Updated readme

README.md

@@ -19,30 +19,31 @@ This is a re-implementation of Aker et al. (2017) ["Simple Open Stance Classific
 
 3) Download the [resources](https://github.com/GateNLP/StanceClassifier/releases/download/v0.1/resources.tar.gz) required for feature extraction and extract it inside the main folder (`StanceClassifer`)
 
-4) Download the [trained models](https://github.com/GateNLP/StanceClassifier/releases/download/v0.1/models.tar.gz) archive and extract it inside the main folder (`StanceClassifer`)
+4) Download the trained model or models that you want to use - each model is provided as a `tar.gz` file which should be unpacked in this folder, e.g. `curl -L <download_url> | tar xvzf -`
+
+ - [Ensemble model](https://github.com/GateNLP/StanceClassifier/releases/download/v0.2/model-ensemble.tar.gz): an English-language feature-based ensemble model built with Logistic Regression, Random Forest and Multi-Layer Perceptron classifiers
+ - [English BERT-based model](https://github.com/GateNLP/StanceClassifier/releases/download/v0.2/model-bert-english.tar.gz): Monolingual English BERT-based model, i.e. fine-tuning of BERT for the rumour stance classification task, using threshold moving for imbalanced data treatment
+ - [Multilingual BERT-based model](https://github.com/GateNLP/StanceClassifier/releases/download/v0.2/model-bert-multi.tar.gz): Multilingual version of the BERT-based model, tuned on the same English data as the above but based on the [multilingual BERT](https://github.com/google-research/bert/blob/master/multilingual.md) underlying model
 
 ## Usage
 
 ### Basic usage
 ```
 python -m StanceClassifier -l <LANGUAGE> -s <ORIGINAL_JSON> -o <REPLY_JSON> -c <MODEL>
 ```
-    Supported languages: en
-    Supported models: 
-        ens (Feature-based ensemble model built with Logistic Regression, Random Forest and Multi-Layer Perceptron classifiers)
-        bert-tm (BERT-based model, i.e. fine-tuning of BERT for the rumour stance classification task, using threshold moving for imbalanced data treatment)
+Supported model names for the `-c` option: `ens` (ensemble), `bert-english` (English BERT model), `bert-multilingual` (multilingual BERT model)
 
-    The output is a class:
-        0.0 = support
-        1.0 = deny
-        2.0 = query
-        3.0 = comment
+The output is a class:
+ - 0.0 = support
+ - 1.0 = deny
+ - 2.0 = query
+ - 3.0 = comment
 
-    and a vector with the probabilities returned for each class.
+and a vector with the probabilities returned for each class.
 
-    The folder `examples` contains examples of original tweets and replies:
-        original_old and reply_old are examples of the old JSON files (140 characters)
-        original_new and reply_new are examples of the new JSON files (280 characters)
+The folder `examples` contains examples of original tweets and replies:
+ - original_old and reply_old are examples of the old JSON files (140 characters)
+ - original_new and reply_new are examples of the new JSON files (280 characters)
 
 ### `StanceClassifer` class (StanceClassifier.stance_classifier.StanceClassifier)
 This is the main class in this project. If you want to add this project as part of your own project, you should import this class. 
@@ -70,6 +71,8 @@ To test the HTTP server:
 python Test_Run_HTTP_StanceClassifier_Server.py
 ```
 
+In addition, the `docker` directory contains configuration to build a Docker image running a particular model of the classifier as an HTTP endpoint compliant with the [API specification](https://european-language-grid.readthedocs.io/en/release1.1.2/all/A2_API/LTInternalAPI.html) of the [European Language Grid](https://www.european-language-grid.eu).
+
 ### Training new models
 To train new models, you can edit `train_model.py` (more support will be given in the future). To run:
 ```