Instructions to use d4data/biomedical-ner-all with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use d4data/biomedical-ner-all with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="d4data/biomedical-ner-all")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("d4data/biomedical-ner-all") model = AutoModelForTokenClassification.from_pretrained("d4data/biomedical-ner-all") - Inference
- Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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tags:
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- Token Classification
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co2_eq_emissions: 0.0279399890043426
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widget:
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- text: "CASE: A 28-year-old previously healthy man presented with a 6-week history of palpitations.
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The symptoms occurred during rest, 2–3 times per week, lasted up to 30 minutes at a time and were associated with dyspnea.
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- en
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tags:
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- Token Classification
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co2_eq_emissions: 0.0279399890043426
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widget:
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- text: "CASE: A 28-year-old previously healthy man presented with a 6-week history of palpitations.
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The symptoms occurred during rest, 2–3 times per week, lasted up to 30 minutes at a time and were associated with dyspnea.
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