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README.md

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+---
+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/)".

config.json

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+{
+  "architectures": [
+    "BertForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.16.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 50000
+}

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tokenizer_config.json

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+{
+    "tokenizer_class": "BertJapaneseTokenizer",
+    "word_tokenizer_type": "mecab",
+    "subword_tokenizer_type": "wordpiece",
+    "mecab_kwargs": {
+        "mecab_dic": "ipadic",
+        "mecab_option": "-u /usr/local/lib/mecab/dic/userdic/MANBYO_201907_Dic-utf8.dic",
+        "normalize_text": false
+    }
+}