Structbert Large Zh

🚀 StructBERT: Un-Official Copy

This is an unofficial copy of StructBERT. It extends BERT by incorporating language structures into pre - training, offering enhanced language understanding capabilities.

🚀 Quick Start

Reproduce HFHub models

Download model/tokenizer vocab:

wget https://raw.githubusercontent.com/alibaba/AliceMind/main/StructBERT/config/ch_large_bert_config.json && mv ch_large_bert_config.json config.json
wget https://raw.githubusercontent.com/alibaba/AliceMind/main/StructBERT/config/ch_vocab.txt
wget https://alice-open.oss-cn-zhangjiakou.aliyuncs.com/StructBERT/ch_model && mv ch_model pytorch_model.bin

from transformers import BertConfig, BertModel, BertTokenizer
config = BertConfig.from_pretrained("./config.json")
model = BertModel.from_pretrained("./", config=config)
tokenizer = BertTokenizer.from_pretrained("./")
model.push_to_hub("structbert-large-zh")
tokenizer.push_to_hub("structbert-large-zh")

The official paper can be found at https://arxiv.org/abs/1908.04577.

✨ Features

We extend BERT to a new model, StructBERT, by incorporating language structures into pre - training. Specifically, we pre - train StructBERT with two auxiliary tasks to make the most of the sequential order of words and sentences, which leverage language structures at the word and sentence levels, respectively.

📦 Installation

Requirements and Installation

• PyTorch version >= 1.0.1
• Install other libraries via

pip install -r requirements.txt

• For faster training install NVIDIA's apex library

💻 Usage Examples

Basic Usage

Finetune MNLI

python run_classifier_multi_task.py \
  --task_name MNLI \
  --do_train \
  --do_eval \
  --do_test \
  --amp_type O1 \
  --lr_decay_factor 1 \
  --dropout 0.1 \
  --do_lower_case \
  --detach_index -1 \
  --core_encoder bert \
  --data_dir path_to_glue_data \
  --vocab_file config/vocab.txt \
  --bert_config_file config/large_bert_config.json \
  --init_checkpoint path_to_pretrained_model \
  --max_seq_length 128 \
  --train_batch_size 32 \
  --learning_rate 2e-5 \
  --num_train_epochs 3 \
  --fast_train \
  --gradient_accumulation_steps 1 \
  --output_dir path_to_output_dir 

📚 Documentation

Pre - trained models

Results

The results of GLUE & CLUE tasks can be reproduced using the hyperparameters listed in the "Example usage" section.

structbert.en.large

GLUE benchmark

structbert.ch.large

CLUE benchmark

📄 License

Citation

If you use our work, please cite:

@article{wang2019structbert,
  title={Structbert: Incorporating language structures into pre-training for deep language understanding},
  author={Wang, Wei and Bi, Bin and Yan, Ming and Wu, Chen and Bao, Zuyi and Xia, Jiangnan and Peng, Liwei and Si, Luo},
  journal={arXiv preprint arXiv:1908.04577},
  year={2019}
}

⚠️ Important Note

This model card is not produced by AliceMind Team.