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

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.

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.

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.

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.