Instructions to use Shushant/biomedical_question_answering with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shushant/biomedical_question_answering with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="Shushant/biomedical_question_answering")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Shushant/biomedical_question_answering") model = AutoModelForQuestionAnswering.from_pretrained("Shushant/biomedical_question_answering", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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# biomedical_question_answering
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This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on
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It achieves the following results on the evaluation set:
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- Loss: 2.6629
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# biomedical_question_answering
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This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on a custom dataset of question answer pairs annotated from research papers from Pubmed.
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It achieves the following results on the evaluation set:
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- Loss: 2.6629
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