Instructions to use Jean-Baptiste/camembert-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jean-Baptiste/camembert-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Jean-Baptiste/camembert-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Jean-Baptiste/camembert-ner") model = AutoModelForTokenClassification.from_pretrained("Jean-Baptiste/camembert-ner", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download model.onnx from Jean-Baptiste/camembert-ner: direct link, hf CLI and curl.
- Browser
- Download file 440 MB
-
https://huggingface.co/Jean-Baptiste/camembert-ner/resolve/main/model.onnx
- Command line
-
hf download hf://Jean-Baptiste/camembert-ner/model.onnx
-
curl -L -o model.onnx https://huggingface.co/Jean-Baptiste/camembert-ner/resolve/main/model.onnx
440 MB
- Xet hash:
- b97d6db9336085ad6ef981c7fb1c950b6904c1135d38e89ba50e22d3b2c6b075
- Size of remote file:
- 440 MB
- SHA256:
- c9dfa2ec9e0bfad82d606782ba3e0ab94c001c46481514e0658fc20a624845c5
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