Token Classification
Transformers
Safetensors
PyTorch
English
bert
named-entity-recognition
ner
medical
disease-extraction
healthcare
clinical-bert
fine-tuned
bio-tagging
Instructions to use keanteng/bert-ner-wqd7005 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use keanteng/bert-ner-wqd7005 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="keanteng/bert-ner-wqd7005")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("keanteng/bert-ner-wqd7005") model = AutoModelForTokenClassification.from_pretrained("keanteng/bert-ner-wqd7005", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload folder using huggingface_hub
Browse files
evaluation/binary_labels.pkl
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oid sha256:ede43610c1c83031d9eb00d0965c70999a1531a1e6a18db04f5afdca37019104
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evaluation/ner_metrics.pkl
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oid sha256:d30873db71c97edaba69c536e20ea2aadd6c77435281d6429cf5384127ebf8c8
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evaluation/prob_preds_flat.pkl
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oid sha256:5ab5f22b8cdcbb0c8e86f4db685ace242e8cdc3186d8b7d954f425453d816e22
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size 123553
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