Merge pull request hellohaptik#2 from hellohaptik/dev1.2

Dev1.2

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 .DS_Store
 
 #jupyter lab checkpoints
-.ipynb_checkpoints
+.ipynb_checkpoints
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+#docs
+docs/source/_build
+docs/build
+
+__pycache__
+
+.vscode

README.rst

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+==============
+multi-task-NLP
+==============
+
+multi_task_NLP is a utility toolkit enabling NLP developers to easily train and infer a single model for multiple tasks.
+We support various data formats for majority of NLI tasks and multiple transformer-based encoders (eg. BERT, Distil-BERT, ALBERT, RoBERTa, XLNET etc.)
+
+For complete documentation for this library, please refer to `documentation <https://multi-task-nlp.readthedocs.io/en/latest/>`_
+
+What is multi_task_NLP about?
+-----------------------------
+
+Any conversational AI system involves building multiple components to perform various tasks and a pipeline to stitch all components together.
+Provided the recent effectiveness of transformer-based models in NLP, it’s very common to build a transformer-based model to solve your use case.
+But having multiple such models running together for a conversational AI system can lead to expensive resource consumption, increased latencies for predictions and make the system difficult to manage.
+This poses a real challenge for anyone who wants to build a conversational AI system in a simplistic way.
+
+multi_task_NLP gives you the capability to define multiple tasks together and train a single model which simultaneously learns on all defined tasks.
+This means one can perform multiple tasks with latency and resource consumption equivalent to a single task.
+
+Installation
+------------
+
+To use multi-task-NLP, you can clone the repository into the desired location on your system
+with the following terminal command.
+
+>>> cd /desired/location/
+>>> git clone https://github.com/hellohaptik/multi-task-NLP.git
+>>> cd multi-task-NLP
+>>> pip install -r requirements.txt 
+
+NOTE:- The library is built and tested using ``Python 3.7.3``. It is recommended to install the requirements in a virtual environment.
+
+Quickstart Guide
+----------------
+A quick guide to show how a single model can be trained for multiple NLI tasks in just 3 simple steps
+and with **no requirement to code!!**
+
+Follow these 3 simple steps to train your multi-task model!
+
+Step 1 - Define your task file
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Task file is a YAML format file where you can add all your tasks for which you want to train a multi-task model.
+
+::
+
+  TaskA:
+    model_type: BERT
+    config_name: bert-base-uncased
+    dropout_prob: 0.05
+    label_map_or_file:
+    -label1
+    -label2
+    -label3
+    metrics:
+    - accuracy
+    loss_type: CrossEntropyLoss
+    task_type: SingleSenClassification
+    file_names:
+    - taskA_train.tsv
+    - taskA_dev.tsv
+    - taskA_test.tsv
+
+  TaskB:
+    model_type: BERT
+    config_name: bert-base-uncased
+    dropout_prob: 0.3
+    label_map_or_file: data/taskB_train_label_map.joblib
+    metrics:
+    - seq_f1
+    - seq_precision
+    - seq_recall
+    loss_type: NERLoss
+    task_type: NER
+    file_names:
+    - taskB_train.tsv
+    - taskB_dev.tsv
+    - taskB_test.tsv
+
+For knowing about the task file parameters to make your task file, `task file parameters <https://multi-task-nlp.readthedocs.io/en/latest/define_multi_task_model.html#task-file-parameters>`_.
+
+Step 2 - Run data preparation
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+After defining the task file, run the following command to prepare the data.
+
+>>> python data_preparation.py \ 
+    --task_file 'sample_task_file.yml' \
+    --data_dir 'data' \
+    --max_seq_len 50 
+
+For knowing about the ``data_preparation.py`` script and its arguments, refer `running data preparation <https://multi-task-nlp.readthedocs.io/en/latest/training.html#running-data-preparation>`_.
+
+Step 3 - Run train
+^^^^^^^^^^^^^^^^^^
+
+Finally you can start your training using the following command.
+
+>>> python train.py \
+    --data_dir 'data/bert-base-uncased_prepared_data' \
+    --task_file 'sample_task_file.yml' \
+    --out_dir 'sample_out' \
+    --epochs 5 \
+    --train_batch_size 4 \
+    --eval_batch_size 8 \
+    --grad_accumulation_steps 2 \
+    --log_per_updates 25 \
+    --save_per_updates 1000 \
+    --eval_while_train True \
+    --test_while_train True \
+    --max_seq_len 50 \
+    --silent True 
+
+For knowing about the ``train.py`` script and its arguments, refer `running train <https://multi-task-nlp.readthedocs.io/en/latest/training.html#running-train>`_.
+
+
+How to Infer?
+=============
+
+Once you have a multi-task model trained on your tasks, we provide a convenient and easy way to use it for getting
+predictions on samples through the **inference pipeline**.
+
+For running inference on samples using a trained model for say TaskA, TaskB and TaskC,
+you can import ``InferPipeline`` class and load the corresponding multi-task model by making an object of this class.
+
+>>> from infer_pipeline import inferPipeline
+>>> pipe = inferPipeline(modelPath = 'sample_out_dir/multi_task_model.pt', maxSeqLen = 50)
+
+``infer`` function can be called to get the predictions for input samples
+for the mentioned tasks.
+
+>>> samples = [ ['sample_sentence_1'], ['sample_sentence_2'] ]
+>>> tasks = ['TaskA', 'TaskB']
+>>> pipe.infer(samples, tasks)
+
+For knowing about the ``infer_pipeline``, refer `infer <https://multi-task-nlp.readthedocs.io/en/latest/infering.html>`_.