gaotianyu1350 / Bert4CoQA

Bert for CoQA based on PyTorch.

Bert4CoQA

Introduction

This code is example demonstrating how to apply Bert on CoQA Challenge.

Code is basically combined from Transformer, run_squad.py, SDNet and SMRCToolkit.

I train model with config:

• --type bert
• --bert_model bert-base-uncased
• --num_train_epochs 2.0
• --do_lower_case
• --max_seq_length 512
• --doc_stride 128
• --max_query_length 64
• --train_batch_size 12
• --learning_rate 3e-5
• --warmup_proportion 0.1
• --max_grad_norm -1
• --weight_decay 0.01
• --fp16

on 1x TITAN Xp in 3 Hours and achieve 78.0 F1-score on dev-set.

That can definitely be improved, and if you found better hyper-parameters, you are welcome to raise an issue :)

Not tested on multi-machine training

Requirement

check requirement.txt or

pip install -r requirement.txt

How to run

make sure that:

1. Put train-coqa-v1.0.json and dev-coqa-v1.0.json on the same dict with run_coqa.py
2. The binary file, config file, and vocab file of bert_model in your bert_model dict name as pytorch_model.bin, config.json, vocab.txt
3. Enough memory on GPU according to this, you can tune --train_batch_size, --gradient_accumulation_steps, --max_seq_length and --fp16 for memeory saving.

and run

python run_coqa.py --bert_model your_bertmodel_dir --output_dir your_outputdir [optional]

or edit and run run.sh

for calculating F1-score, use evaluate-v1.0.py

python evaluate-v1.0.py --data-file <path_to_coqa-dev-v1.0.json> --pred-file <path_to_predictions.json>

Contact

If you have any questions, please new an issue or contact me, [e-mail].