Token Classification
Transformers
Safetensors
ONNX
English
bert
clinical-nlp
named-entity-recognition
ner
biomedical
knowledge-distillation
phi-detection
quantization
Eval Results (legacy)
Instructions to use SantoshAdabala/ClinicalFlamo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SantoshAdabala/ClinicalFlamo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="SantoshAdabala/ClinicalFlamo")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("SantoshAdabala/ClinicalFlamo") model = AutoModelForTokenClassification.from_pretrained("SantoshAdabala/ClinicalFlamo", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from SantoshAdabala/ClinicalFlamo: direct link, hf CLI and curl.
- Browser
- Download file 669 kB
-
https://huggingface.co/SantoshAdabala/ClinicalFlamo/resolve/main/tokenizer.json
- Command line
-
hf download hf://SantoshAdabala/ClinicalFlamo/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/SantoshAdabala/ClinicalFlamo/resolve/main/tokenizer.json
669 kB
File too large to display, you can check the raw version instead.