jordiluque / charbert

CharBERT: Character-aware Pre-trained Language Model (COLING2020)

CharBERT: Character-aware Pre-trained Language Model

This repository contains resources of the following COLING 2020 paper.

Title: CharBERT: Character-aware Pre-trained Language Model
Authors: Wentao Ma, Yiming Cui, Chenglei Si, Ting Liu, Shijin Wang, Guoping Hu
Link: https://arxiv.org/abs/2011.01513

Models

We primarily provide two models. Here are the download links:
pre-trained CharBERT based on BERT charbert-bert-wiki
pre-trained CharBERT based on RoBERTa charbert-roberta-wiki

Directory Guide

root_directory
    |- modeling    # contains source codes of CharBERT model part
    |- data   # Character attack datasets and the dicts for CharBERT
    |- processors # contains source codes for processing the datasets
    |- shell     # the examples of shell script for training and evaluation
    |- run_*.py  # codes for pre-training or finetuning

Requirements

Python 3.6  
Pytorch 1.2
Transformers 2.4.0

Performance

SQuAD

Model	1.1	2.0
BERT	80.5 / 88.5	73.7 / 76.3
CharBERT	82.9 / 89.9	75.7 / 78.6

Text Classification

Model	CoLA	MRPC	QQP	QNLI
BERT	57.4	86.7	90.6	90.7
CharBERT	59.1	87.8	91.0	91.7

Sequence Labeling

Model	NER	POS
BERT	91.24	97.93
CharBERT	91.81	98.05

Robustness

Evaluation Datasets

We conduct the robustness evaluation on adversarial misspellings followed Pruthi et al.,2019. Those attacks include four kinds of character-level attack: dropping, adding, swapping, keyboard. For the evaluation tasks, we select SQuAD 2.0, CoNLL2003 NER, and QNLI. For example, the original example in QNLI:

Question: What came into force after the new constitution was herald?
Sentence: As of that day, the new constitution heralding the Second Republic came into force.
Label: entailment

After the attack, the example is:

Question: What capme ino forxe afgter the new constitugion was herapd?
Sentence: As of taht day, the new clnstitution herajlding the Sscond Republuc cae into forcte.
Label: entailment

Please check data/attack_datasets folder for these data.

Robustness Evaluation (original/attack)

Model	QNLI	CoNLL2003 NER	SQuAD 2.0
BERT	90.7/63.4	91.24/60.79	76.3/50.1
CharBERT	91.7/80.1	91.81/76.14	78.6/56.3

Usage

You may use another hyper-parameter set to adapt to your computing device, but it may require further tuning, especially learning_rate and num_train_epoch.

MLM && NLM Pre-training

DATA_DIR=YOUR_DATA_PATH
MODEL_DIR=YOUR_MODEL_PATH/bert_base_cased #initialized by bert_base_cased model
OUTPUT_DIR=YOUR_OUTUT_PATH/mlm
python3 run_lm_finetuning.py \
    --model_type bert \
    --model_name_or_path ${MODEL_DIR} \
    --do_train \
    --do_eval \
    --train_data_file $DATA_DIR/testdata/mlm_pretrain_enwiki.train.t \
    --eval_data_file $DATA_DIR/testdata/mlm_pretrain_enwiki.test.t \
    --term_vocab ${DATA_DIR}/dict/term_vocab \
    --learning_rate 3e-5 \
    --num_train_epochs 2 \
    --mlm_probability 0.10 \
    --input_nraws 1000 \
    --per_gpu_train_batch_size 4 \
    --per_gpu_eval_batch_size 4 \
    --save_steps 10000 \
    --block_size 384 \
    --overwrite_output_dir \
    --mlm \
    --output_dir ${OUTPUT_DIR}

SQuAD

MODEL_DIR=YOUR_MODEL_PARH/charbert-bert-pretrain 
SQUAD2_DIR=YOUR_DATA_PATH/squad 
OUTPUT_DIR=YOUR_OUTPUT_PATH/squad 
python run_squad.py \
    --model_type bert \
    --model_name_or_path ${MODEL_DIR} \
    --do_train \
    --do_eval \
    --data_dir $SQUAD2_DIR \
    --train_file $SQUAD2_DIR/train-v1.1.json \
    --predict_file $SQUAD2_DIR/dev-v1.1.json \
    --learning_rate 3e-5 \
    --num_train_epochs 2 \
    --per_gpu_train_batch_size 4 \
    --per_gpu_eval_batch_size 4 \
    --save_steps 2000 \
    --max_seq_length 384 \
    --overwrite_output_dir \
    --doc_stride 128 \
    --output_dir ${OUTPUT_DIR}

NER

DATA_DIR=YOUR_DATA_PATH/CoNLL2003/NER-en
MODEL_DIR=YOUR_MODEL_PATH/charbert-bert-wiki
OUTPUT_DIR=YOUR_OUTPUT_PATH/ner
python run_ner.py --data_dir ${DATA_DIR} \
                  --model_type bert \
                  --model_name_or_path $MODEL_DIR \
                  --output_dir ${OUTPUT_DIR} \
                  --num_train_epochs 3 \
                  --learning_rate 3e-5 \
                  --char_vocab ./data/dict/bert_char_vocab \
                  --per_gpu_train_batch_size 6 \
                  --do_train \
                  --do_predict \
                  --overwrite_output_dir

Citation

If you use the data or codes in this repository, please cite our paper.

@misc{ma2020charbert,
      title={CharBERT: Character-aware Pre-trained Language Model}, 
      author={Wentao Ma and Yiming Cui and Chenglei Si and Ting Liu and Shijin Wang and Guoping Hu},
      year={2020},
      eprint={2011.01513},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Issues

If there is any problem, please submit a GitHub Issue.