readme: ---
  language: ja
  license: cc-by-nc-sa-4.0
  tags:
  - roberta
  - medical
  inference: false
  ---
  
  # alabnii/jmedroberta-base-manbyo-wordpiece
  
  ## Model description
  
  This is a Japanese RoBERTa base model pre-trained on academic articles in medical sciences collected by Japan Science and Technology Agency (JST).
  
  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).
  
  ## Datasets used for pre-training
  
  - abstracts (train: 1.6GB (10M sentences), validation: 0.2GB (1.3M sentences))
  - abstracts & body texts (train: 0.2GB (1.4M sentences))
  
  ## How to use
  
  **Before using the model, make sure that [Manbyo Dictionary](https://sociocom.naist.jp/manbyou-dic/) has been downloaded under `/usr/local/lib/mecab/dic/userdic`.**
  
  ```bash
  # download Manbyo-Dictionary
  
  mkdir -p /usr/local/lib/mecab/dic/userdic
  wget https://sociocom.jp/~data/2018-manbyo/data/MANBYO_201907_Dic-utf8.dic
  mv MANBYO_201907_Dic-utf8.dic /usr/local/lib/mecab/dic/userdic
  ```
  
  ---
  
  **Note: If you don't have root privileges and find it difficult to download the Manbyo Dictionary to `/usr/local/lib/mecab/dic/userdic`, you can still load our model by overriding tokenizer settings as follows:**
  
  ```bash
  # download Manbyo-Dictionary wherever you like
  
  wget https://sociocom.jp/~data/2018-manbyo/data/MANBYO_201907_Dic-utf8.dic
  mv MANBYO_201907_Dic-utf8.dic /anywhere/you/like
  ```
  
  ```python
  from transformers import AutoModelForMaskedLM, AutoTokenizer
  
  model = AutoModelForMaskedLM.from_pretrained("alabnii/jmedroberta-base-manbyo-wordpiece")
  tokenizer = AutoTokenizer.from_pretrained("alabnii/jmedroberta-base-manbyo-wordpiece", **{
      "mecab_kwargs": {
          "mecab_option": "-u /anywhere/you/like/MANBYO_201907_Dic-utf8.dic"
      }
  })
  ```
  
  ---
  
  **Input text must be converted to full-width characters（全角）in advance.**
  
  You can use this model for masked language modeling as follows:
  ```python
  from transformers import AutoModelForMaskedLM, AutoTokenizer
  
  model = AutoModelForMaskedLM.from_pretrained("alabnii/jmedroberta-base-manbyo-wordpiece")
  model.eval()
  tokenizer = AutoTokenizer.from_pretrained("alabnii/jmedroberta-base-manbyo-wordpiece")
  
  texts = ['この患者は[MASK]と診断された。']
  inputs = tokenizer.batch_encode_plus(texts, return_tensors='pt')
  outputs = model(**inputs)
  tokenizer.convert_ids_to_tokens(outputs.logits[0][1:-1].argmax(axis=-1))
  # ['この', '患者', 'は', 'ＡＬＳ', 'と', '診断', 'さ', 'れ', 'た', '。']
  ```
  
  Alternatively, you can employ [Fill-mask pipeline](https://huggingface.co/tasks/fill-mask).
  
  ```python
  from transformers import pipeline
  
  fill = pipeline("fill-mask", model="alabnii/jmedroberta-base-manbyo-wordpiece", top_k=10)
  fill("この患者は[MASK]と診断された。")
  #[{'score': 0.020739275962114334,
  #  'token': 11474,
  #  'token_str': 'ＡＬＳ',
  #  'sequence': 'この 患者 は ＡＬＳ と 診断 さ れ た 。'},
  # {'score': 0.0193060003221035,
  #  'token': 10777,
  #  'token_str': '統合失調症',
  #  'sequence': 'この 患者 は 統合失調症 と 診断 さ れ た 。'},
  # {'score': 0.014001614414155483,
  #  'token': 27318,
  #  'token_str': 'Ｆａｂｒｙ病',
  #  'sequence': 'この 患者 は Ｆａｂｒｙ病 と 診断 さ れ た 。'},
  # ...
  ```
  
  You can fine-tune this model on downstream tasks.
  
  **See also sample Colab notebooks:** https://colab.research.google.com/drive/1yqUaqLf0Lf_imRT9TXPXEt1dowfK_2CS?usp=sharing
  
  ## Tokenization
  
  Mecab (w/ IPAdic & [Manbyo Dictionary](https://sociocom.naist.jp/manbyou-dic/)) was used for pre-training. Each word is tokenized into tokens by [WordPiece](https://huggingface.co/course/chapter6/6).
  
  ## Vocabulary
  
  The vocabulary consists of 30000 tokens including words (IPAdic & [Manbyo Dictionary](https://sociocom.naist.jp/manbyou-dic/)) and subwords induced by [WordPiece](https://huggingface.co/course/chapter6/6).
  
  ## Training procedure
  
  The following hyperparameters were used during pre-training:
  
  - learning_rate: 0.0001
  - train_batch_size: 32
  - eval_batch_size: 32
  - seed: 42
  - distributed_type: multi-GPU
  - num_devices: 8
  - total_train_batch_size: 256
  - total_eval_batch_size: 256
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 20000
  - training_steps: 2000000
  - mixed_precision_training: Native AMP
  
  ## Note: Why do we call our model RoBERTa, not BERT?
  
  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:
  
  - We kept training only with max sequence length (= 512) tokens.
  - We removed the next sentence prediction (NSP) training objective.
  - We introduced dynamic masking (changing the masking pattern in each training iteration).
  
  ## Acknowledgements
  
  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.  
  In this research work, we used the "[mdx: a platform for the data-driven future](https://mdx.