biogpt
Maintainer: microsoft
205
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
biogpt is a domain-specific generative transformer language model pre-trained on large-scale biomedical literature by researchers at Microsoft. It was developed to address the lack of generation ability in other popular biomedical language models like BioBERT and PubMedBERT, which are constrained to discriminative downstream tasks. In contrast, biogpt demonstrates strong performance on a variety of biomedical natural language processing tasks, including relation extraction and question answering.
Model inputs and outputs
Inputs
- Raw text data in the biomedical domain, such as research abstracts or papers
Outputs
- Automatically generated text in the biomedical domain, such as descriptions of biomedical terms or concepts
- Embeddings and representations of biomedical text that can be used for downstream tasks
Capabilities
biogpt can be used to generate fluent, coherent text in the biomedical domain. For example, when prompted with "COVID-19 is", the model can generate relevant and informative continuations like "COVID-19 is a disease that spreads worldwide and is currently found in a growing proportion of the population" or "COVID-19 is transmitted via droplets, air-borne, or airborne transmission."
What can I use it for?
biogpt can be used for a variety of biomedical NLP applications, such as:
- Biomedical text generation: Automatically generating descriptions, summaries, or explanations of biomedical concepts and findings.
- Downstream biomedical tasks: Fine-tuning
biogpton specific tasks like relation extraction, question answering, or biomedical text classification. - Biomedical text understanding: Using
biogptembeddings as input features for downstream biomedical ML models.
Things to try
One interesting aspect of biogpt is its strong performance on biomedical relation extraction tasks, achieving over 40% F1 score on benchmarks like BC5CDR and KD-DTI. Researchers could explore using biogpt as a starting point for building more advanced biomedical information extraction systems, leveraging its ability to understand complex biomedical relationships and entities.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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