Instructions to use fondress/PDeepPP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fondress/PDeepPP with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="fondress/PDeepPP")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("fondress/PDeepPP", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Update pipeline tag, add specific tags, and refine citation
Verified signatureThis PR updates the model card for PDeepPP to improve its accuracy and discoverability. Key changes include: * **Pipeline Tag Update**: Changed the `pipeline_tag` from `feature-extraction` to `text-classification`. This more accurately reflects the model's primary function of "peptide identification" and "biological identification tasks," ensuring it is discoverable under the correct category on the Hugging Face Hub (e.g., at [https://huggingface.co/models?pipeline_tag=text-classification](https://huggingface.co/models?pipeline_tag=text-classification)). * **Addition of Specific Tags**: Added relevant `tags` such as `peptide`, `protein`, `bioinformatics`, and `biology` to enhance discoverability for users searching within the biological and bioinformatics domains. * **Citation Refinement**: Updated the placeholder BibTeX entry in the citation section with more accurate details, including the arXiv preprint journal and URL, derived from the provided paper link. * **GitHub Link …efbcc88
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Verified signature- Add metadata, link to paper and code (034b4e0381d786a75f520810ea785a810edc013c) Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>d172b75
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