SpanBERT

This repository contains code and models for the paper: SpanBERT: Improving Pre-training by Representing and Predicting Spans.

Requirements

Pre-trained Models

We release both base and large cased models for SpanBERT. The base & large models have the same model configuration as BERT but they differ in both the masking scheme and the training objectives (see our paper for more details).

SpanBERT (base & cased): 12-layer, 768-hidden, 12-heads , 110M parameters
SpanBERT (large & cased): 24-layer, 1024-hidden, 16-heads, 340M parameters

These models have the same format as the HuggingFace BERT models, so you can easily replace them with our SpanBET models. If you would like to use our fine-tuning code, the model paths are already hard-coded in the code :)

	SQuAD 1.1	SQuAD 2.0	Coref	TACRED
	F1	F1	avg. F1	F1
BERT (base)	88.5*	76.5*	73.1	67.7
SpanBERT (base)	92.4*	83.6*	77.4	68.2
BERT (large)	91.3	83.3	77.1	66.4
SpanBERT (large)	94.6	88.7	79.6	70.8

Note: The numbers marked as * are evaluated on the development sets because we didn't submit those models to the official SQuAD leaderboard. All the other numbers are test numbers.

Fine-tuning

SQuAD 1.1

python code/run_squad.py \
  --do_train \
  --do_eval \
  --model spanbert-base-cased \
  --train_file train-v1.1.json \
  --dev_file dev-v1.1.json \
  --train_batch_size 32 \
  --eval_batch_size 32  \
  --learning_rate 2e-5 \
  --num_train_epochs 4 \
  --max_seq_length 512 \
  --doc_stride 128 \
  --eval_metric f1 \
  --output_dir squad_output \
  --fp16

SQuAD 2.0

python code/run_squad.py \
  --do_train \
  --do_eval \
  --model spanbert-base-cased \
  --train_file train-v2.0.json \
  --dev_file dev-v2.0.json \
  --train_batch_size 32 \
  --eval_batch_size 32  \
  --learning_rate 2e-5 \
  --num_train_epochs 4 \
  --max_seq_length 512 \
  --doc_stride 128 \
  --eval_metric best_f1 \
  --output_dir squad2_output \
  --version_2_with_negative \
  --fp16

TACRED

python code/run_tacred.py \
  --do_train \
  --do_eval \
  --data_dir <TACRED_DATA_DIR> \
  --model spanbert-base-cased \
  --train_batch_size 32 \
  --eval_batch_size 32 \
  --learning_rate 2e-5 \
  --num_train_epochs 10 \
  --max_seq_length 128 \
  --output_dir tacred_dir \
  --fp16

MRQA (NewsQA, TriviaQA, SearchQA, HotpotQA, NaturalQuestions)

python code/run_mrqa.py \
  --do_train \
  --do_eval \
  --model spanbert-base-cased \
  --train_file TriviaQA-train.jsonl.gz \
  --dev_file TriviaQA-dev.jsonl.gz \
  --train_batch_size 32 \
  --eval_batch_size 32 \
  --learning_rate 2e-5 \
  --num_train_epochs 4 \
  --max_seq_length 512 \
  --doc_stride 128 \
  --eval_per_epoch 5 \
  --output_dir triviaqa_dir \
  --fp16

GLUE

python code/run_glue.py \
   --task_name RTE \
   --model spanbert-base-cased \
   --do_train \
   --do_eval \
   --data_dir <RTE_DATA_DIR> \
   --train_batch_size 32 \
   --eval_batch_size 32 \
   --num_train_epochs 10  \
   --max_seq_length 128 \
   --learning_rate 2e-5 \
   --output_dir RTE_DIR \
   --fp16

Coref

Our coref fine-tuning code is implemented in Tensorflow. Please see https://github.com/mandarjoshi90/coref for more details.

Available models (QA, Coreference, Relation Extraction)

If you are interested in using our fine-tuned models for downstream tasks (QA, coreference, relation extraction) directly, we also provide the following models:

Citation

  @article{joshi2019spanbert,
      title={{SpanBERT}: Improving Pre-training by Representing and Predicting Spans},
      author={Mandar Joshi and Danqi Chen and Yinhan Liu and Daniel S. Weld and Luke Zettlemoyer and Omer Levy},
      journal={arXiv preprint arXiv:1907.10529},
      year={2019}
    }

License

SpanBERT is CC-BY-NC 4.0. The license applies to the pre-trained models as well.

Contact

If you have any questions, please contact Mandar Joshi <mandar90@cs.washington.edu> or Danqi Chen <danqic@cs.princeton.edu> or create a Github issue.

GitHub30/SpanBERT