architkaila / Fine-Tuning-LLMs-for-Medical-Entity-Extraction

Exploring the potential of fine-tuning Large Language Models (LLMs) like Llama2 and StableLM for medical entity extraction. This project focuses on adapting these models using PEFT, Adapter V2, and LoRA techniques to efficiently and accurately extract drug names and adverse side-effects from pharmaceutical texts

Date Created 2023-12-03 (about a year ago)

Commits 26 (last one about a year ago)

Stargazers 61 (0 this week)

Watchers 3 (0 this week)

Forks 6

License mit

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RepositoryStats indexes 595,856 repositories, of these architkaila/Fine-Tuning-LLMs-for-Medical-Entity-Extraction is ranked #411,131 (31st percentile) for total stargazers, and #427,587 for total watchers. Github reports the primary language for this repository as Python, for repositories using this language it is ranked #78,380/119,431.

architkaila/Fine-Tuning-LLMs-for-Medical-Entity-Extraction is also tagged with popular topics, for these it's ranked: large-language-models (#791/1090)

All Topics

fine-tuning-llm entity-extraction medical-application large-language-models parameter-efficient-fine-tuning

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26 commits on the default branch (main) since jan '22

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updated: 2024-12-12 @ 02:30pm, id: 726968466 / R_kgDOK1Sokg