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---
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
```
**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.jp/)". |