Instructions to use sschet/bert-base-uncased_clinical-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sschet/bert-base-uncased_clinical-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="sschet/bert-base-uncased_clinical-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("sschet/bert-base-uncased_clinical-ner") model = AutoModelForTokenClassification.from_pretrained("sschet/bert-base-uncased_clinical-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from sschet/bert-base-uncased_clinical-ner: direct link, hf CLI and curl.
- Browser
- Download file 2.1 kB
-
https://huggingface.co/sschet/bert-base-uncased_clinical-ner/resolve/main/training_args.bin
- Command line
-
hf download hf://sschet/bert-base-uncased_clinical-ner/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/sschet/bert-base-uncased_clinical-ner/resolve/main/training_args.bin
2.1 kB
- Xet hash:
- 1e36663e512a6706cb5c1829ba943976d4bb79b1af61e4a376b8f8be81d799b1
- Size of remote file:
- 2.1 kB
- SHA256:
- 4e3f0d4c42ed125cb0e18463a06d056eef55c65dc3f317af6f1db3f4ad32d292
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