EQUES
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jpharma-bert-base

@@ -3,197 +3,57 @@ library_name: transformers
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 tags: []
 ---
+# Model Card
 <!-- Provide a quick summary of what the model is/does. -->
+Our **JpharmaBERT (base)** is a continually pre-trained version of the BERT model ([tohoku-nlp/bert-base-japanese-v3](https://huggingface.co/tohoku-nlp/bert-base-japanese-v3)), further trained on pharmaceutical data — the same dataset used for [eques/jpharmatron](https://huggingface.co/EQUES/JPharmatron-7B).
+# Examoke Usage
 <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+```python
+import torch
+from transformers import AutoModelForMaskedLM, AutoTokenizer, pipeline
+model = AutoModelForMaskedLM.from_pretrained("EQUES/jpharma-bert-large", torch_dtype=torch.bfloat16)
+tokenizer = AutoTokenizer.from_pretrained("EQUES/jpharma-bert-large")
+fill_mask = pipeline("fill-mask", model=model, tokenizer=tokenizer)
+results = fill_mask("水は化学式で[MASK]2Oです。")
+for result in results:
+    print(result)
+# {'score': 0.49609375, 'token': 55, 'token_str': 'H', 'sequence': '水は化学式でH2Oです。'}
+# {'score': 0.11767578125, 'token': 29257, 'token_str': 'Na', 'sequence': '水は化学式でNa2Oです。'}
+# {'score': 0.047607421875, 'token': 61, 'token_str': 'N', 'sequence': '水は化学式でN2Oです。'}
+# {'score': 0.038330078125, 'token': 16966, 'token_str': 'CH', 'sequence': '水は化学式でCH2Oです。'}
+# {'score': 0.0255126953125, 'token': 66, 'token_str': 'S', 'sequence': '水は化学式でS2Oです。'}
+```
 ## Training Details
 ### Training Data
 <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+We used the same dataset as [eques/jpharmatron](https://huggingface.co/EQUES/JPharmatron-7B) for training our JpharmaBERT, which consists of:
+- Japanese text data (2B tokens) collected from pharmaceutical documents such as academic papers and package inserts
+- English data (8B tokens) obtained from PubMed abstracts
+- Pharmaceutical-related data (1.2B tokens) extracted from the multilingual CC100 dataset
+After removing duplicate entries across these sources, the final dataset contains approximately 9 billion tokens.
+(For details, please refer to our paper about Jpharmatron: [link](https://arxiv.org/abs/2505.16661))
 #### Training Hyperparameters
+The model was continually pre-trained with the following settings:
+- Mask probability: 15%
+- Maximum sequence length: 512 tokens
+- Number of training epochs: 6
+- Learning rate: 1e-4
+- Warm-up steps: 10,000
+- Per-device training batch size: 64
+## Model Card Authors
+Created by Takuro Fujii (tkr.fujii.ynu@gmail.com)