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--- |
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language: ja |
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license: cc-by-nc-sa-4.0 |
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tags: |
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- roberta |
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- medical |
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mask_token: "[MASK]" |
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widget: |
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- text: "この患者は[MASK]と診断された。" |
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--- |
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# alabnii/jmedroberta-base-sentencepiece |
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## Model description |
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This is a Japanese RoBERTa base model pre-trained on academic articles in medical sciences collected by Japan Science and Technology Agency (JST). |
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This model is released under the [Creative Commons 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0/deed) (CC BY-NC-SA 4.0). |
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## Datasets used for pre-training |
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- abstracts (train: 1.6GB (10M sentences), validation: 0.2GB (1.3M sentences)) |
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- abstracts & body texts (train: 0.2GB (1.4M sentences)) |
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## How to use |
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**Input text must be converted to full-width characters（全角）in advance.** |
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You can use this model for masked language modeling as follows: |
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```python |
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from transformers import AutoModelForMaskedLM, AutoTokenizer |
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model = AutoModelForMaskedLM.from_pretrained("alabnii/jmedroberta-base-sentencepiece") |
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model.eval() |
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tokenizer = AutoTokenizer.from_pretrained("alabnii/jmedroberta-base-sentencepiece") |
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texts = ['この患者は[MASK]と診断された。'] |
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inputs = tokenizer.batch_encode_plus(texts, return_tensors='pt') |
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outputs = model(**inputs) |
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tokenizer.convert_ids_to_tokens(outputs.logits[0][1:-1].argmax(axis=-1)) |
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# ['▁この', '患者は', 'ＡＭＬ', '▁', 'と診断された', '。'] |
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``` |
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Alternatively, you can employ [Fill-mask pipeline](https://huggingface.co/tasks/fill-mask). |
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```python |
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from transformers import pipeline |
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fill = pipeline("fill-mask", model="alabnii/jmedroberta-base-sentencepiece", top_k=10) |
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fill("この患者は[MASK]と診断された。") |
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#[{'score': 0.04239409416913986, |
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# 'token': 7698, |
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# 'token_str': 'ＡＭＬ', |
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# 'sequence': 'この患者はＡＭＬ と診断された。'}, |
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# {'score': 0.03562006726861, |
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# 'token': 3298, |
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# 'token_str': 'ＳＬＥ', |
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# 'sequence': 'この患者はＳＬＥ と診断された。'}, |
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# {'score': 0.025064188987016678, |
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# 'token': 10303, |
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# 'token_str': 'ＭＤＳ', |
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# 'sequence': 'この患者はＭＤＳ と診断された。'}, |
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# ... |
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``` |
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You can fine-tune this model on downstream tasks. |
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**See also sample Colab notebooks:** https://colab.research.google.com/drive/1BUD3DKOUMqcwIO3X5bYUOsR_wDzgOJcd?usp=sharing |
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## Tokenization |
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Each sentence is tokenized into tokens by [SentencePiece (Unigram)](https://huggingface.co/course/chapter6/7). |
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## Vocabulary |
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The vocabulary consists of 30000 tokens induced by [SentencePiece (Unigram)](https://huggingface.co/course/chapter6/7). |
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## Training procedure |
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The following hyperparameters were used during pre-training: |
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- learning_rate: 0.0001 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 8 |
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- total_train_batch_size: 256 |
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- total_eval_batch_size: 256 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 20000 |
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- training_steps: 2000000 |
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- mixed_precision_training: Native AMP |
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## Note: Why do we call our model RoBERTa, not BERT? |
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As the config file suggests, our model is based on HuggingFace's `BertForMaskedLM` class. However, we consider our model as **RoBERTa** for the following reasons: |
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- We kept training only with max sequence length (= 512) tokens. |
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- We removed the next sentence prediction (NSP) training objective. |
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- We introduced dynamic masking (changing the masking pattern in each training iteration). |
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## Acknowledgements |
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This work was supported by Japan Japan Science and Technology Agency (JST) AIP Trilateral AI Research (Grant Number: JPMJCR20G9), and Joint Usage/Research Center for Interdisciplinary Large-scale Information Infrastructures (JHPCN) (Project ID: jh221004), in Japan. |
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In this research work, we used the "[mdx: a platform for the data-driven future](https://mdx.jp/)". |
