README.md

---
language: ja
license: cc-by-nc-sa-4.0
tags:
- roberta
- medical
inference: false
---

# alabnii/jmedroberta-base-manbyo-wordpiece-vocab50000

## 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-vocab50000")
model.eval()
tokenizer = AutoTokenizer.from_pretrained("alabnii/jmedroberta-base-manbyo-wordpiece-vocab50000")

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', 'ＳＬＥ', '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-vocab50000", top_k=10)
fill("この患者は[MASK]と診断された。")
#[{'score': 0.035826072096824646,
#  'token': 10840,
#  'token_str': 'ＳＬＥ',
#  'sequence': 'この 患者 は ＳＬＥ と 診断 さ れ た 。'},
# {'score': 0.020926717668771744,
#  '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': 0.02092057280242443,
#  'token': 8338,
#  'token_str': '糖尿病',
#  'sequence': 'この 患者 は 糖尿病 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/1p2770dXs0lge1IkuSHYLO-G-KJ4gZtou?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 50000 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/)".