Instructions to use MU-NLPC/ner-gazetteers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MU-NLPC/ner-gazetteers with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="MU-NLPC/ner-gazetteers")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("MU-NLPC/ner-gazetteers") model = AutoModelForTokenClassification.from_pretrained("MU-NLPC/ner-gazetteers", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from MU-NLPC/ner-gazetteers: direct link, hf CLI and curl.
- Browser
- Download file 502 MB
-
https://huggingface.co/MU-NLPC/ner-gazetteers/resolve/4e5ea9601677926399571074b2e85916dc4afd1c/pytorch_model.bin
- Command line
-
hf download hf://MU-NLPC/ner-gazetteers@4e5ea9601677926399571074b2e85916dc4afd1c/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/MU-NLPC/ner-gazetteers/resolve/4e5ea9601677926399571074b2e85916dc4afd1c/pytorch_model.bin
502 MB
- Xet hash:
- 58a9debda20bee328d09b2d82e9df49cb6887820ac8afcb353b2fce20d2f4d67
- Size of remote file:
- 502 MB
- SHA256:
- fb73526d8b8543011fa7b5f9f07f6f1e815d069af3566cc3978749b22b9150c5
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