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--- |
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language: |
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- multilingual |
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- en |
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- ar |
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- bg |
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- de |
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- el |
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- es |
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- fr |
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- hi |
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- ru |
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- sw |
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- th |
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- tr |
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- ur |
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- vi |
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- zh |
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thumbnail: https://huggingface.co/front/thumbnails/microsoft.png |
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tags: |
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- text-classification |
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license: mit |
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--- |
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## MiniLM: Small and Fast Pre-trained Models for Language Understanding and Generation |
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MiniLM is a distilled model from the paper "[MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://arxiv.org/abs/2002.10957)". |
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Please find the information about preprocessing, training and full details of the MiniLM in the [original MiniLM repository](https://github.com/microsoft/unilm/blob/master/minilm/). |
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Please note: This checkpoint uses `BertModel` with `XLMRobertaTokenizer` so `AutoTokenizer` won't work with this checkpoint! |
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### Multilingual Pretrained Model |
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- Multilingual-MiniLMv1-L12-H384: 12-layer, 384-hidden, 12-heads, 21M Transformer parameters, 96M embedding parameters |
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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](https://github.com/huggingface/transformers). Please replace `run_xnli.py` in transformers with [ours](https://github.com/microsoft/unilm/blob/master/minilm/examples/run_xnli.py) to fine-tune multilingual MiniLM. |
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We evaluate the multilingual MiniLM on cross-lingual natural language inference benchmark (XNLI) and cross-lingual question answering benchmark (MLQA). |
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#### Cross-Lingual Natural Language Inference - [XNLI](https://arxiv.org/abs/1809.05053) |
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We evaluate our model on cross-lingual transfer from English to other languages. Following [Conneau et al. (2019)](https://arxiv.org/abs/1911.02116), we select the best single model on the joint dev set of all the languages. |
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| Model | #Layers | #Hidden | #Transformer Parameters | Average | en | fr | es | de | el | bg | ru | tr | ar | vi | th | zh | hi | sw | ur | |
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|---------------------------------------------------------------------------------------------|---------|---------|-------------------------|---------|------|------|------|------|------|------|------|------|------|------|------|------|------|------|------| |
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| [mBERT](https://github.com/google-research/bert) | 12 | 768 | 85M | 66.3 | 82.1 | 73.8 | 74.3 | 71.1 | 66.4 | 68.9 | 69.0 | 61.6 | 64.9 | 69.5 | 55.8 | 69.3 | 60.0 | 50.4 | 58.0 | |
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| [XLM-100](https://github.com/facebookresearch/XLM#pretrained-cross-lingual-language-models) | 16 | 1280 | 315M | 70.7 | 83.2 | 76.7 | 77.7 | 74.0 | 72.7 | 74.1 | 72.7 | 68.7 | 68.6 | 72.9 | 68.9 | 72.5 | 65.6 | 58.2 | 62.4 | |
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| [XLM-R Base](https://arxiv.org/abs/1911.02116) | 12 | 768 | 85M | 74.5 | 84.6 | 78.4 | 78.9 | 76.8 | 75.9 | 77.3 | 75.4 | 73.2 | 71.5 | 75.4 | 72.5 | 74.9 | 71.1 | 65.2 | 66.5 | |
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| **mMiniLM-L12xH384** | 12 | 384 | 21M | 71.1 | 81.5 | 74.8 | 75.7 | 72.9 | 73.0 | 74.5 | 71.3 | 69.7 | 68.8 | 72.1 | 67.8 | 70.0 | 66.2 | 63.3 | 64.2 | |
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This example code fine-tunes **12**-layer multilingual MiniLM on XNLI. |
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```bash |
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# run fine-tuning on XNLI |
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DATA_DIR=/{path_of_data}/ |
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OUTPUT_DIR=/{path_of_fine-tuned_model}/ |
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MODEL_PATH=/{path_of_pre-trained_model}/ |
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python ./examples/run_xnli.py --model_type minilm \ |
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--output_dir ${OUTPUT_DIR} --data_dir ${DATA_DIR} \ |
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--model_name_or_path microsoft/Multilingual-MiniLM-L12-H384 \ |
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--tokenizer_name xlm-roberta-base \ |
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--config_name ${MODEL_PATH}/multilingual-minilm-l12-h384-config.json \ |
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--do_train \ |
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--do_eval \ |
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--max_seq_length 128 \ |
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--per_gpu_train_batch_size 128 \ |
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--learning_rate 5e-5 \ |
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--num_train_epochs 5 \ |
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--per_gpu_eval_batch_size 32 \ |
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--weight_decay 0.001 \ |
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--warmup_steps 500 \ |
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--save_steps 1500 \ |
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--logging_steps 1500 \ |
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--eval_all_checkpoints \ |
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--language en \ |
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--fp16 \ |
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--fp16_opt_level O2 |
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``` |
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#### Cross-Lingual Question Answering - [MLQA](https://arxiv.org/abs/1910.07475) |
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Following [Lewis et al. (2019b)](https://arxiv.org/abs/1910.07475), we adopt SQuAD 1.1 as training data and use MLQA English development data for early stopping. |
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| Model F1 Score | #Layers | #Hidden | #Transformer Parameters | Average | en | es | de | ar | hi | vi | zh | |
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|--------------------------------------------------------------------------------------------|---------|---------|-------------------------|---------|------|------|------|------|------|------|------| |
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| [mBERT](https://github.com/google-research/bert) | 12 | 768 | 85M | 57.7 | 77.7 | 64.3 | 57.9 | 45.7 | 43.8 | 57.1 | 57.5 | |
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| [XLM-15](https://github.com/facebookresearch/XLM#pretrained-cross-lingual-language-models) | 12 | 1024 | 151M | 61.6 | 74.9 | 68.0 | 62.2 | 54.8 | 48.8 | 61.4 | 61.1 | |
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| [XLM-R Base](https://arxiv.org/abs/1911.02116) (Reported) | 12 | 768 | 85M | 62.9 | 77.8 | 67.2 | 60.8 | 53.0 | 57.9 | 63.1 | 60.2 | |
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| [XLM-R Base](https://arxiv.org/abs/1911.02116) (Our fine-tuned) | 12 | 768 | 85M | 64.9 | 80.3 | 67.0 | 62.7 | 55.0 | 60.4 | 66.5 | 62.3 | |
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| **mMiniLM-L12xH384** | 12 | 384 | 21M | 63.2 | 79.4 | 66.1 | 61.2 | 54.9 | 58.5 | 63.1 | 59.0 | |
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### Citation |
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If you find MiniLM useful in your research, please cite the following paper: |
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``` latex |
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@misc{wang2020minilm, |
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title={MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers}, |
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author={Wenhui Wang and Furu Wei and Li Dong and Hangbo Bao and Nan Yang and Ming Zhou}, |
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year={2020}, |
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eprint={2002.10957}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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