Instructions to use cp500/hebert_medical_ner_fixed_labels_v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cp500/hebert_medical_ner_fixed_labels_v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="cp500/hebert_medical_ner_fixed_labels_v3")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("cp500/hebert_medical_ner_fixed_labels_v3") model = AutoModelForTokenClassification.from_pretrained("cp500/hebert_medical_ner_fixed_labels_v3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from cp500/hebert_medical_ner_fixed_labels_v3: direct link, hf CLI and curl.
- Browser
- Download file 4.09 kB
-
https://huggingface.co/cp500/hebert_medical_ner_fixed_labels_v3/resolve/main/training_args.bin
- Command line
-
hf download hf://cp500/hebert_medical_ner_fixed_labels_v3/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/cp500/hebert_medical_ner_fixed_labels_v3/resolve/main/training_args.bin
4.09 kB
- Xet hash:
- fea1e378bbe4dccdc1a32207cbfded7257eacfdaa1ef0950c4451550e04cdcec
- Size of remote file:
- 4.09 kB
- SHA256:
- a7dcf5f6d29f542cd3ebff9ea244a9e6d087aa11c84c303aeae6844cd7a0ed7c
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