Instructions to use commanderstrife/ADE-Bio_ClinicalBERT-NER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use commanderstrife/ADE-Bio_ClinicalBERT-NER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="commanderstrife/ADE-Bio_ClinicalBERT-NER")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("commanderstrife/ADE-Bio_ClinicalBERT-NER") model = AutoModelForTokenClassification.from_pretrained("commanderstrife/ADE-Bio_ClinicalBERT-NER", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from commanderstrife/ADE-Bio_ClinicalBERT-NER: direct link, hf CLI and curl.
- Browser
- Download file 431 MB
-
https://huggingface.co/commanderstrife/ADE-Bio_ClinicalBERT-NER/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://commanderstrife/ADE-Bio_ClinicalBERT-NER/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/commanderstrife/ADE-Bio_ClinicalBERT-NER/resolve/main/pytorch_model.bin
431 MB
- Xet hash:
- 42a1b3930c53695ded7ae8a345a74a91f3e4c32a3d14af182d3263ed75fa9fff
- Size of remote file:
- 431 MB
- SHA256:
- 61160e0d362e90695d90b3292006a0519e176f6eeb189f4e095aea65670e8384
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