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 config.json
Browse files- config.json +2 -2
config.json
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@@ -34,7 +34,7 @@
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"22": "B-Mass",
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"23": "B-Medication",
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"24": "B-Non[biological](Detailed_description",
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"25": "B-
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"26": "B-Occupation",
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"27": "B-Other_entity",
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"28": "B-Other_event",
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"B-Mass": 22,
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"B-Medication": 23,
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"B-Non[biological](Detailed_description": 24,
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"B-
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"B-Occupation": 26,
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"B-Other_entity": 27,
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"B-Other_event": 28,
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"22": "B-Mass",
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"23": "B-Medication",
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"24": "B-Non[biological](Detailed_description",
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"25": "B-location",
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"26": "B-Occupation",
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"27": "B-Other_entity",
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"28": "B-Other_event",
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"B-Mass": 22,
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"B-Medication": 23,
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"B-Non[biological](Detailed_description": 24,
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"B-location": 25,
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"B-Occupation": 26,
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"B-Other_entity": 27,
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"B-Other_event": 28,
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