notebooks/data_preparation/train/title-final-ner-part1-3.ipynb

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     "text": [
      "2021-10-27 19:10:57.187203: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /opt/conda/lib\n",
      "2021-10-27 19:10:57.187410: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
      "/opt/conda/lib/python3.7/site-packages/torchaudio/backend/utils.py:54: UserWarning: \"sox\" backend is being deprecated. The default backend will be changed to \"sox_io\" backend in 0.8.0 and \"sox\" backend will be removed in 0.9.0. Please migrate to \"sox_io\" backend. Please refer to https://github.com/pytorch/audio/issues/903 for the detail.\n",
      "  '\"sox\" backend is being deprecated. '\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import cloudpickle\n",
    "\n",
    "from transformers import *\n",
    "import torch"
   ]
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     "text": [
      "(301664, 23)\n"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image_url</th>\n",
       "      <th>page_title</th>\n",
       "      <th>caption_reference_description</th>\n",
       "      <th>count</th>\n",
       "      <th>spaced_filename</th>\n",
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       "      <th>title_translit</th>\n",
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       "      <th>page_title_contains_digit</th>\n",
       "      <th>undigit_page_title</th>\n",
       "      <th>target</th>\n",
       "      <th>caption_translation</th>\n",
       "      <th>final_caption</th>\n",
       "      <th>page_title_translation</th>\n",
       "      <th>final_page_title</th>\n",
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       "      <td>Jurgen ovens justitia</td>\n",
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       "      <th>2</th>\n",
       "      <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
       "      <td>Овенс, Юрген</td>\n",
       "      <td>Юрген Овенс Юстиция, 1663—1665, Музей Фленсбур...</td>\n",
       "      <td>4</td>\n",
       "      <td>Jurgen ovens justitia</td>\n",
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       "      <td>Ovens, Jurgen</td>\n",
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       "      <td>Jurgen Owens Justice, -, Flensburg Museum. Owe...</td>\n",
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       "                                           image_url    page_title  \\\n",
       "0  https://upload.wikimedia.org/wikipedia/commons...  Jürgen Ovens   \n",
       "1  https://upload.wikimedia.org/wikipedia/commons...  Jürgen Ovens   \n",
       "2  https://upload.wikimedia.org/wikipedia/commons...  Овенс, Юрген   \n",
       "\n",
       "                       caption_reference_description  count  \\\n",
       "0  Jürgen Ovens's Justitia, 1663-1665, Museumsber...      4   \n",
       "1  Jürgen Ovens: Justitia, \\n 1663-1665, Museumsb...      4   \n",
       "2  Юрген Овенс Юстиция, 1663—1665, Музей Фленсбур...      4   \n",
       "\n",
       "         spaced_filename spaced_filename_translit title_translit  \\\n",
       "0  Jurgen ovens justitia                           Juergen Ovens   \n",
       "1  Jurgen ovens justitia                           Juergen Ovens   \n",
       "2  Jurgen ovens justitia                           Ovens, Jurgen   \n",
       "\n",
       "                                    caption_translit page_title_lang  \\\n",
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       "\n",
       "                                     undigit_caption  \\\n",
       "0  Jürgen Ovens's Justitia, -, Museumsberg Flensb...   \n",
       "1  Jürgen Ovens: Justitia, \\n -, Museumsberg, Fle...   \n",
       "2  Юрген Овенс Юстиция, —, Музей Фленсбург. Овенс...   \n",
       "\n",
       "   page_title_contains_digit  undigit_page_title  \\\n",
       "0                      False        Jürgen Ovens   \n",
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       "\n",
       "                                              target  \\\n",
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       "1  Jürgen Ovens [SEP] Jürgen Ovens: Justitia, \\n ...   \n",
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       "\n",
       "                                 caption_translation  \\\n",
       "0                                                      \n",
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       "\n",
       "                                       final_caption page_title_translation  \\\n",
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       "1  Jürgen Ovens: Justitia, \\n 1663-1665, Museumsb...                          \n",
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       "\n",
       "  final_page_title  \n",
       "0     Jürgen Ovens  \n",
       "1     Jürgen Ovens  \n",
       "2    Owens, Jürgen  \n",
       "\n",
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      ]
     },
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   ],
   "source": [
    "matchings = pd.read_csv('../input/combiner-target-dataset-0-of-5/final_matchings.csv', keep_default_na=False)\n",
    "\n",
    "print(matchings.shape)\n",
    "\n",
    "matchings.head(3)"
   ]
  },
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   ],
   "source": [
    "model = AutoModelForTokenClassification.from_pretrained(\"dbmdz/bert-large-cased-finetuned-conll03-english\")\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
    "\n",
    "label_list = [\n",
    "    \"O\",       # Outside of a named entity\n",
    "    \"B-MISC\",  # Beginning of a miscellaneous entity right after another miscellaneous entity\n",
    "    \"I-MISC\",  # Miscellaneous entity\n",
    "    \"B-PER\",   # Beginning of a person's name right after another person's name\n",
    "    \"I-PER\",   # Person's name\n",
    "    \"B-ORG\",   # Beginning of an organisation right after another organisation\n",
    "    \"I-ORG\",   # Organisation\n",
    "    \"B-LOC\",   # Beginning of a location right after another location\n",
    "    \"I-LOC\"    # Location\n",
    "]"
   ]
  },
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   "source": [
    "def retrieve_entities(pairs, verbose=False):\n",
    "    def repair(s):\n",
    "        return s.replace(' #', '').replace('#', '')\n",
    "    def unique(l):\n",
    "        return list(set(l))\n",
    "    \n",
    "    \n",
    "    reading_entity = False\n",
    "    current_entity_type = None\n",
    "    \n",
    "    misc, per, org, loc = [], [], [], []\n",
    "    \n",
    "    entity_parts = []\n",
    "    \n",
    "    for part, et in pairs:\n",
    "        if not reading_entity and et == 'O':\n",
    "            continue\n",
    "        else:\n",
    "            if not reading_entity:\n",
    "                current_et = et\n",
    "                reading_entity = True               \n",
    "                \n",
    "            if current_et == et:\n",
    "                entity_parts.append(part)\n",
    "            else:\n",
    "                repaired_entity = repair(' '.join(entity_parts))\n",
    "                \n",
    "                if verbose:\n",
    "                    print(repaired_entity)\n",
    "                    \n",
    "                locals()[current_et.split('-')[-1].lower()].append(repaired_entity)\n",
    "                entity_parts.clear()\n",
    "                \n",
    "                if et == 'O':\n",
    "                    reading_entity = False\n",
    "                    current_et = None\n",
    "                else:\n",
    "                    entity_parts.append(part)\n",
    "                    current_et = 'I-' + et.split('-')[-1]\n",
    "                    \n",
    "                    \n",
    "    return { 'misc': unique(misc), 'per': unique(per), 'org': unique(org), 'loc': unique(loc)}"
   ]
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   "source": [
    "image_entities = { }\n",
    "\n",
    "for index, row in matchings.loc[:100000].iterrows():\n",
    "    try:\n",
    "        sequence = row['final_page_title'].strip()\n",
    "        \n",
    "        if sequence != '':\n",
    "            tokens = tokenizer.tokenize(tokenizer.decode(tokenizer.encode(sequence)))\n",
    "            inputs = tokenizer.encode(sequence, return_tensors=\"pt\")\n",
    "\n",
    "            outputs = model(inputs)[0]\n",
    "            predictions = torch.argmax(outputs, dim=2)\n",
    "\n",
    "            l = [(token, label_list[prediction]) for token, prediction in zip(tokens, predictions[0].tolist())]\n",
    "\n",
    "            image_entities[index] = retrieve_entities(l)\n",
    "    except Exception as ex:\n",
    "        print(index, sequence)\n",
    "        print(str(ex))\n",
    "        print()"
   ]
  },
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   "outputs": [],
   "source": [
    "with open('final_page_title_entities.dump', 'wb') as file:\n",
    "    cloudpickle.dump(image_entities, file)"
   ]
  },
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