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 NLU tasks and multiple transformer-based encoders (eg. BERT, Distil-BERT, ALBERT, RoBERTa, XLNET etc.)
For complete documentation for this library, please refer to documentation
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.
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.
A quick guide to show how a model can be trained for single/multiple NLU tasks in just 3 simple steps and with no requirement to code!!
Follow these 3 simple steps to train your multi-task model!
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.tsvFor knowing about the task file parameters to make your task file, task file parameters.
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 50For knowing about the data_preparation.py script and its arguments, refer running data preparation.
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
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.
Here you can find various conversational AI tasks as examples and can train multi-task models in simple steps mentioned in the notebooks.
(Setup : Multi-task , Task type : Multiple)
Intent Detection(Task type : Single sentence classification)
Query: I need a reservation for a bar in bangladesh on feb the 11th 2032
Intent: BookRestaurant
NER (Task type :sequence labelling)
Query: ['book', 'a', 'spot', 'for', 'ten', 'at', 'a', 'top-rated', 'caucasian', 'restaurant', 'not', 'far', 'from', 'selmer']
NER tags: ['O', 'O', 'O', 'O', 'B-party_size_number', 'O', 'O', 'B-sort', 'B-cuisine', 'B-restaurant_type', 'B-spatial_relation', 'I-spatial_relation', 'O', 'B-city']
Fragment Detection (Task type : single sentence classification)
Query: a reservation for
Label: fragment
Notebook :- intent_ner_fragment
Transform file :- transform_file_snips
Tasks file :- tasks_file_snips
(Setup : single-task , Task type : sentence pair classification)
Query1: An old man with a package poses in front of an advertisement.
Query2: A man poses in front of an ad.
Label: entailment
Query1: An old man with a package poses in front of an advertisement.
Query2: A man poses in front of an ad for beer.
Label: non-entailment
Notebook :- entailment_snli
Transform file :- transform_file_snli
Tasks file :- tasks_file_snli
(Setup : single-task , Task type : sentence pair classification)
Query: how much money did evander holyfield make
Context: Evander Holyfield Net Worth. How much is Evander Holyfield Worth? Evander Holyfield Net Worth: Evander Holyfield is a retired American professional boxer who has a net worth of $500 thousand. A professional boxer, Evander Holyfield has fought at the Heavyweight, Cruiserweight, and Light-Heavyweight Divisions, and won a Bronze medal a the 1984 Olympic Games.
Label: answerable
Notebook :- answerability_detection_msmarco
Transform file :- transform_file_answerability
Tasks file :- tasks_file_answerability
(Setup : single-task , Task type : single sentence classification)
Query: what's the distance between destin florida and birmingham alabama?
Label: NUMERIC
Query: who is suing scott wolter
Label: PERSON
Notebook :- query_type_detection
Transform file :- transform_file_querytype
Tasks file :- tasks_file_querytype
(Setup : Multi-task , Task type : sequence labelling)
Query: ['Despite', 'winning', 'the', 'Asian', 'Games', 'title', 'two', 'years', 'ago', ',', 'Uzbekistan', 'are', 'in', 'the', 'finals', 'as', 'outsiders', '.']
NER tags: ['O', 'O', 'O', 'I-MISC', 'I-MISC', 'O', 'O', 'O', 'O', 'O', 'I-LOC', 'O', 'O', 'O', 'O', 'O', 'O', 'O']
POS tags: ['I-PP', 'I-VP', 'I-NP', 'I-NP', 'I-NP', 'I-NP', 'B-NP', 'I-NP', 'I-ADVP', 'O', 'I-NP', 'I-VP', 'I-PP', 'I-NP', 'I-NP', 'I-SBAR', 'I-NP', 'O']
Notebook :- ner_pos_tagging_conll
Transform file :- transform_file_conll
Tasks file :- tasks_file_conll
(Setup : single-task , Task type : single sentence classification)
Query: What places have the oligarchy government ?
Label: well-formed
Query: What day of Diwali in 1980 ?
Label: not well-formed
Notebook :- query_correctness
Transform file :- transform_file_query_correctness
Tasks file :- tasks_file_query_correctness
(Setup : single-task , Task type : single sentence classification)
Query1: What is the most used word in Malayalam?
Query2: What is meaning of the Malayalam word ""thumbatthu""?
Label: not similar
Query1: Which is the best compliment you have ever received?
Query2: What's the best compliment you've got?
Label: similar
Notebook :- query_similarity
Transform file :- transform_file_qqp
Tasks file :- tasks_file_qqp
(Setup : single-task , Task type : single sentence classification)
Review: What I enjoyed most in this film was the scenery of Corfu, being Greek I adore my country and I liked the flattering director's point of view. Based on a true story during the years when Greece was struggling to stand on her own two feet through war, Nazis and hardship. An Italian soldier and a Greek girl fall in love but the times are hard and they have a lot of sacrifices to make. Nicholas Cage looking great in a uniform gives a passionate account of this unfulfilled (in the beginning) love. I adored Christian Bale playing Mandras the heroine's husband-to-be, he looks very very good as a Greek, his personality matched the one of the Greek patriot! A true fighter in there, or what! One of the movies I would like to buy and keep it in my collection...for ever!
Label: positive
Notebook :- IMDb_sentiment_analysis
Transform file :- transform_file_imdb
Tasks file :- tasks_file_imdb