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
File size: 125 Bytes
5d8d600 | 1 2 3 4 5 6 7 8 | {
"cls_token": "[CLS]",
"mask_token": "[MASK]",
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"unk_token": "[UNK]"
}
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