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
- Xet hash:
- 523936876d90a10ff240472487e16bc6b84aa0e40004e93048bbe2ae96ac5f27
- Size of remote file:
- 431 MB
- SHA256:
- bed49a0c55140678c94d403b24f90e09a95fb538b5a03b421723d2a02b55c99f
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