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--- |
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library_name: transformers |
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license: cc-by-sa-4.0 |
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language: |
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- en |
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- ja |
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base_model: |
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- EQUES/JPharmatron-7B-base |
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tags: |
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- pharmacy |
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- biology |
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- chemistry |
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- medical |
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--- |
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# JPharmatron-7B |
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<!-- Provide a quick summary of what the model is/does. --> |
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JPharmatron-7B is a 7B large language model designed for pharmaceutical applications and researches. |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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The JPharmatron-7B is continually pre-trained using 8.8B tokens from Japanese and English datasets, based on Qwen2.5-7B. Compared to the JPharmatron-7B-base model, JPharmatron-7B has enhanced chat capabilities, obtained from Qwen2.5-7B-Instruct's chat vector. |
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- **Developed by:** EQUES Inc. |
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- **Funded by [optional]:** [GENIAC Project](https://www.meti.go.jp/policy/mono_info_service/geniac/index.html) |
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- **Model type:** Causal decoder-only |
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- **Language(s) (NLP):** Japanese, English |
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- **License:** CC-BY-SA-4.0 |
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### Model Sources [optional] |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** https://github.com/EQUES-Inc/pharma-LLM-eval |
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- **Paper [optional]:** [A Japanese Language Model and Three New Evaluation Benchmarks for Pharmaceutical NLP](https://arxiv.org/abs/2505.16661) (IJCNLP-AACL 2025) |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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This model is intended for applications in pharmaceutical paperwork and research. It is not validated for medical use or any other risk-sensitive use. |
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## Evaluation |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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We evaluated our model, JPharmatron-7B, with other general / domain-specific models of a similar size. |
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### Testing Data |
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<!-- This should link to a Dataset Card if possible. --> |
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[JPharmaBench](https://huggingface.co/collections/EQUES/jpharmabench-680a34acfe96870e41d050d8) and two existing benchmarks (JMMLU (pharma) and IgakuQA) were used. |
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### Results |
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Compared to Meditron3-Qwen2.5-7B and Llama3.1-Swallow-8B-Instruct-v0.3, JPharmatron-7B achieved the highest score on all of the five benchmarks. |
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## Citation [optional] |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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**This paper has been accepted to IJCNLP-AACL 2025. We will update the bibtex info below soon.** |
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**BibTeX:** |
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``` |
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@misc{ono2025japaneselanguagemodelnew, |
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title={A Japanese Language Model and Three New Evaluation Benchmarks for Pharmaceutical NLP}, |
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author={Shinnosuke Ono and Issey Sukeda and Takuro Fujii and Kosei Buma and Shunsuke Sasaki}, |
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year={2025}, |
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eprint={2505.16661}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/2505.16661}, |
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} |
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``` |
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## More Information [optional] |
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See our preprint: [A Japanese Language Model and Three New Evaluation Benchmarks for Pharmaceutical NLP](https://arxiv.org/abs/2505.16661). |
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## Model Card Authors [optional] |
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[@shinnosukeono](https://shinnosukeono.github.io/) |
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