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--- |
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title: Llama2-MedTuned-7b |
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emoji: 🧬 |
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colorFrom: blue |
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colorTo: green |
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sdk: static |
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pinned: false |
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license: apache-2.0 |
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tags: |
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- biomedical |
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- clinical |
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- medical |
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--- |
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# Model Description |
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Llama2-MedTuned-7b is an instruction-tuned version of the Llama2 7B model, specifically adapted for biomedical language processing tasks. It has been fine-tuned on a dataset consisting of approximately 200,000 instruction-focused samples, covering a range of biomedical and clinical NLP tasks such as Named Entity Recognition (NER), Relation Extraction (RE), and Medical Natural Language Inference (NLI). |
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# Instruction Tuning Procedure |
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This model underwent instruction tuning, a process where the model is fine-tuned with detailed instructions to enhance its ability to interpret and execute specific tasks in the biomedical domain. The tuning involved the use of a comprehensive instruction-based dataset, tailor-made to align with the requirements of biomedical NLP tasks. |
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# Model Capabilities |
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Llama2-MedTuned-7b demonstrates an enhanced understanding of biomedical contexts, effectively handling NER, RE, and NLI tasks. It showcases improved accuracy in generating structured outputs suitable for evaluation using conventional metrics. |
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# Architecture |
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The architecture of Llama2-MedTuned-7b is based on the autoregressive transformer model Llama2 7B. This model maintains the original transformer layers and attention mechanisms, specifically adjusted to cater to the linguistic intricacies of the biomedical field. |
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# Citation |
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If you utilise Llama2-MedTuned-7b in your research or application, please consider citing our paper: |
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```bibtex |
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@article{rohanian2024exploring, |
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title = {Exploring the Effectiveness of Instruction Tuning in Biomedical Language Processing}, |
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author = {Rohanian, Omid and Nouriborji, Mohammadmahdi and Kouchaki, Samaneh and Nooralahzadeh, Farhad and Clifton, Lei and Clifton, David A}, |
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journal = {Artificial Intelligence in Medicine}, |
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volume = {158}, |
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pages = {103007}, |
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year = {2024}, |
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publisher = {Elsevier}, |
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doi = {10.1016/j.artmed.2024.103007}, |
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url = {https://www.sciencedirect.com/science/article/pii/S0933365724002495}, |
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issn = {0933-3657} |
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} |
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``` |
