BiDaF

Implementation of Bidirectional Attention Flow for Machine Comprehension on Squad v1.0 & Cloze-Style Reading Comprehension as illustrated in the paper from Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, Hannaneh Hajishirzi

Training Results

• Training Results

DATASET	RESULTS
SQuAD v1.1
Cloze-Style Reading Comprehension (CNN)
Cloze-Style Reading Comprehension (DailyMail)

• Training Results Original Paper

DATASET	RESULTS
SQuAD v1.1	Exact Match - 67.7%, F1 - 77.3%
Cloze-Style Reading Comprehension (CNN)	76.3%
Cloze-Style Reading Comprehension (DailyMail)	80.3%

The Results are without hyperparameter tuning models, tuning could be applied to achieve results much closer to original paper.

Getting Started

Dataset preprocessing is required to flatten the dataset.
When first running the train process use -load_data=True

• Training BIDAF model on SQUAD v1.1

python train.py -model_name=<model_name> -dataset_name=SQUAD -data=<path to data>

• Training BIDAF model on Cloze-Style Reading Comprehension

EXAMPLE

python train.py -model_name=LOCAL -dataset_name=SQUAD -data='../../../../data/squad v1.1/'

Note: Please check parameters section for complete details. Also, saving the model would slow down the training process.

Prediction

To extract response from a context

python predict.py -context=<context> -query=<query> -model_path=<path of trained model> -word_vocab=<path of WORD vocab> -char_vocab=<path of CHAR vocab>

EXAMPLE

python predict.py -context="He was speaking after figures showed that the country's economy shrank by 20.4% in April - the largest monthly contraction on record - as the country spent its first full month in lockdown." -query="By how much did the country's economy shrank" -model_path=./model_checkpoints/best_local_squadv1.1.torch -word_vocab=./model_checkpoints/SQUAD_WORDS.vocab -char_vocab=./model_checkpoints/SQUAD_CHAR.vocab

Time: 0:00:01 , ANSWER: 20 . 4 %

Prerequisites

torch==1.4.0
torchtext==0.6.0
pandas==1.0.3

Parameters

python train.py --help

optional arguments:
  -h, --help            show this help message and exit
  -data DATA            Path to input data
  -data_checkpoint DATA_CHECKPOINT
                        Path to store preprocessed data checkpoints
  -model_checkpoint MODEL_CHECKPOINT
                        Path to store modelled data checkpoints
  -model_name MODEL_NAME
                        provide a name to the model for storing at chekpoints
  -load_data [LOAD_DATA]
                        To Load data of use preprocessed data
  -dataset_name DATASET_NAME
                        Name of the Dataset
  -epochs EPOCHS        No. of Epoch to run
  -batch_size BATCH_SIZE
                        Number of examples in each batch
  -glove_size GLOVE_SIZE
                        Size of Glove vector to use
  -char_embedding_size CHAR_EMBEDDING_SIZE
                        Size of Character embeddings to be used
  -kernel_size KERNEL_SIZE
                        Kernel Size
  -channels_count CHANNELS_COUNT
                        Count of channels for character embeddings
  -learning_rate LEARNING_RATE
                        Learning Rate
  -epoch_log EPOCH_LOG  Print logs after xx epochs

python predict.py --help

optional arguments:
  -h, --help            show this help message and exit
  -context CONTEXT      Context in text
  -query QUERY          Query in text
  -model_path MODEL_PATH
                        path to model
  -word_vocab WORD_VOCAB
                        path to word vocab
  -char_vocab CHAR_VOCAB
                        path to char vocab