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 pytorch_model.bin from roscazo/ner_ANAT_DISO: direct link, hf CLI and curl.
- Browser
- Download file 496 MB
-
https://huggingface.co/roscazo/ner_ANAT_DISO/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://roscazo/ner_ANAT_DISO/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/roscazo/ner_ANAT_DISO/resolve/main/pytorch_model.bin
496 MB
- Xet hash:
- cc163b0c4bd00fe85aa5a121e7d5e68ce180c1d36099365046ad22c4a08dfecf
- Size of remote file:
- 496 MB
- SHA256:
- bc21b0018e11dbdadb08a063a3bb8111c9be6a3080217e8b705da32e305436cc
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