MiniLM: Small and Fast Pre-trained Models for Language Understanding and Generation

MiniLM is a distilled model from the paper " MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers ".

Please find the information about preprocessing, training and full details of the MiniLM in the original MiniLM repository .

Please note: This checkpoint uses BertModel with XLMRobertaTokenizer so AutoTokenizer won't work with this checkpoint!

Multilingual Pretrained Model

• Multilingual-MiniLMv1-L12-H384: 12-layer, 384-hidden, 12-heads, 21M Transformer parameters, 96M embedding parameters

Multilingual MiniLM uses the same tokenizer as XLM-R. But the Transformer architecture of our model is the same as BERT. We provide the fine-tuning code on XNLI based on huggingface/transformers . Please replace run_xnli.py in transformers with ours to fine-tune multilingual MiniLM.

We evaluate the multilingual MiniLM on cross-lingual natural language inference benchmark (XNLI) and cross-lingual question answering benchmark (MLQA).

We evaluate our model on cross-lingual transfer from English to other languages. Following Conneau et al. (2019) , we select the best single model on the joint dev set of all the languages.

This example code fine-tunes 12 -layer multilingual MiniLM on XNLI.

# run fine-tuning on XNLI
DATA_DIR=/{path_of_data}/
OUTPUT_DIR=/{path_of_fine-tuned_model}/
MODEL_PATH=/{path_of_pre-trained_model}/

python ./examples/run_xnli.py --model_type minilm \
 --output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \
 --model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \
 --tokenizer_name xlm-roberta-base \
 --config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \
 --do_train \
 --do_eval \
 --max_seq_length 128 \
 --per_gpu_train_batch_size 128 \
 --learning_rate 5e-5 \
 --num_train_epochs 5 \
 --per_gpu_eval_batch_size 32 \
 --weight_decay 0.001 \
 --warmup_steps 500 \
 --save_steps 1500 \
 --logging_steps 1500 \
 --eval_all_checkpoints \
 --language en \
 --fp16 \
 --fp16_opt_level O2

Following Lewis et al. (2019b) , we adopt SQuAD 1.1 as training data and use MLQA English development data for early stopping.

Citation

If you find MiniLM useful in your research, please cite the following paper:

@misc{wang2020minilm,
    title={MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers},
    author={Wenhui Wang and Furu Wei and Li Dong and Hangbo Bao and Nan Yang and Ming Zhou},
    year={2020},
    eprint={2002.10957},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}