksize` words of larger. It is suitable for extracting
     keywords representing the major themes of long texts.
 
     References
     ----------
-    [1] Marcello A Montemurro, Damian Zanette,
-        "Towards the quantification of the semantic information encoded in
-        written language"
-        Advances in Complex Systems, Volume 13, Issue 2 (2010), pp. 135-153
-        DOI: 10.1142/S0219525910002530
-        https://arxiv.org/abs/0907.1558
+    .. [1] Marcello A Montemurro, Damian Zanette, "Towards the quantification of the semantic information encoded in
+           written language". Advances in Complex Systems, Volume 13, Issue 2 (2010), pp. 135-153,
+           DOI: 10.1142/S0219525910002530, https://arxiv.org/abs/0907.1558
 
     """
     text = to_unicode(text)
diff --git a/gensim/summarization/pagerank_weighted.py b/gensim/summarization/pagerank_weighted.py
index f5a24635a1..df1352367c 100644
--- a/gensim/summarization/pagerank_weighted.py
+++ b/gensim/summarization/pagerank_weighted.py
@@ -2,6 +2,36 @@
 # -*- coding: utf-8 -*-
 #
 # Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html
+
+"""This module calculate PageRank [1]_ based on wordgraph.
+
+
+.. [1] https://en.wikipedia.org/wiki/PageRank
+
+Examples
+--------
+
+Calculate Pagerank for words
+
+>>> from gensim.summarization.keywords import get_graph
+>>> from gensim.summarization.pagerank_weighted import pagerank_weighted
+>>> graph = get_graph("The road to hell is paved with good intentions.")
+>>> # result will looks like {'good': 0.70432858653171504, 'hell': 0.051128871128006126, ...}
+>>> result = pagerank_weighted(graph)
+
+Build matrix from graph
+
+>>> from gensim.summarization.pagerank_weighted import build_adjacency_matrix
+>>> build_adjacency_matrix(graph).todense()
+matrix([[ 0.,  0.,  0.,  0.,  0.],
+        [ 0.,  0.,  1.,  0.,  0.],
+        [ 0.,  1.,  0.,  0.,  0.],
+        [ 0.,  0.,  0.,  0.,  0.],
+        [ 0.,  0.,  0.,  0.,  0.]])
+
+"""
+
+
 import numpy
 from numpy import empty as empty_matrix
 from scipy.linalg import eig
@@ -9,15 +39,23 @@
 from scipy.sparse.linalg import eigs
 from six.moves import xrange
 
-try:
-    from numpy import VisibleDeprecationWarning
-    import warnings
-    warnings.filterwarnings("ignore", category=VisibleDeprecationWarning)
-except ImportError:
-    pass
-
 
 def pagerank_weighted(graph, damping=0.85):
+    """Get dictionary of `graph` nodes and its ranks.
+
+    Parameters
+    ----------
+    graph : :class:`~gensim.summarization.graph.Graph`
+        Given graph.
+    damping : float
+        Damping parameter, optional
+
+    Returns
+    -------
+    dict
+        Nodes of `graph` as keys, its ranks as values.
+
+    """
     adjacency_matrix = build_adjacency_matrix(graph)
     probability_matrix = build_probability_matrix(graph)
 
@@ -30,6 +68,19 @@ def pagerank_weighted(graph, damping=0.85):
 
 
 def build_adjacency_matrix(graph):
+    """Get matrix representation of given `graph`.
+
+    Parameters
+    ----------
+    graph : :class:`~gensim.summarization.graph.Graph`
+        Given graph.
+
+    Returns
+    -------
+    :class:`scipy.sparse.csr_matrix`, shape = [n, n]
+        Adjacency matrix of given `graph`, n is number of nodes.
+
+    """
     row = []
     col = []
     data = []
@@ -50,6 +101,20 @@ def build_adjacency_matrix(graph):
 
 
 def build_probability_matrix(graph):
+    """Get square matrix of shape (n, n), where n is number of nodes of the
+    given `graph`.
+
+    Parameters
+    ----------
+    graph : :class:`~gensim.summarization.graph.Graph`
+        Given graph.
+
+    Returns
+    -------
+    numpy.ndarray, shape = [n, n]
+        Eigenvector of matrix `a`, n is number of nodes of `graph`.
+
+    """
     dimension = len(graph.nodes())
     matrix = empty_matrix((dimension, dimension))
 
@@ -60,6 +125,19 @@ def build_probability_matrix(graph):
 
 
 def principal_eigenvector(a):
+    """Get eigenvector of square matrix `a`.
+
+    Parameters
+    ----------
+    a : numpy.ndarray, shape = [n, n]
+        Given matrix.
+
+    Returns
+    -------
+    numpy.ndarray, shape = [n, ]
+        Eigenvector of matrix `a`.
+
+    """
     # Note that we prefer to use `eigs` even for dense matrix
     # because we need only one eigenvector. See #441, #438 for discussion.
 
@@ -74,6 +152,22 @@ def principal_eigenvector(a):
 
 
 def process_results(graph, vec):
+    """Get `graph` nodes and corresponding absolute values of provided eigenvector.
+    This function is helper for :func:`~gensim.summarization.pagerank_weighted.pagerank_weighted`
+
+    Parameters
+    ----------
+    graph : :class:`~gensim.summarization.graph.Graph`
+        Given graph.
+    vec : numpy.ndarray, shape = [n, ]
+        Given eigenvector, n is number of nodes of `graph`.
+
+    Returns
+    -------
+    dict
+        Graph nodes as keys, corresponding elements of eigenvector as values.
+
+    """
     scores = {}
     for i, node in enumerate(graph.nodes()):
         scores[node] = abs(vec[i])
diff --git a/gensim/summarization/summarizer.py b/gensim/summarization/summarizer.py
index 1b1251b1d7..afeb359b15 100644
--- a/gensim/summarization/summarizer.py
+++ b/gensim/summarization/summarizer.py
@@ -3,7 +3,55 @@
 #
 # Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html
 
+"""This module provides functions for summarizing texts. Summarizing is based on
+ranks of text sentences using a variation of the TextRank algorithm [1]_.
+
+.. [1] Federico Barrios, Federico L´opez, Luis Argerich, Rosita Wachenchauzer (2016).
+       Variations of the Similarity Function of TextRank for Automated Summarization,
+       https://arxiv.org/abs/1602.03606
+
+
+Data
+----
+
+.. data:: INPUT_MIN_LENGTH - Minimal number of sentences in text
+.. data:: WEIGHT_THRESHOLD - Minimal weight of edge between graph nodes. Smaller weights set to zero.
+
+Example
+-------
+
+>>> from gensim.summarization.summarizer import summarize
+>>> text = '''Rice Pudding - Poem by Alan Alexander Milne
+... What is the matter with Mary Jane?
+... She's crying with all her might and main,
+... And she won't eat her dinner - rice pudding again -
+... What is the matter with Mary Jane?
+... What is the matter with Mary Jane?
+... I've promised her dolls and a daisy-chain,
+... And a book about animals - all in vain -
+... What is the matter with Mary Jane?
+... What is the matter with Mary Jane?
+... She's perfectly well, and she hasn't a pain;
+... But, look at her, now she's beginning again! -
+... What is the matter with Mary Jane?
+... What is the matter with Mary Jane?
+... I've promised her sweets and a ride in the train,
+... And I've begged her to stop for a bit and explain -
+... What is the matter with Mary Jane?
+... What is the matter with Mary Jane?
+... She's perfectly well and she hasn't a pain,
+... And it's lovely rice pudding for dinner again!
+... What is the matter with Mary Jane?'''
+>>> print(summarize(text))
+And she won't eat her dinner - rice pudding again -
+I've promised her dolls and a daisy-chain,
+I've promised her sweets and a ride in the train,
+And it's lovely rice pudding for dinner again!
+
+"""
+
 import logging
+from gensim.utils import deprecated
 from gensim.summarization.pagerank_weighted import pagerank_weighted as _pagerank
 from gensim.summarization.textcleaner import clean_text_by_sentences as _clean_text_by_sentences
 from gensim.summarization.commons import build_graph as _build_graph
@@ -22,6 +70,15 @@
 
 
 def _set_graph_edge_weights(graph):
+    """Sets weights using BM25 algorithm. Leaves small weights as zeroes. If all weights are fairly small,
+     forces all weights to 1, inplace.
+
+    Parameters
+    ----------
+    graph : :class:`~gensim.summarization.graph.Graph`
+        Given graph.
+
+    """
     documents = graph.nodes()
     weights = _bm25_weights(documents)
 
@@ -48,6 +105,14 @@ def _set_graph_edge_weights(graph):
 
 
 def _create_valid_graph(graph):
+    """Sets all weights of edges for different edges as 1, inplace.
+
+    Parameters
+    ----------
+    graph : :class:`~gensim.summarization.graph.Graph`
+        Given graph.
+
+    """
     nodes = graph.nodes()
 
     for i in xrange(len(nodes)):
@@ -63,11 +128,45 @@ def _create_valid_graph(graph):
             graph.add_edge(edge, 1)
 
 
+@deprecated("Function will be removed in 4.0.0")
 def _get_doc_length(doc):
+    """Get length of (tokenized) document.
+
+    Parameters
+    ----------
+    doc : list of (list of (tuple of int))
+        Given document.
+
+    Returns
+    -------
+    int
+        Length of document.
+
+    """
     return sum([item[1] for item in doc])
 
 
+@deprecated("Function will be removed in 4.0.0")
 def _get_similarity(doc1, doc2, vec1, vec2):
+    """Returns similarity of two documents.
+
+    Parameters
+    ----------
+    doc1 : list of (list of (tuple of int))
+        First document.
+    doc2 : list of (list of (tuple of int))
+        Second document.
+    vec1 : array
+        ? of first document.
+    vec1 : array
+        ? of secont document.
+
+    Returns
+    -------
+    float
+        Similarity of two documents.
+
+    """
     numerator = vec1.dot(vec2.transpose()).toarray()[0][0]
     length_1 = _get_doc_length(doc1)
     length_2 = _get_doc_length(doc2)
@@ -78,20 +177,63 @@ def _get_similarity(doc1, doc2, vec1, vec2):
 
 
 def _build_corpus(sentences):
+    """Construct corpus from provided sentences.
+
+    Parameters
+    ----------
+    sentences : list of :class:`~gensim.summarization.syntactic_unit.SyntacticUnit`
+        Given sentences.
+
+    Returns
+    -------
+    list of list of (int, int)
+        Corpus built from sentences.
+
+    """
     split_tokens = [sentence.token.split() for sentence in sentences]
     dictionary = Dictionary(split_tokens)
     return [dictionary.doc2bow(token) for token in split_tokens]
 
 
 def _get_important_sentences(sentences, corpus, important_docs):
+    """Get most important sentences.
+
+    Parameters
+    ----------
+    sentences : list of :class:`~gensim.summarization.syntactic_unit.SyntacticUnit`
+        Given sentences.
+    corpus : list of list of (int, int)
+        Provided corpus.
+    important_docs : list of list of (int, int)
+        Most important documents of the corpus.
+
+    Returns
+    -------
+    list of :class:`~gensim.summarization.syntactic_unit.SyntacticUnit`
+        Most important sentences.
+
+    """
     hashable_corpus = _build_hasheable_corpus(corpus)
     sentences_by_corpus = dict(zip(hashable_corpus, sentences))
     return [sentences_by_corpus[tuple(important_doc)] for important_doc in important_docs]
 
 
 def _get_sentences_with_word_count(sentences, word_count):
-    """ Given a list of sentences, returns a list of sentences with a
-    total word count similar to the word count provided."""
+    """Get list of sentences. Total number of returned words close to specified `word_count`.
+
+    Parameters
+    ----------
+    sentences : list of :class:`~gensim.summarization.syntactic_unit.SyntacticUnit`
+        Given sentences.
+    word_count : int or None
+        Number of returned words. If None full most important sentences will be returned.
+
+    Returns
+    -------
+    list of :class:`~gensim.summarization.syntactic_unit.SyntacticUnit`
+        Most important sentences.
+
+    """
     length = 0
     selected_sentences = []
 
@@ -111,6 +253,25 @@ def _get_sentences_with_word_count(sentences, word_count):
 
 
 def _extract_important_sentences(sentences, corpus, important_docs, word_count):
+    """Get most important sentences of the `corpus`.
+
+    Parameters
+    ----------
+    sentences : list of :class:`~gensim.summarization.syntactic_unit.SyntacticUnit`
+        Given sentences.
+    corpus : list of list of (int, int)
+        Provided corpus.
+    important_docs : list of list of (int, int)
+        Most important docs of the corpus.
+    word_count : int
+        Number of returned words. If None full most important sentences will be returned.
+
+    Returns
+    -------
+    list of :class:`~gensim.summarization.syntactic_unit.SyntacticUnit`
+        Most important sentences.
+
+    """
     important_sentences = _get_important_sentences(sentences, corpus, important_docs)
 
     # If no "word_count" option is provided, the number of sentences is
@@ -121,29 +282,69 @@ def _extract_important_sentences(sentences, corpus, important_docs, word_count):
 
 
 def _format_results(extracted_sentences, split):
+    """Returns `extracted_sentences` in desired format.
+
+    Parameters
+    ----------
+    extracted_sentences : list of :class:~gensim.summarization.syntactic_unit.SyntacticUnit
+        Given sentences.
+    split : bool
+        If True sentences will be returned as list. Otherwise sentences will be merged and returned as string.
+
+    Returns
+    -------
+    list of str
+        If `split` **OR**
+    str
+        Formatted result.
+
+    """
     if split:
         return [sentence.text for sentence in extracted_sentences]
     return "\n".join([sentence.text for sentence in extracted_sentences])
 
 
 def _build_hasheable_corpus(corpus):
+    """Hashes and get `corpus`.
+
+    Parameters
+    ----------
+    corpus : list of list of (int, int)
+        Given corpus.
+
+    Returns
+    -------
+    list of list of (int, int)
+        Hashable corpus.
+
+    """
     return [tuple(doc) for doc in corpus]
 
 
 def summarize_corpus(corpus, ratio=0.2):
+    """Get a list of the most important documents of a corpus using a variation of the TextRank algorithm [1]_.
+     Used as helper for summarize :func:`~gensim.summarization.summarizer.summarizer`
+
+    Note
+    ----
+    The input must have at least :const:`~gensim.summarization.summarizer.INPUT_MIN_LENGTH` documents for the summary
+    to make sense.
+
+
+    Parameters
+    ----------
+    corpus : list of list of (int, int)
+        Given corpus.
+    ratio : float, optional
+        Number between 0 and 1 that determines the proportion of the number of
+        sentences of the original text to be chosen for the summary, optional.
+
+    Returns
+    -------
+    list of str
+        Most important documents of given `corpus` sorted by the document score, highest first.
+
     """
-    Returns a list of the most important documents of a corpus using a
-    variation of the TextRank algorithm.
-    The input must have at least INPUT_MIN_LENGTH (%d) documents for the
-    summary to make sense.
-
-    The length of the output can be specified using the ratio parameter,
-    which determines how many documents will be chosen for the summary
-    (defaults at 20%% of the number of documents of the corpus).
-
-    The most important documents are returned as a list sorted by the
-    document score, highest first.
-    """ % INPUT_MIN_LENGTH
     hashable_corpus = _build_hasheable_corpus(corpus)
 
     # If the corpus is empty, the function ends.
@@ -173,29 +374,39 @@ def summarize_corpus(corpus, ratio=0.2):
 
 
 def summarize(text, ratio=0.2, word_count=None, split=False):
-    """
-    Returns a summarized version of the given text using a variation of
-    the TextRank algorithm (see https://arxiv.org/abs/1602.03606).
+    """Get a summarized version of the given text.
 
     The output summary will consist of the most representative sentences
     and will be returned as a string, divided by newlines.
-    If the split parameter is set to True, a list of sentences will be
-    returned instead.
-
-    The input should be a string, and must be longer than
-    INPUT_MIN_LENGTH sentences for the summary to make sense. The text
-    will be split into sentences using the split_sentences method in the
-    summarization.texcleaner module.
-    Note that newlines divide sentences.
-
-    The length of the output can be specified using the ratio and
-    word_count parameters:
-
-        ratio should be a number between 0 and 1 that determines the
-        percentage of the number of sentences of the original text to be
-        chosen for the summary (defaults at 0.2).
-        word_count determines how many words will the output contain.
+
+    Note
+    ----
+    The input should be a string, and must be longer than :const:`~gensim.summarization.summarizer.INPUT_MIN_LENGTH`
+    sentences for the summary to make sense.
+    The text will be split into sentences using the split_sentences method in the :mod:`gensim.summarization.texcleaner`
+    module. Note that newlines divide sentences.
+
+
+    Parameters
+    ----------
+    text : str
+        Given text.
+    ratio : float, optional
+        Number between 0 and 1 that determines the proportion of the number of
+        sentences of the original text to be chosen for the summary.
+    word_count : int or None, optional
+        Determines how many words will the output contain.
         If both parameters are provided, the ratio will be ignored.
+    split : bool, optional
+        If True, list of sentences will be returned. Otherwise joined
+        strings will bwe returned.
+
+    Returns
+    -------
+    list of str
+        If `split` **OR**
+    str
+        Most representative sentences of given the text.
 
     """
     # Gets a list of processed sentences.
diff --git a/gensim/summarization/syntactic_unit.py b/gensim/summarization/syntactic_unit.py
index 89842e1122..335ee6a212 100644
--- a/gensim/summarization/syntactic_unit.py
+++ b/gensim/summarization/syntactic_unit.py
@@ -3,13 +3,46 @@
 #
 # Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html
 
+"""This module contains implementation of SyntacticUnit class. It generally used while text cleaning.
+:class:`~gensim.summarization.syntactic_unit.SyntacticUnit` represents printable version of provided text.
+
+"""
+
 
 class SyntacticUnit(object):
+    """SyntacticUnit class.
+
+    Attributes
+    ----------
+    text : str
+        Input text.
+    token : str
+        Tokenized text.
+    tag : str
+        Tag of unit, optional.
+    index : int
+        Index of sytactic unit in corpus, optional.
+    score : float
+        Score of synctatic unit, optional.
+
+    """
 
     def __init__(self, text, token=None, tag=None):
+        """
+
+        Parameters
+        ----------
+        text : str
+            Input text.
+        token : str
+            Tokenized text, optional.
+        tag : str
+            Tag of unit, optional.
+
+        """
         self.text = text
         self.token = token
-        self.tag = tag[:2] if tag else None     # Just first two letters of tag
+        self.tag = tag[:2] if tag else None  # Just first two letters of tag
         self.index = -1
         self.score = -1
 
diff --git a/gensim/summarization/textcleaner.py b/gensim/summarization/textcleaner.py
index 4829d9f892..5af6bef257 100644
--- a/gensim/summarization/textcleaner.py
+++ b/gensim/summarization/textcleaner.py
@@ -3,6 +3,23 @@
 #
 # Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html
 
+"""This module contains functions and processors used for processing text,
+extracting sentences from text, working with acronyms and abbreviations.
+
+Data
+----
+
+.. data:: SEPARATOR - Special separator used in abbreviations.
+.. data:: RE_SENTENCE - Pattern to split text to sentences.
+.. data:: AB_SENIOR - Pattern for detecting abbreviations (example: Sgt. Pepper).
+.. data:: AB_ACRONYM - Pattern for detecting acronyms.
+.. data:: AB_ACRONYM_LETTERS - Pattern for detecting acronyms (example: P.S. I love you).
+.. data:: UNDO_AB_SENIOR - Pattern like AB_SENIOR but with SEPARATOR between abbreviation and next word.
+.. data:: UNDO_AB_ACRONYM - Pattern like AB_ACRONYM but with SEPARATOR between abbreviation and next word.
+
+"""
+
+
 from gensim.summarization.syntactic_unit import SyntacticUnit
 from gensim.parsing.preprocessing import preprocess_documents
 from gensim.utils import tokenize
@@ -10,7 +27,7 @@
 import re
 import logging
 
-logger = logging.getLogger('summa.preprocessing.cleaner')
+logger = logging.getLogger('summarizer.preprocessing.cleaner')
 
 try:
     from pattern.en import tag
@@ -31,19 +48,96 @@
 
 
 def split_sentences(text):
+    """Split and get list of sentences from given text. It preserves abbreviations set in
+    :const:`~gensim.summarization.textcleaner.AB_SENIOR` and :const:`~gensim.summarization.textcleaner.AB_ACRONYM`.
+
+    Parameters
+    ----------
+    text : str
+        Input text.
+
+    Returns
+    -------
+    list of str
+        Sentences of given text.
+
+    Example
+    -------
+    >>> from gensim.summarization.textcleaner import split_sentences
+    >>> text = '''Beautiful is better than ugly.
+    ... Explicit is better than implicit. Simple is better than complex.'''
+    >>> split_sentences(text)
+    ['Beautiful is better than ugly.',
+    'Explicit is better than implicit.',
+    'Simple is better than complex.']
+
+    """
     processed = replace_abbreviations(text)
     return [undo_replacement(sentence) for sentence in get_sentences(processed)]
 
 
 def replace_abbreviations(text):
+    """Replace blank space to '@' separator after abbreviation and next word.
+
+    Parameters
+    ----------
+    text : str
+        Input sentence.
+
+    Returns
+    -------
+    str
+        Sentence with changed separator.
+
+    Example
+    -------
+    >>> replace_abbreviations("God bless you, please, Mrs. Robinson")
+    God bless you, please, Mrs.@Robinson
+
+    """
     return replace_with_separator(text, SEPARATOR, [AB_SENIOR, AB_ACRONYM])
 
 
 def undo_replacement(sentence):
+    """Replace `@` separator back to blank space after each abbreviation.
+
+    Parameters
+    ----------
+    sentence : str
+        Input sentence.
+
+    Returns
+    -------
+    str
+        Sentence with changed separator.
+
+    Example
+    -------
+    >>> undo_replacement("God bless you, please, Mrs.@Robinson")
+    God bless you, please, Mrs. Robinson
+
+    """
     return replace_with_separator(sentence, r" ", [UNDO_AB_SENIOR, UNDO_AB_ACRONYM])
 
 
 def replace_with_separator(text, separator, regexs):
+    """Get text with replaced separator if provided regular expressions were matched.
+
+    Parameters
+    ----------
+    text : str
+        Input text.
+    separator : str
+        The separator between words to be replaced.
+    regexs : list of `_sre.SRE_Pattern`
+        Regular expressions used in processing text.
+
+    Returns
+    -------
+    str
+        Text with replaced separators.
+
+    """
     replacement = r"\1" + separator + r"\2"
     result = text
     for regex in regexs:
@@ -52,11 +146,51 @@ def replace_with_separator(text, separator, regexs):
 
 
 def get_sentences(text):
+    """Sentence generator from provided text. Sentence pattern set
+    in :const:`~gensim.summarization.textcleaner.RE_SENTENCE`.
+
+    Parameters
+    ----------
+    text : str
+        Input text.
+
+    Yields
+    ------
+    str
+        Single sentence extracted from text.
+
+    Example
+    -------
+    >>> text = "Does this text contains two sentences? Yes, it does."
+    >>> for sentence in get_sentences(text):
+    >>>     print(sentence)
+    Does this text contains two sentences?
+    Yes, it does.
+
+    """
     for match in RE_SENTENCE.finditer(text):
         yield match.group()
 
 
 def merge_syntactic_units(original_units, filtered_units, tags=None):
+    """Process given sentences and its filtered (tokenized) copies into
+    :class:`~gensim.summarization.syntactic_unit.SyntacticUnit`. Also adds tags if they are provided to produced units.
+
+    Parameters
+    ----------
+    original_units : list
+        List of original sentences.
+    filtered_units : list
+        List of tokenized sentences.
+    tags : list of str, optional
+        List of strings used as tags for each unit. None as deafault.
+
+    Returns
+    -------
+    list of :class:~gensim.summarization.syntactic_unit.SyntacticUnit
+        List of syntactic units (sentences).
+
+    """
     units = []
     for i in xrange(len(original_units)):
         if filtered_units[i] == '':
@@ -74,12 +208,38 @@ def merge_syntactic_units(original_units, filtered_units, tags=None):
 
 
 def join_words(words, separator=" "):
+    """Concatenates `words` with `separator` between elements.
+
+    Parameters
+    ----------
+    words : list of str
+        Given words.
+    separator : str, optional
+        The separator between elements.
+
+    Returns
+    -------
+    str
+        String of merged words with separator between elements.
+
+    """
     return separator.join(words)
 
 
 def clean_text_by_sentences(text):
-    """ Tokenizes a given text into sentences, applying filters and lemmatizing them.
-    Returns a SyntacticUnit list. """
+    """Tokenize a given text into sentences, applying filters and lemmatize them.
+
+    Parameters
+    ----------
+    text : str
+        Given text.
+
+    Returns
+    -------
+    list of :class:`~gensim.summarization.syntactic_unit.SyntacticUnit`
+        Sentences of the given text.
+
+    """
     original_sentences = split_sentences(text)
     filtered_sentences = [join_words(sentence) for sentence in preprocess_documents(original_sentences)]
 
@@ -87,8 +247,29 @@ def clean_text_by_sentences(text):
 
 
 def clean_text_by_word(text, deacc=True):
-    """ Tokenizes a given text into words, applying filters and lemmatizing them.
-    Returns a dict of word -> syntacticUnit. """
+    """Tokenize a given text into words, applying filters and lemmatize them.
+
+    Parameters
+    ----------
+    text : str
+        Given text.
+    deacc : bool, optional
+        Remove accentuation if True.
+
+    Returns
+    -------
+    dict
+        Words as keys, :class:`~gensim.summarization.syntactic_unit.SyntacticUnit` as values.
+
+    Example
+    -------
+    >>> from gensim.summarization.textcleaner import clean_text_by_word
+    >>> clean_text_by_word("God helps those who help themselves")
+    {'god': Original unit: 'god' *-*-*-* Processed unit: 'god',
+    'help': Original unit: 'help' *-*-*-* Processed unit: 'help',
+    'helps': Original unit: 'helps' *-*-*-* Processed unit: 'help'}
+
+    """
     text_without_acronyms = replace_with_separator(text, "", [AB_ACRONYM_LETTERS])
     original_words = list(tokenize(text_without_acronyms, to_lower=True, deacc=deacc))
     filtered_words = [join_words(word_list, "") for word_list in preprocess_documents(original_words)]
@@ -101,5 +282,30 @@ def clean_text_by_word(text, deacc=True):
 
 
 def tokenize_by_word(text):
+    """Tokenize input text. Before tokenizing transforms text to lower case and removes accentuation and acronyms set
+    :const:`~gensim.summarization.textcleaner.AB_ACRONYM_LETTERS`.
+
+    Parameters
+    ----------
+    text : str
+        Given text.
+
+    Returns
+    -------
+    generator
+        Generator that yields sequence words of the given text.
+
+    Example
+    -------
+    >>> from gensim.summarization.textcleaner import tokenize_by_word
+    >>> g = tokenize_by_word('Veni. Vedi. Vici.')
+    >>> print(next(g))
+    veni
+    >>> print(next(g))
+    vedi
+    >>> print(next(g))
+    vici
+
+    """
     text_without_acronyms = replace_with_separator(text, "", [AB_ACRONYM_LETTERS])
     return tokenize(text_without_acronyms, to_lower=True, deacc=True)
diff --git a/gensim/utils.py b/gensim/utils.py
index eb1cb6fff6..3f35824fed 100644
--- a/gensim/utils.py
+++ b/gensim/utils.py
@@ -4,9 +4,7 @@
 # Copyright (C) 2010 Radim Rehurek <radimrehurek@seznam.cz>
 # Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html
 
-"""
-This module contains various general utility functions.
-"""
+"""This module contains various general utility functions."""
 
 from __future__ import with_statement
 
@@ -43,50 +41,44 @@
 from six import iterkeys, iteritems, u, string_types, unichr
 from six.moves import xrange
 
+from smart_open import smart_open
+
 if sys.version_info[0] >= 3:
     unicode = str
 
 logger = logging.getLogger(__name__)
 
-try:
-    from smart_open import smart_open
-except ImportError:
-    logger.info("smart_open library not found; falling back to local-filesystem-only")
 
-    def make_closing(base, **attrs):
-        """
-        Add support for `with Base(attrs) as fout:` to the base class if it's missing.
-        The base class' `close()` method will be called on context exit, to always close the file properly.
+PAT_ALPHABETIC = re.compile(r'(((?![\d])\w)+)', re.UNICODE)
+RE_HTML_ENTITY = re.compile(r'&(#?)([xX]?)(\w{1,8});', re.UNICODE)
 
-        This is needed for gzip.GzipFile, bz2.BZ2File etc in older Pythons (<=2.6), which otherwise
-        raise "AttributeError: GzipFile instance has no attribute '__exit__'".
 
-        """
-        if not hasattr(base, '__enter__'):
-            attrs['__enter__'] = lambda self: self
-        if not hasattr(base, '__exit__'):
-            attrs['__exit__'] = lambda self, type, value, traceback: self.close()
-        return type('Closing' + base.__name__, (base, object), attrs)
-
-    def smart_open(fname, mode='rb'):
-        _, ext = os.path.splitext(fname)
-        if ext == '.bz2':
-            from bz2 import BZ2File
-            return make_closing(BZ2File)(fname, mode)
-        if ext == '.gz':
-            from gzip import GzipFile
-            return make_closing(GzipFile)(fname, mode)
-        return open(fname, mode)
+def get_random_state(seed):
+    """Generate :class:`numpy.random.RandomState` based on input seed.
 
+    Parameters
+    ----------
+    seed : {None, int, array_like}
+        Seed for random state.
 
-PAT_ALPHABETIC = re.compile(r'(((?![\d])\w)+)', re.UNICODE)
-RE_HTML_ENTITY = re.compile(r'&(#?)([xX]?)(\w{1,8});', re.UNICODE)
+    Returns
+    -------
+    :class:`numpy.random.RandomState`
+        Random state.
 
+    Raises
+    ------
+    AttributeError
+        If seed is not {None, int, array_like}.
+
+    Notes
+    -----
+    Method originally from [1]_ and written by @joshloyal.
+
+    References
+    ----------
+    .. [1] https://github.com/maciejkula/glove-python
 
-def get_random_state(seed):
-    """
-    Turn seed into a np.random.RandomState instance.
-    Method originally from maciejkula/glove-python, and written by @joshloyal.
     """
     if seed is None or seed is np.random:
         return np.random.mtrand._rand
@@ -98,10 +90,16 @@ def get_random_state(seed):
 
 
 def synchronous(tlockname):
-    """
-    A decorator to place an instance-based lock around a method.
+    """A decorator to place an instance-based lock around a method.
+
+    Notes
+    -----
+    Adapted from [2]_
+
+    References
+    ----------
+    .. [2] http://code.activestate.com/recipes/577105-synchronization-decorator-for-class-methods/
 
-    Adapted from http://code.activestate.com/recipes/577105-synchronization-decorator-for-class-methods/
     """
     def _synched(func):
         @wraps(func)
@@ -118,27 +116,18 @@ def _synchronizer(self, *args, **kwargs):
     return _synched
 
 
-class NoCM(object):
-    def acquire(self):
-        pass
-
-    def release(self):
-        pass
-
-    def __enter__(self):
-        pass
-
-    def __exit__(self, type, value, traceback):
-        pass
-
-
-nocm = NoCM()
+def file_or_filename(input):
+    """Open file with `smart_open`.
 
+    Parameters
+    ----------
+    input : str or file-like
+        Filename or file-like object.
 
-def file_or_filename(input):
-    """
-    Return a file-like object ready to be read from the beginning. `input` is either
-    a filename (gz/bz2 also supported) or a file-like object supporting seek.
+    Returns
+    -------
+    input : file-like object
+        Opened file OR seek out to 0 byte if `input` is already file-like object.
 
     """
     if isinstance(input, string_types):
@@ -151,11 +140,21 @@ def file_or_filename(input):
 
 
 def deaccent(text):
-    """
-    Remove accentuation from the given string. Input text is either a unicode string or utf8 encoded bytestring.
+    """Remove accentuation from the given string.
 
-    Return input string with accents removed, as unicode.
+    Parameters
+    ----------
+    text : str
+        Input string.
 
+    Returns
+    -------
+    str
+        Unicode string without accentuation.
+
+    Examples
+    --------
+    >>> from gensim.utils import deaccent
     >>> deaccent("Šéf chomutovských komunistů dostal poštou bílý prášek")
     u'Sef chomutovskych komunistu dostal postou bily prasek'
 
@@ -169,9 +168,19 @@ def deaccent(text):
 
 
 def copytree_hardlink(source, dest):
-    """
-    Recursively copy a directory ala shutils.copytree, but hardlink files
-    instead of copying. Available on UNIX systems only.
+    """Recursively copy a directory ala shutils.copytree, but hardlink files instead of copying.
+
+    Parameters
+    ----------
+    source : str
+        Path to source directory
+    dest : str
+        Path to destination directory
+
+    Warnings
+    --------
+    Available on UNIX systems only.
+
     """
     copy2 = shutil.copy2
     try:
@@ -182,17 +191,35 @@ def copytree_hardlink(source, dest):
 
 
 def tokenize(text, lowercase=False, deacc=False, encoding='utf8', errors="strict", to_lower=False, lower=False):
-    """
-    Iteratively yield tokens as unicode strings, removing accent marks
-    and optionally lowercasing the unidoce string by assigning True
-    to one of the parameters, lowercase, to_lower, or lower.
-
-    Input text may be either unicode or utf8-encoded byte string.
-
-    The tokens on output are maximal contiguous sequences of alphabetic
-    characters (no digits!).
-
-    >>> list(tokenize('Nic nemůže letět rychlostí vyšší, než 300 tisíc kilometrů za sekundu!', deacc = True))
+    """Iteratively yield tokens as unicode strings, removing accent marks and optionally lowercasing string
+    if any from `lowercase`, `to_lower`, `lower` set to True.
+
+    Parameters
+    ----------
+    text : str
+        Input string.
+    lowercase : bool, optional
+        If True - lowercase input string.
+    deacc : bool, optional
+        If True - remove accentuation from string by :func:`~gensim.utils.deaccent`.
+    encoding : str, optional
+        Encoding of input string, used as parameter for :func:`~gensim.utils.to_unicode`.
+    errors : str, optional
+        Error handling behaviour, used as parameter for :func:`~gensim.utils.to_unicode`.
+    to_lower : bool, optional
+        Same as `lowercase`.
+    lower : bool, optional
+        Same as `lowercase`.
+
+    Yields
+    ------
+    str
+        Contiguous sequences of alphabetic characters (no digits!), using :func:`~gensim.utils.simple_tokenize`
+
+    Examples
+    --------
+    >>> from gensim.utils import tokenize
+    >>> list(tokenize('Nic nemůže letět rychlostí vyšší, než 300 tisíc kilometrů za sekundu!', deacc=True))
     [u'Nic', u'nemuze', u'letet', u'rychlosti', u'vyssi', u'nez', u'tisic', u'kilometru', u'za', u'sekundu']
 
     """
@@ -206,16 +233,42 @@ def tokenize(text, lowercase=False, deacc=False, encoding='utf8', errors="strict
 
 
 def simple_tokenize(text):
+    """Tokenize input test using :const:`gensim.utils.PAT_ALPHABETIC`.
+
+    Parameters
+    ----------
+    text : str
+        Input text.
+
+    Yields
+    ------
+    str
+        Tokens from `text`.
+
+    """
     for match in PAT_ALPHABETIC.finditer(text):
         yield match.group()
 
 
 def simple_preprocess(doc, deacc=False, min_len=2, max_len=15):
-    """
-    Convert a document into a list of tokens.
-
-    This lowercases, tokenizes, de-accents (optional). -- the output are final
-    tokens = unicode strings, that won't be processed any further.
+    """Convert a document into a list of tokens (also with lowercase and optional de-accents),
+    used :func:`~gensim.utils.tokenize`.
+
+    Parameters
+    ----------
+    doc : str
+        Input document.
+    deacc : bool, optional
+        If True - remove accentuation from string by :func:`~gensim.utils.deaccent`.
+    min_len : int, optional
+        Minimal length of token in result (inclusive).
+    max_len : int, optional
+        Maximal length of token in result (inclusive).
+
+    Returns
+    -------
+    list of str
+        Tokens extracted from `doc`.
 
     """
     tokens = [
@@ -226,7 +279,24 @@ def simple_preprocess(doc, deacc=False, min_len=2, max_len=15):
 
 
 def any2utf8(text, errors='strict', encoding='utf8'):
-    """Convert a string (unicode or bytestring in `encoding`), to bytestring in utf8."""
+    """Convert `text` to bytestring in utf8.
+
+    Parameters
+    ----------
+    text : str
+        Input text.
+    errors : str, optional
+        Error handling behaviour, used as parameter for `unicode` function (python2 only).
+    encoding : str, optional
+        Encoding of `text` for `unicode` function (python2 only).
+
+    Returns
+    -------
+    str
+        Bytestring in utf8.
+
+    """
+
     if isinstance(text, unicode):
         return text.encode('utf8')
     # do bytestring -> unicode -> utf8 full circle, to ensure valid utf8
@@ -237,7 +307,23 @@ def any2utf8(text, errors='strict', encoding='utf8'):
 
 
 def any2unicode(text, encoding='utf8', errors='strict'):
-    """Convert a string (bytestring in `encoding` or unicode), to unicode."""
+    """Convert `text` to unicode.
+
+    Parameters
+    ----------
+    text : str
+        Input text.
+    errors : str, optional
+        Error handling behaviour, used as parameter for `unicode` function (python2 only).
+    encoding : str, optional
+        Encoding of `text` for `unicode` function (python2 only).
+
+    Returns
+    -------
+    str
+        Unicode version of `text`.
+
+    """
     if isinstance(text, unicode):
         return text
     return unicode(text, encoding, errors=errors)
@@ -247,31 +333,59 @@ def any2unicode(text, encoding='utf8', errors='strict'):
 
 
 def call_on_class_only(*args, **kwargs):
-    """Raise exception when load methods are called on instance"""
+    """Helper for raise `AttributeError` if method should be called from instance.
+
+    Parameters
+    ----------
+    *args
+        Variable length argument list.
+    **kwargs
+        Arbitrary keyword arguments.
+
+    Raises
+    ------
+    AttributeError
+        If `load` method are called on instance.
+
+    """
     raise AttributeError('This method should be called on a class object.')
 
 
 class SaveLoad(object):
-    """
-    Objects which inherit from this class have save/load functions, which un/pickle
-    them to disk.
+    """Class which inherit from this class have save/load functions, which un/pickle them to disk.
 
-    This uses pickle for de/serializing, so objects must not contain
-    unpicklable attributes, such as lambda functions etc.
+    Warnings
+    --------
+    This uses pickle for de/serializing, so objects must not contain unpicklable attributes,
+    such as lambda functions etc.
 
     """
     @classmethod
     def load(cls, fname, mmap=None):
-        """
-        Load a previously saved object from file (also see `save`).
-
-        If the object was saved with large arrays stored separately, you can load
-        these arrays via mmap (shared memory) using `mmap='r'`. Default: don't use
-        mmap, load large arrays as normal objects.
-
-        If the file being loaded is compressed (either '.gz' or '.bz2'), then
-        `mmap=None` must be set.  Load will raise an `IOError` if this condition
-        is encountered.
+        """Load a previously saved object (using :meth:`~gensim.utils.SaveLoad.save`) from file.
+
+        Parameters
+        ----------
+        fname : str
+            Path to file that contains needed object.
+        mmap : str, optional
+            Memory-map option.  If the object was saved with large arrays stored separately, you can load these arrays
+            via mmap (shared memory) using `mmap='r'.
+            If the file being loaded is compressed (either '.gz' or '.bz2'), then `mmap=None` **must be** set.
+
+        See Also
+        --------
+        :meth:`~gensim.utils.SaveLoad.save`
+
+        Returns
+        -------
+        object
+            Object loaded from `fname`.
+
+        Raises
+        ------
+        IOError
+            When methods are called on instance (should be called from class).
 
         """
         logger.info("loading %s object from %s", cls.__name__, fname)
@@ -284,9 +398,20 @@ def load(cls, fname, mmap=None):
         return obj
 
     def _load_specials(self, fname, mmap, compress, subname):
-        """
-        Loads any attributes that were stored specially, and gives the same
-        opportunity to recursively included SaveLoad instances.
+        """Loads any attributes that were stored specially, and gives the same opportunity
+        to recursively included :class:`~gensim.utils.SaveLoad` instances.
+
+        Parameters
+        ----------
+        fname : str
+            Path to file that contains needed object.
+        mmap : str
+            Memory-map option.
+        compress : bool
+            Set to True if file is compressed.
+        subname : str
+            ...
+
 
         """
         def mmap_error(obj, filename):
@@ -337,14 +462,40 @@ def mmap_error(obj, filename):
 
     @staticmethod
     def _adapt_by_suffix(fname):
-        """Give appropriate compress setting and filename formula"""
+        """Give appropriate compress setting and filename formula.
+
+        Parameters
+        ----------
+        fname : str
+            Input filename.
+
+        Returns
+        -------
+        (bool, function)
+            First argument will be True if `fname` compressed.
+
+        """
         compress, suffix = (True, 'npz') if fname.endswith('.gz') or fname.endswith('.bz2') else (False, 'npy')
         return compress, lambda *args: '.'.join(args + (suffix,))
 
     def _smart_save(self, fname, separately=None, sep_limit=10 * 1024**2, ignore=frozenset(), pickle_protocol=2):
-        """
-        Save the object to file (also see `load`).
-
+        """Save the object to file.
+
+        Parameters
+        ----------
+        fname : str
+            Path to file.
+        separately : list, optional
+            Iterable of attributes than need to store distinctly.
+        sep_limit : int, optional
+            Limit for separation.
+        ignore : frozenset, optional
+            Attributes that shouldn't be store.
+        pickle_protocol : int, optional
+            Protocol number for pickle.
+
+        Notes
+        -----
         If `separately` is None, automatically detect large
         numpy/scipy.sparse arrays in the object being stored, and store
         them into separate files. This avoids pickle memory errors and
@@ -354,12 +505,9 @@ def _smart_save(self, fname, separately=None, sep_limit=10 * 1024**2, ignore=fro
         a list of attribute names to be stored in separate files. The
         automatic check is not performed in this case.
 
-        `ignore` is a set of attribute names to *not* serialize (file
-        handles, caches etc). On subsequent load() these attributes will
-        be set to None.
-
-        `pickle_protocol` defaults to 2 so the pickled object can be imported
-        in both Python 2 and 3.
+        See Also
+        --------
+        :meth:`~gensim.utils.SaveLoad.load`
 
         """
         logger.info("saving %s object under %s, separately %s", self.__class__.__name__, fname, separately)
@@ -378,13 +526,31 @@ def _smart_save(self, fname, separately=None, sep_limit=10 * 1024**2, ignore=fro
         logger.info("saved %s", fname)
 
     def _save_specials(self, fname, separately, sep_limit, ignore, pickle_protocol, compress, subname):
-        """
-        Save aside any attributes that need to be handled separately, including
-        by recursion any attributes that are themselves SaveLoad instances.
-
-        Returns a list of (obj, {attrib: value, ...}) settings that the caller
-        should use to restore each object's attributes that were set aside
-        during the default pickle().
+        """Save aside any attributes that need to be handled separately, including
+        by recursion any attributes that are themselves :class:`~gensim.utils.SaveLoad` instances.
+
+        Parameters
+        ----------
+        fname : str
+            Output filename.
+        separately : list or None
+            Iterable of attributes than need to store distinctly
+        sep_limit : int
+            Limit for separation.
+        ignore : iterable of str
+            Attributes that shouldn't be store.
+        pickle_protocol : int
+            Protocol number for pickle.
+        compress : bool
+            If True - compress output with :func:`numpy.savez_compressed`.
+        subname : function
+            Produced by :meth:`~gensim.utils.SaveLoad._adapt_by_suffix`
+
+        Returns
+        -------
+        list of (obj, {attrib: value, ...})
+            Settings that the caller should use to restore each object's attributes that were set aside
+            during the default :func:`~gensim.utils.pickle`.
 
         """
         asides = {}
@@ -463,29 +629,29 @@ def _save_specials(self, fname, separately, sep_limit, ignore, pickle_protocol,
         return restores + [(self, asides)]
 
     def save(self, fname_or_handle, separately=None, sep_limit=10 * 1024**2, ignore=frozenset(), pickle_protocol=2):
-        """
-        Save the object to file (also see `load`).
-
-        `fname_or_handle` is either a string specifying the file name to
-        save to, or an open file-like object which can be written to. If
-        the object is a file handle, no special array handling will be
-        performed; all attributes will be saved to the same file.
-
-        If `separately` is None, automatically detect large
-        numpy/scipy.sparse arrays in the object being stored, and store
-        them into separate files. This avoids pickle memory errors and
-        allows mmap'ing large arrays back on load efficiently.
-
-        You can also set `separately` manually, in which case it must be
-        a list of attribute names to be stored in separate files. The
-        automatic check is not performed in this case.
-
-        `ignore` is a set of attribute names to *not* serialize (file
-        handles, caches etc). On subsequent load() these attributes will
-        be set to None.
-
-        `pickle_protocol` defaults to 2 so the pickled object can be imported
-        in both Python 2 and 3.
+        """Save the object to file.
+
+        Parameters
+        ----------
+        fname_or_handle : str or file-like
+            Path to output file or already opened file-like object. If the object is a file handle,
+            no special array handling will be performed, all attributes will be saved to the same file.
+        separately : list of str or None, optional
+            If None -  automatically detect large numpy/scipy.sparse arrays in the object being stored, and store
+            them into separate files. This avoids pickle memory errors and allows mmap'ing large arrays
+            back on load efficiently.
+            If list of str - this attributes will be stored in separate files, the automatic check
+            is not performed in this case.
+        sep_limit : int
+            Limit for automatic separation.
+        ignore : frozenset of str
+            Attributes that shouldn't be serialize/store.
+        pickle_protocol : int
+            Protocol number for pickle.
+
+        See Also
+        --------
+        :meth:`~gensim.utils.SaveLoad.load`
 
         """
         try:
@@ -496,15 +662,38 @@ def save(self, fname_or_handle, separately=None, sep_limit=10 * 1024**2, ignore=
 
 
 def identity(p):
-    """Identity fnc, for flows that don't accept lambda (pickling etc)."""
+    """Identity fnc, for flows that don't accept lambda (pickling etc).
+
+    Parameters
+    ----------
+    p : object
+        Input parameter.
+
+    Returns
+    -------
+    object
+        Same as `p`.
+
+    """
     return p
 
 
 def get_max_id(corpus):
-    """
-    Return the highest feature id that appears in the corpus.
+    """Get the highest feature id that appears in the corpus.
 
-    For empty corpora (no features at all), return -1.
+    Parameters
+    ----------
+    corpus : iterable of iterable of (int, int)
+        Collection of texts in BoW format.
+
+    Returns
+    ------
+    int
+        Highest feature id.
+
+    Notes
+    -----
+    For empty `corpus` return -1.
 
     """
     maxid = -1
@@ -514,16 +703,22 @@ def get_max_id(corpus):
 
 
 class FakeDict(object):
-    """
-    Objects of this class act as dictionaries that map integer->str(integer), for
-    a specified range of integers <0, num_terms).
+    """Objects of this class act as dictionaries that map integer->str(integer), for a specified
+    range of integers <0, num_terms).
 
-    This is meant to avoid allocating real dictionaries when `num_terms` is huge, which
-    is a waste of memory.
+    This is meant to avoid allocating real dictionaries when `num_terms` is huge, which is a waste of memory.
 
     """
 
     def __init__(self, num_terms):
+        """
+
+        Parameters
+        ----------
+        num_terms : int
+            Number of terms.
+
+        """
         self.num_terms = num_terms
 
     def __str__(self):
@@ -532,20 +727,34 @@ def __str__(self):
     def __getitem__(self, val):
         if 0 <= val < self.num_terms:
             return str(val)
-        raise ValueError("internal id out of bounds (%s, expected <0..%s))" %
-                         (val, self.num_terms))
+        raise ValueError("internal id out of bounds (%s, expected <0..%s))" % (val, self.num_terms))
 
     def iteritems(self):
+        """Iterate over all keys and values.
+
+
+        Yields
+        ------
+        (int, str)
+            Pair of (id, token).
+
+        """
         for i in xrange(self.num_terms):
             yield i, str(i)
 
     def keys(self):
-        """
-        Override the dict.keys() function, which is used to determine the maximum
-        internal id of a corpus = the vocabulary dimensionality.
+        """Override the `dict.keys()`, which is used to determine the maximum internal id of a corpus,
+        i.e. the vocabulary dimensionality.
 
-        HACK: To avoid materializing the whole `range(0, self.num_terms)`, this returns
-        the highest id = `[self.num_terms - 1]` only.
+        Returns
+        -------
+        list of int
+            Highest id, packed in list.
+
+        Warnings
+        --------
+        To avoid materializing the whole `range(0, self.num_terms)`,
+        this returns the highest id = `[self.num_terms - 1]` only.
 
         """
         return [self.num_terms - 1]
@@ -560,13 +769,24 @@ def get(self, val, default=None):
 
 
 def dict_from_corpus(corpus):
-    """
-    Scan corpus for all word ids that appear in it, then construct and return a mapping
-    which maps each `wordId -> str(wordId)`.
+    """Scan corpus for all word ids that appear in it, then construct a mapping
+    which maps each `word_id` -> `str(word_id)`.
+
+    Parameters
+    ----------
+    corpus : iterable of iterable of (int, int)
+        Collection of texts in BoW format.
 
-    This function is used whenever *words* need to be displayed (as opposed to just
-    their ids) but no wordId->word mapping was provided. The resulting mapping
-    only covers words actually used in the corpus, up to the highest wordId found.
+    Returns
+    ------
+    id2word : :class:`~gensim.utils.FakeDict`
+        "Fake" mapping which maps each `word_id` -> `str(word_id)`.
+
+    Warnings
+    --------
+    This function is used whenever *words* need to be displayed (as opposed to just their ids)
+    but no `word_id` -> `word` mapping was provided. The resulting mapping only covers words actually
+    used in the corpus, up to the highest `word_id` found.
 
     """
     num_terms = 1 + get_max_id(corpus)
@@ -575,16 +795,22 @@ def dict_from_corpus(corpus):
 
 
 def is_corpus(obj):
-    """
-    Check whether `obj` is a corpus. Return (is_corpus, new) 2-tuple, where
-    `new is obj` if `obj` was an iterable, or `new` yields the same sequence as
-    `obj` if it was an iterator.
+    """Check whether `obj` is a corpus.
 
-    `obj` is a corpus if it supports iteration over documents, where a document
-    is in turn anything that acts as a sequence of 2-tuples (int, float).
+    Parameters
+    ----------
+    obj : object
+        Something `iterable of iterable` that contains (int, int).
 
-    Note: An "empty" corpus (empty input sequence) is ambiguous, so in this case the
-    result is forcefully defined as `is_corpus=False`.
+    Return
+    ------
+    (bool, object)
+        Pair of (is_corpus, `obj`), is_corpus True if `obj` is corpus.
+
+    Warnings
+    --------
+    An "empty" corpus (empty input sequence) is ambiguous, so in this case
+    the result is forcefully defined as (False, `obj`).
 
     """
     try:
@@ -613,20 +839,25 @@ def is_corpus(obj):
 
 
 def get_my_ip():
-    """
-    Try to obtain our external ip (from the pyro nameserver's point of view)
+    """Try to obtain our external ip (from the Pyro4 nameserver's point of view)
 
-    This tries to sidestep the issue of bogus `/etc/hosts` entries and other
-    local misconfigurations, which often mess up hostname resolution.
+    Returns
+    -------
+    str
+        IP address.
 
+    Warnings
+    --------
+    This tries to sidestep the issue of bogus `/etc/hosts` entries and other local misconfiguration,
+    which often mess up hostname resolution.
     If all else fails, fall back to simple `socket.gethostbyname()` lookup.
 
     """
     import socket
     try:
-        import Pyro4
+        from Pyro4.naming import locateNS
         # we know the nameserver must exist, so use it as our anchor point
-        ns = Pyro4.naming.locateNS()
+        ns = locateNS()
         s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
         s.connect((ns._pyroUri.host, ns._pyroUri.port))
         result, port = s.getsockname()
@@ -644,21 +875,29 @@ def get_my_ip():
 
 
 class RepeatCorpus(SaveLoad):
-    """
-    Used in the tutorial on distributed computing and likely not useful anywhere else.
+    """Wrap a `corpus` as another corpus of length `reps`. This is achieved by repeating documents from `corpus`
+    over and over again, until the requested length `len(result) == reps` is reached.
+    Repetition is done on-the-fly=efficiently, via `itertools`.
+
+    Examples
+    --------
+    >>> from gensim.utils import RepeatCorpus
+    >>>
+    >>> corpus = [[(1, 2)], []] # 2 documents
+    >>> list(RepeatCorpus(corpus, 5)) # repeat 2.5 times to get 5 documents
+    [[(1, 2)], [], [(1, 2)], [], [(1, 2)]]
 
     """
 
     def __init__(self, corpus, reps):
         """
-        Wrap a `corpus` as another corpus of length `reps`. This is achieved by
-        repeating documents from `corpus` over and over again, until the requested
-        length `len(result)==reps` is reached. Repetition is done
-        on-the-fly=efficiently, via `itertools`.
 
-        >>> corpus = [[(1, 0.5)], []] # 2 documents
-        >>> list(RepeatCorpus(corpus, 5)) # repeat 2.5 times to get 5 documents
-        [[(1, 0.5)], [], [(1, 0.5)], [], [(1, 0.5)]]
+        Parameters
+        ----------
+        corpus : iterable of iterable of (int, int)
+            Input corpus.
+        reps : int
+            Number of repeats for documents from corpus.
 
         """
         self.corpus = corpus
@@ -669,14 +908,28 @@ def __iter__(self):
 
 
 class RepeatCorpusNTimes(SaveLoad):
+    """Wrap a `corpus` and repeat it `n` times.
+
+    Examples
+    --------
+    >>> from gensim.utils import RepeatCorpusNTimes
+    >>>
+    >>> corpus = [[(1, 0.5)], []]
+    >>> list(RepeatCorpusNTimes(corpus, 3)) # repeat 3 times
+    [[(1, 0.5)], [], [(1, 0.5)], [], [(1, 0.5)], []]
+
+    """
 
     def __init__(self, corpus, n):
         """
-        Repeat a `corpus` `n` times.
 
-        >>> corpus = [[(1, 0.5)], []]
-        >>> list(RepeatCorpusNTimes(corpus, 3)) # repeat 3 times
-        [[(1, 0.5)], [], [(1, 0.5)], [], [(1, 0.5)], []]
+        Parameters
+        ----------
+        corpus : iterable of iterable of (int, int)
+            Input corpus.
+        n : int
+            Number of repeats for corpus.
+
         """
         self.corpus = corpus
         self.n = n
@@ -688,13 +941,22 @@ def __iter__(self):
 
 
 class ClippedCorpus(SaveLoad):
+    """Wrap a `corpus` and return `max_doc` element from it"""
+
     def __init__(self, corpus, max_docs=None):
         """
-        Return a corpus that is the "head" of input iterable `corpus`.
 
-        Any documents after `max_docs` are ignored. This effectively limits the
-        length of the returned corpus to <= `max_docs`. Set `max_docs=None` for
-        "no limit", effectively wrapping the entire input corpus.
+        Parameters
+        ----------
+        corpus : iterable of iterable of (int, int)
+            Input corpus.
+        max_docs : int
+            Maximal number of documents in result corpus.
+
+        Warnings
+        --------
+        Any documents after `max_docs` are ignored. This effectively limits the length of the returned corpus
+        to <= `max_docs`. Set `max_docs=None` for "no limit", effectively wrapping the entire input corpus.
 
         """
         self.corpus = corpus
@@ -708,19 +970,26 @@ def __len__(self):
 
 
 class SlicedCorpus(SaveLoad):
+    """Wrap `corpus` and return the slice of it"""
+
     def __init__(self, corpus, slice_):
         """
-        Return a corpus that is the slice of input iterable `corpus`.
 
-        Negative slicing can only be used if the corpus is indexable.
-        Otherwise, the corpus will be iterated over.
+        Parameters
+        ----------
+        corpus : iterable of iterable of (int, int)
+            Input corpus.
+        slice_ : slice or iterable
+            Slice for `corpus`
 
+        Notes
+        -----
+        Negative slicing can only be used if the corpus is indexable, otherwise, the corpus will be iterated over.
         Slice can also be a np.ndarray to support fancy indexing.
 
-        NOTE: calculating the size of a SlicedCorpus is expensive
-        when using a slice as the corpus has to be iterated over once.
-        Using a list or np.ndarray does not have this drawback, but
-        consumes more memory.
+        Calculating the size of a SlicedCorpus is expensive when using a slice as the corpus has
+        to be iterated over once. Using a list or np.ndarray does not have this drawback, but consumes more memory.
+
         """
         self.corpus = corpus
         self.slice_ = slice_
@@ -747,6 +1016,19 @@ def __len__(self):
 
 
 def safe_unichr(intval):
+    """
+
+    Parameters
+    ----------
+    intval : int
+        Integer code of character
+
+    Returns
+    -------
+    string
+        Unicode string of character
+
+    """
     try:
         return unichr(intval)
     except ValueError:
@@ -757,12 +1039,18 @@ def safe_unichr(intval):
 
 
 def decode_htmlentities(text):
-    """
-    Decode HTML entities in text, coded as hex, decimal or named.
-
-    Adapted
-    from http://github.com/sku/python-twitter-ircbot/blob/321d94e0e40d0acc92f5bf57d126b57369da70de/html_decode.py
-
+    """Decode HTML entities in text, coded as hex, decimal or named.
+    This function from [3]_.
+
+    Parameters
+    ----------
+    text : str
+        Input html text.
+
+    Examples
+    --------
+    >>> from gensim.utils import decode_htmlentities
+    >>>
     >>> u = u'E tu vivrai nel terrore - L&#x27;aldil&#xE0; (1981)'
     >>> print(decode_htmlentities(u).encode('UTF-8'))
     E tu vivrai nel terrore - L'aldilà (1981)
@@ -771,6 +1059,10 @@ def decode_htmlentities(text):
     >>> print(decode_htmlentities("foo &lt; bar"))
     foo < bar
 
+    References
+    ----------
+    .. [3] http://github.com/sku/python-twitter-ircbot/blob/321d94e0e40d0acc92f5bf57d126b57369da70de/html_decode.py
+
     """
     def substitute_entity(match):
         try:
@@ -798,10 +1090,25 @@ def substitute_entity(match):
 
 
 def chunkize_serial(iterable, chunksize, as_numpy=False):
-    """
-    Return elements from the iterable in `chunksize`-ed lists. The last returned
-    element may be smaller (if length of collection is not divisible by `chunksize`).
-
+    """Give elements from the iterable in `chunksize`-ed lists.
+    The last returned element may be smaller (if length of collection is not divisible by `chunksize`).
+
+    Parameters
+    ----------
+    iterable : iterable of object
+        Any iterable.
+    chunksize : int
+        Size of chunk from result.
+    as_numpy : bool, optional
+        If True - yield `np.ndarray`, otherwise - list
+
+    Yields
+    ------
+    list of object OR np.ndarray
+        Groups based on `iterable`
+
+    Examples
+    --------
     >>> print(list(grouper(range(10), 3)))
     [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]]
 
@@ -860,29 +1167,58 @@ def run(self):
     warnings.warn("detected Windows; aliasing chunkize to chunkize_serial")
 
     def chunkize(corpus, chunksize, maxsize=0, as_numpy=False):
+        """Split `corpus` into smaller chunks, used :func:`~gensim.utils.chunkize_serial`.
+
+        Parameters
+        ----------
+        corpus : iterable of object
+            Any iterable object.
+        chunksize : int
+            Size of chunk from result.
+        maxsize : int, optional
+            THIS PARAMETER IGNORED.
+        as_numpy : bool, optional
+            If True - yield `np.ndarray`, otherwise - list
+
+        Yields
+        ------
+        list of object OR np.ndarray
+            Groups based on `iterable`
+
+        """
         for chunk in chunkize_serial(corpus, chunksize, as_numpy=as_numpy):
             yield chunk
 else:
     def chunkize(corpus, chunksize, maxsize=0, as_numpy=False):
-        """
-        Split a stream of values into smaller chunks.
+        """Split `corpus` into smaller chunks, used :func:`~gensim.utils.chunkize_serial`.
+
+        Parameters
+        ----------
+        corpus : iterable of object
+            Any iterable object.
+        chunksize : int
+            Size of chunk from result.
+        maxsize : int, optional
+            THIS PARAMETER IGNORED.
+        as_numpy : bool, optional
+            If True - yield `np.ndarray`, otherwise - list
+
+        Notes
+        -----
         Each chunk is of length `chunksize`, except the last one which may be smaller.
-        A once-only input stream (`corpus` from a generator) is ok, chunking is done
-        efficiently via itertools.
+        A once-only input stream (`corpus` from a generator) is ok, chunking is done efficiently via itertools.
 
-        If `maxsize > 1`, don't wait idly in between successive chunk `yields`, but
-        rather keep filling a short queue (of size at most `maxsize`) with forthcoming
-        chunks in advance. This is realized by starting a separate process, and is
-        meant to reduce I/O delays, which can be significant when `corpus` comes
-        from a slow medium (like harddisk).
+        If `maxsize > 1`, don't wait idly in between successive chunk `yields`, but rather keep filling a short queue
+        (of size at most `maxsize`) with forthcoming chunks in advance. This is realized by starting a separate process,
+        and is meant to reduce I/O delays, which can be significant when `corpus` comes from a slow medium (like HDD).
 
-        If `maxsize==0`, don't fool around with parallelism and simply yield the chunksize
-        via `chunkize_serial()` (no I/O optimizations).
+        If `maxsize == 0`, don't fool around with parallelism and simply yield the chunksize
+        via :func:`~gensim.utils.chunkize_serial` (no I/O optimizations).
 
-        >>> for chunk in chunkize(range(10), 4): print(chunk)
-        [0, 1, 2, 3]
-        [4, 5, 6, 7]
-        [8, 9]
+        Yields
+        ------
+        list of object OR np.ndarray
+            Groups based on `iterable`
 
         """
         assert chunksize > 0
@@ -903,6 +1239,21 @@ def chunkize(corpus, chunksize, maxsize=0, as_numpy=False):
 
 
 def smart_extension(fname, ext):
+    """Generate filename with `ext`.
+
+    Parameters
+    ----------
+    fname : str
+        Path to file.
+    ext : str
+        File extension.
+
+    Returns
+    -------
+    str
+        New path to file with `ext`.
+
+    """
     fname, oext = os.path.splitext(fname)
     if oext.endswith('.bz2'):
         fname = fname + oext[:-4] + ext + '.bz2'
@@ -917,8 +1268,14 @@ def smart_extension(fname, ext):
 def pickle(obj, fname, protocol=2):
     """Pickle object `obj` to file `fname`.
 
-    `protocol` defaults to 2 so pickled objects are compatible across
-    Python 2.x and 3.x.
+    Parameters
+    ----------
+    obj : object
+        Any python object.
+    fname : str
+        Path to pickle file.
+    protocol : int, optional
+        Pickle protocol number, default is 2 to support compatible across python 2.x and 3.x.
 
     """
     with smart_open(fname, 'wb') as fout:  # 'b' for binary, needed on Windows
@@ -926,7 +1283,19 @@ def pickle(obj, fname, protocol=2):
 
 
 def unpickle(fname):
-    """Load pickled object from `fname`"""
+    """Load object from `fname`.
+
+    Parameters
+    ----------
+    fname : str
+        Path to pickle file.
+
+    Returns
+    -------
+    object
+        Python object loaded from `fname`.
+
+    """
     with smart_open(fname, 'rb') as f:
         # Because of loading from S3 load can't be used (missing readline in smart_open)
         if sys.version_info > (3, 0):
@@ -936,25 +1305,109 @@ def unpickle(fname):
 
 
 def revdict(d):
-    """
-    Reverse a dictionary mapping.
+    """Reverse a dictionary mapping, i.e. `{1: 2, 3: 4}` -> `{2: 1, 4: 3}`.
+
+    Parameters
+    ----------
+    d : dict
+        Input dictionary.
+
+    Returns
+    -------
+    dict
+        Reversed dictionary mapping.
+
+    Notes
+    -----
+    When two keys map to the same value, only one of them will be kept in the result (which one is kept is arbitrary).
 
-    When two keys map to the same value, only one of them will be kept in the
-    result (which one is kept is arbitrary).
+    Examples
+    --------
+    >>> from gensim.utils import revdict
+    >>> d = {1: 2, 3: 4}
+    >>> revdict(d)
+    {2: 1, 4: 3}
 
     """
     return {v: k for (k, v) in iteritems(dict(d))}
 
 
+def deprecated(reason):
+    """Decorator which can be used to mark functions as deprecated.
+
+    Parameters
+    ----------
+    reason : str
+        Reason of deprecation.
+
+    Returns
+    -------
+    function
+        Decorated function
+
+    Notes
+    -----
+    It will result in a warning being emitted when the function is used, base code from [4]_.
+
+    References
+    ----------
+    .. [4] https://stackoverflow.com/a/40301488/8001386
+
+    """
+    if isinstance(reason, string_types):
+        def decorator(func):
+            fmt = "Call to deprecated `{name}` ({reason})."
+
+            @wraps(func)
+            def new_func1(*args, **kwargs):
+                warnings.warn(
+                    fmt.format(name=func.__name__, reason=reason),
+                    category=DeprecationWarning,
+                    stacklevel=2
+                )
+                return func(*args, **kwargs)
+
+            return new_func1
+        return decorator
+
+    elif inspect.isclass(reason) or inspect.isfunction(reason):
+        func = reason
+        fmt = "Call to deprecated `{name}`."
+
+        @wraps(func)
+        def new_func2(*args, **kwargs):
+            warnings.warn(
+                fmt.format(name=func.__name__),
+                category=DeprecationWarning,
+                stacklevel=2
+            )
+            return func(*args, **kwargs)
+        return new_func2
+
+    else:
+        raise TypeError(repr(type(reason)))
+
+
+@deprecated("Function will be removed in 4.0.0")
 def toptexts(query, texts, index, n=10):
     """
     Debug fnc to help inspect the top `n` most similar documents (according to a
     similarity index `index`), to see if they are actually related to the query.
 
-    `texts` is any object that can return something insightful for each document
-    via `texts[docid]`, such as its fulltext or snippet.
-
-    Return a list of 3-tuples (docid, doc's similarity to the query, texts[docid]).
+    Parameters
+    ----------
+    query : list
+        vector OR BoW (list of tuples)
+    texts : str
+        object that can return something insightful for each document via `texts[docid]`,
+        such as its fulltext or snippet.
+    index : any
+        a class from gensim.similarity.docsim
+
+    Return
+    ------
+    list
+        a list of 3-tuples (docid, doc's similarity to the query, texts[docid])
 
     """
     sims = index[query]  # perform a similarity query against the corpus
@@ -964,18 +1417,31 @@ def toptexts(query, texts, index, n=10):
 
 
 def randfname(prefix='gensim'):
+    """Generate path with random filename/
+
+    Parameters
+    ----------
+    prefix : str
+        Prefix of filename.
+
+    Returns
+    -------
+    str
+        Full path with random filename (in temporary folder).
+
+    """
     randpart = hex(random.randint(0, 0xffffff))[2:]
     return os.path.join(tempfile.gettempdir(), prefix + randpart)
 
 
+@deprecated("Function will be removed in 4.0.0")
 def upload_chunked(server, docs, chunksize=1000, preprocess=None):
-    """
-    Memory-friendly upload of documents to a SimServer (or Pyro SimServer proxy).
-
+    """Memory-friendly upload of documents to a SimServer (or Pyro SimServer proxy).
+    Notes
+    -----
     Use this function to train or index large collections -- avoid sending the
     entire corpus over the wire as a single Pyro in-memory object. The documents
     will be sent in smaller chunks, of `chunksize` documents each.
-
     """
     start = 0
     for chunk in grouper(docs, chunksize):
@@ -993,8 +1459,29 @@ def upload_chunked(server, docs, chunksize=1000, preprocess=None):
 
 
 def getNS(host=None, port=None, broadcast=True, hmac_key=None):
-    """
-    Return a Pyro name server proxy.
+    """Get a Pyro4 name server proxy.
+
+    Parameters
+    ----------
+    host : str, optional
+        Hostname of ns.
+    port : int, optional
+        Port of ns.
+    broadcast : bool, optional
+        If True - use broadcast mechanism (i.e. all Pyro nodes in local network), not otherwise.
+    hmac_key : str, optional
+        Private key.
+
+    Raises
+    ------
+    RuntimeError
+        when Pyro name server is not found
+
+    Returns
+    -------
+    :class:`Pyro4.core.Proxy`
+        Proxy from Pyro4.
+
     """
     import Pyro4
     try:
@@ -1004,8 +1491,7 @@ def getNS(host=None, port=None, broadcast=True, hmac_key=None):
 
 
 def pyro_daemon(name, obj, random_suffix=False, ip=None, port=None, ns_conf=None):
-    """
-    Register object with name server (starting the name server if not running
+    """Register object with name server (starting the name server if not running
     yet) and block until the daemon is terminated. The object is registered under
     `name`, or `name`+ some random suffix if `random_suffix` is set.
 
@@ -1027,8 +1513,17 @@ def pyro_daemon(name, obj, random_suffix=False, ip=None, port=None, ns_conf=None
 
 
 def has_pattern():
-    """
-    Function which returns a flag indicating whether pattern is installed or not
+    """Check that `pattern` [5]_ package already installed.
+
+    Returns
+    -------
+    bool
+        True if `pattern` installed, False otherwise.
+
+    References
+    ----------
+    .. [5] https://github.com/clips/pattern
+
     """
     try:
         from pattern.en import parse  # noqa:F401
@@ -1039,21 +1534,42 @@ def has_pattern():
 
 def lemmatize(content, allowed_tags=re.compile(r'(NN|VB|JJ|RB)'), light=False,
               stopwords=frozenset(), min_length=2, max_length=15):
-    """
-    This function is only available when the optional 'pattern' package is installed.
-
-    Use the English lemmatizer from `pattern` to extract UTF8-encoded tokens in
+    """Use the English lemmatizer from `pattern` [5]_ to extract UTF8-encoded tokens in
     their base form=lemma, e.g. "are, is, being" -> "be" etc.
     This is a smarter version of stemming, taking word context into account.
 
-    Only considers nouns, verbs, adjectives and adverbs by default (=all other lemmas are discarded).
+    Parameters
+    ----------
+    content : str
+        Input string
+    allowed_tags : :class:`_sre.SRE_Pattern`, optional
+        Compiled regexp to select POS that will be used.
+        Only considers nouns, verbs, adjectives and adverbs by default (=all other lemmas are discarded).
+    light : bool, optional
+        DEPRECATED FLAG, DOESN'T SUPPORT BY `pattern`.
+    stopwords : frozenset
+        Set of words that will be removed from output.
+    min_length : int
+        Minimal token length in output (inclusive).
+    max_length : int
+        Maximal token length in output (inclusive).
+
+    Returns
+    -------
+    list of str
+        List with tokens with POS tag.
+
+    Warnings
+    --------
+    This function is only available when the optional 'pattern' package is installed.
 
+    Examples
+    --------
+    >>> from gensim.utils import lemmatize
     >>> lemmatize('Hello World! How is it going?! Nonexistentword, 21')
     ['world/NN', 'be/VB', 'go/VB', 'nonexistentword/NN']
-
     >>> lemmatize('The study ranks high.')
     ['study/NN', 'rank/VB', 'high/JJ']
-
     >>> lemmatize('The ranks study hard.')
     ['rank/NN', 'study/VB', 'hard/RB']
 
@@ -1086,10 +1602,21 @@ def lemmatize(content, allowed_tags=re.compile(r'(NN|VB|JJ|RB)'), light=False,
 
 
 def mock_data_row(dim=1000, prob_nnz=0.5, lam=1.0):
-    """
-    Create a random gensim sparse vector. Each coordinate is nonzero with
-    probability `prob_nnz`, each non-zero coordinate value is drawn from
-    a Poisson distribution with parameter lambda equal to `lam`.
+    """Create a random gensim BoW vector.
+
+    Parameters
+    ----------
+    dim : int, optional
+        Dimension of vector.
+    prob_nnz : float, optional
+        Probability of each coordinate will be nonzero, will be drawn from Poisson distribution.
+    lam : float, optional
+        Parameter for Poisson distribution.
+
+    Returns
+    -------
+    list of (int, float)
+        Vector in BoW format.
 
     """
     nnz = np.random.uniform(size=(dim,))
@@ -1097,19 +1624,46 @@ def mock_data_row(dim=1000, prob_nnz=0.5, lam=1.0):
 
 
 def mock_data(n_items=1000, dim=1000, prob_nnz=0.5, lam=1.0):
-    """
-    Create a random gensim-style corpus, as a list of lists of (int, float) tuples,
-    to be used as a mock corpus.
+    """Create a random gensim-style corpus (BoW), used :func:`~gensim.utils.mock_data_row`.
+
+    Parameters
+    ----------
+    n_items : int
+        Size of corpus
+    dim : int
+        Dimension of vector, used for :func:`~gensim.utils.mock_data_row`.
+    prob_nnz : float, optional
+        Probability of each coordinate will be nonzero, will be drawn from Poisson distribution,
+        used for :func:`~gensim.utils.mock_data_row`.
+    lam : float, optional
+        Parameter for Poisson distribution, used for :func:`~gensim.utils.mock_data_row`.
+
+    Returns
+    -------
+    list of list of (int, float)
+        Gensim-style corpus.
 
     """
     return [mock_data_row(dim=dim, prob_nnz=prob_nnz, lam=lam) for _ in xrange(n_items)]
 
 
 def prune_vocab(vocab, min_reduce, trim_rule=None):
-    """
-    Remove all entries from the `vocab` dictionary with count smaller than `min_reduce`.
+    """Remove all entries from the `vocab` dictionary with count smaller than `min_reduce`.
 
     Modifies `vocab` in place, returns the sum of all counts that were pruned.
+    Parameters
+    ----------
+    vocab : dict
+        Input dictionary.
+    min_reduce : int
+        Frequency threshold for tokens in `vocab`.
+    trim_rule : function, optional
+        Function for trimming entities from vocab, default behaviour is `vocab[w] <= min_reduce`.
+
+    Returns
+    -------
+    result : int
+        Sum of all counts that were pruned.
 
     """
     result = 0
@@ -1126,7 +1680,19 @@ def prune_vocab(vocab, min_reduce, trim_rule=None):
 
 
 def qsize(queue):
-    """Return the (approximate) queue size where available; -1 where not (OS X)."""
+    """Get the (approximate) queue size where available.
+
+    Parameters
+    ----------
+    queue : :class:`queue.Queue`
+        Input queue.
+
+    Returns
+    -------
+    int
+        Queue size, -1 if `qsize` method isn't implemented (OS X).
+
+    """
     try:
         return queue.qsize()
     except NotImplementedError:
@@ -1140,6 +1706,25 @@ def qsize(queue):
 
 
 def keep_vocab_item(word, count, min_count, trim_rule=None):
+    """Check that should we keep `word` in vocab or remove.
+
+    Parameters
+    ----------
+    word : str
+        Input word.
+    count : int
+        Number of times that word contains in corpus.
+    min_count : int
+        Frequency threshold for `word`.
+    trim_rule : function, optional
+        Function for trimming entities from vocab, default behaviour is `vocab[w] <= min_reduce`.
+
+    Returns
+    -------
+    bool
+        True if `word` should stay, False otherwise.
+
+    """
     default_res = count >= min_count
 
     if trim_rule is None:
@@ -1155,13 +1740,27 @@ def keep_vocab_item(word, count, min_count, trim_rule=None):
 
 
 def check_output(stdout=subprocess.PIPE, *popenargs, **kwargs):
-    """
-    Run command with arguments and return its output as a byte string.
-    Backported from Python 2.7 as it's implemented as pure python on stdlib.
+    r"""Run command with arguments and return its output as a byte string.
+    Backported from Python 2.7 as it's implemented as pure python on stdlib + small modification.
+    Widely used for :mod:`gensim.models.wrappers`.
+
+    Very similar with [6]_
+
+    Examples
+    --------
+    >>> from gensim.utils import check_output
+    >>> check_output(args=['echo', '1'])
+    '1\n'
+
+    Raises
+    ------
+    KeyboardInterrupt
+        If Ctrl+C pressed.
+
+    References
+    ----------
+    .. [6] https://docs.python.org/2/library/subprocess.html#subprocess.check_output
 
-    >>> check_output(args=['/usr/bin/python', '--version'])
-    Python 2.6.2
-    Added extra KeyboardInterrupt handling
     """
     try:
         logger.debug("COMMAND: %s %s", popenargs, kwargs)
@@ -1182,19 +1781,47 @@ def check_output(stdout=subprocess.PIPE, *popenargs, **kwargs):
 
 
 def sample_dict(d, n=10, use_random=True):
-    """
-    Pick `n` items from dictionary `d` and return them as a list.
-    The items are picked randomly if `use_random` is True, otherwise picked
-    according to natural dict iteration.
+    """Pick `n` items from dictionary `d`.
+
+    Parameters
+    ----------
+    d : dict
+        Input dictionary.
+    n : int, optional
+        Number of items that will be picked.
+    use_random : bool, optional
+        If True - pick items randomly, otherwise - according to natural dict iteration.
+
+    Returns
+    -------
+    list of (object, object)
+        Picked items from dictionary, represented as list.
+
     """
     selected_keys = random.sample(list(d), min(len(d), n)) if use_random else itertools.islice(iterkeys(d), n)
     return [(key, d[key]) for key in selected_keys]
 
 
 def strided_windows(ndarray, window_size):
-    """
-    Produce a numpy.ndarray of windows, as from a sliding window.
-
+    """Produce a numpy.ndarray of windows, as from a sliding window.
+
+    Parameters
+    ----------
+    ndarray : numpy.ndarray
+        Input array
+    window_size : int
+        Sliding window size.
+
+    Returns
+    -------
+    numpy.ndarray
+        Subsequences produced by sliding a window of the given size over the `ndarray`.
+        Since this uses striding, the individual arrays are views rather than copies of `ndarray`.
+        Changes to one view modifies the others and the original.
+
+    Examples
+    --------
+    >>> from gensim.utils import strided_windows
     >>> strided_windows(np.arange(5), 2)
     array([[0, 1],
            [1, 2],
@@ -1208,14 +1835,6 @@ def strided_windows(ndarray, window_size):
            [4, 5, 6, 7, 8],
            [5, 6, 7, 8, 9]])
 
-    Args:
-        ndarray: either a numpy.ndarray or something that can be converted into one.
-        window_size: sliding window size.
-
-    Returns:
-        numpy.ndarray of the subsequences produced by sliding a window of the given size over
-        the `ndarray`. Since this uses striding, the individual arrays are views rather than
-        copies of `ndarray`. Changes to one view modifies the others and the original.
     """
     ndarray = np.asarray(ndarray)
     if window_size == ndarray.shape[0]:
@@ -1231,15 +1850,22 @@ def strided_windows(ndarray, window_size):
 
 def iter_windows(texts, window_size, copy=False, ignore_below_size=True, include_doc_num=False):
     """Produce a generator over the given texts using a sliding window of `window_size`.
-    The windows produced are views of some subsequence of a text. To use deep copies
-    instead, pass `copy=True`.
-
-    Args:
-        texts: List of string sentences.
-        window_size: Size of sliding window.
-        copy: False to use views of the texts (default) or True to produce deep copies.
-        ignore_below_size: ignore documents that are not at least `window_size` in length (default behavior).
-            If False, the documents below `window_size` will be yielded as the full document.
+    The windows produced are views of some subsequence of a text.
+    To use deep copies instead, pass `copy=True`.
+
+
+    Parameters
+    ----------
+    texts : list of str
+        List of string sentences.
+    window_size : int
+        Size of sliding window.
+    copy : bool, optional
+        If True - produce deep copies.
+    ignore_below_size : bool, optional
+        If True - ignore documents that are not at least `window_size` in length.
+    include_doc_num : bool, optional
+        If True - will be yield doc_num too.
 
     """
     for doc_num, document in enumerate(texts):
@@ -1263,60 +1889,38 @@ def _iter_windows(document, window_size, copy=False, ignore_below_size=True):
 def flatten(nested_list):
     """Recursively flatten out a nested list.
 
-    Args:
-        nested_list (list): possibly nested list.
+    Parameters
+    ----------
+    nested_list : list
+        Possibly nested list.
+
+    Returns
+    -------
+    list
+        Flattened version of input, where any list elements have been unpacked into the top-level list
+        in a recursive fashion.
 
-    Returns:
-        list: flattened version of input, where any list elements have been unpacked into the
-            top-level list in a recursive fashion.
     """
     return list(lazy_flatten(nested_list))
 
 
 def lazy_flatten(nested_list):
-    """Lazy version of `flatten`."""
+    """Lazy version of :func:`~gensim.utils.flatten`.
+
+    Parameters
+    ----------
+    nested_list : list
+        Possibly nested list.
+
+    Yields
+    ------
+    object
+        Element of list
+
+    """
     for el in nested_list:
         if isinstance(el, collections.Iterable) and not isinstance(el, string_types):
             for sub in flatten(el):
                 yield sub
         else:
             yield el
-
-
-def deprecated(reason):
-    """Decorator which can be used to mark functions as deprecated. It will result in a warning being emitted
-    when the function is used, base code from https://stackoverflow.com/a/40301488/8001386.
-
-    """
-    if isinstance(reason, string_types):
-        def decorator(func):
-            fmt = "Call to deprecated `{name}` ({reason})."
-
-            @wraps(func)
-            def new_func1(*args, **kwargs):
-                warnings.warn(
-                    fmt.format(name=func.__name__, reason=reason),
-                    category=DeprecationWarning,
-                    stacklevel=2
-                )
-                return func(*args, **kwargs)
-
-            return new_func1
-        return decorator
-
-    elif inspect.isclass(reason) or inspect.isfunction(reason):
-        func = reason
-        fmt = "Call to deprecated `{name}`."
-
-        @wraps(func)
-        def new_func2(*args, **kwargs):
-            warnings.warn(
-                fmt.format(name=func.__name__),
-                category=DeprecationWarning,
-                stacklevel=2
-            )
-            return func(*args, **kwargs)
-        return new_func2
-
-    else:
-        raise TypeError(repr(type(reason)))
diff --git a/setup.cfg b/setup.cfg
index 282192a170..f35f138329 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -1,4 +1,4 @@
 [wheelhouse_uploader]
 artifact_indexes=
     # all wheels builded in gensim-wheels repo: https://github.com/MacPython/gensim-wheels
-    http://b153eb958f4da6029aca-3f9dff7fe564350f10153d8c7bfc5ab6.r54.cf2.rackcdn.com/index.html
+    http://b153eb958f4da6029aca-3f9dff7fe564350f10153d8c7bfc5ab6.r54.cf2.rackcdn.com/
diff --git a/setup.py b/setup.py
index a5d8fa6bdc..2c16ded0f7 100644
--- a/setup.py
+++ b/setup.py
@@ -307,7 +307,7 @@ def finalize_options(self):
         'distributed': distributed_env,
         'test-win': win_testenv,
         'test': linux_testenv,
-        'docs': linux_testenv + distributed_env + ['sphinx', 'sphinxcontrib-napoleon', 'plotly'],
+        'docs': linux_testenv + distributed_env + ['sphinx', 'sphinxcontrib-napoleon', 'plotly', 'pattern'],
     },
 
     include_package_data=True,
diff --git a/tox.ini b/tox.ini
index 229692fbee..d8cf92ed69 100644
--- a/tox.ini
+++ b/tox.ini
@@ -13,7 +13,7 @@ builtins = get_ipython
 
 
 [pytest]
-addopts = -rfxEXs --durations=20 --showlocals --rerun 3
+addopts = -rfxEXs --durations=20 --showlocals --reruns 3 --reruns-delay 1
 
 
 [testenv]
@@ -76,14 +76,13 @@ commands =
 
 
 [testenv:upload-wheels]
-deps = wheelhouse_uploader
+deps = twine
 
-commands = python setup.py register sdist upload
+commands = twine upload dist/*
 
 
 [testenv:test-pypi]
-deps = wheelhouse_uploader
-       twine
+deps = twine
 whitelist_externals = rm
 
 commands =