Instructions to use hadA42/ner-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hadA42/ner-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="hadA42/ner-classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hadA42/ner-classification") model = AutoModelForTokenClassification.from_pretrained("hadA42/ner-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from hadA42/ner-classification: direct link, hf CLI and curl.
- Browser
- Download file 5.27 kB
-
https://huggingface.co/hadA42/ner-classification/resolve/main/training_args.bin
- Command line
-
hf download hf://hadA42/ner-classification/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/hadA42/ner-classification/resolve/main/training_args.bin
5.27 kB
- Xet hash:
- 6db6a0db51e3e007b05adfbdd91921f4e18c25ee72a987bba243fcbfd9c5cbef
- Size of remote file:
- 5.27 kB
- SHA256:
- 4ecd8f8f0a1e3d4c6e92af7976550af28d5182802a703fe201acb4e0bc1fe519
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