Instructions to use roscazo/ner_ANAT_DISO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use roscazo/ner_ANAT_DISO with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="roscazo/ner_ANAT_DISO")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("roscazo/ner_ANAT_DISO") model = AutoModelForTokenClassification.from_pretrained("roscazo/ner_ANAT_DISO", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from roscazo/ner_ANAT_DISO: direct link, hf CLI and curl.
- Browser
- Download file 3.39 kB
-
https://huggingface.co/roscazo/ner_ANAT_DISO/resolve/main/training_args.bin
- Command line
-
hf download hf://roscazo/ner_ANAT_DISO/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/roscazo/ner_ANAT_DISO/resolve/main/training_args.bin
3.39 kB
- Xet hash:
- a3bd636ab7ffff12f66ac656e64986c81a31ac65614f47eb5c9241f18bfcf902
- Size of remote file:
- 3.39 kB
- SHA256:
- ab9ff0503273bf3b6fd0fc297c672342b9ae23806ca08861d39ceea57394df35
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