Instructions to use MU-NLPC/ahisto-ner-model-tds2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MU-NLPC/ahisto-ner-model-tds2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="MU-NLPC/ahisto-ner-model-tds2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("MU-NLPC/ahisto-ner-model-tds2") model = AutoModelForTokenClassification.from_pretrained("MU-NLPC/ahisto-ner-model-tds2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from MU-NLPC/ahisto-ner-model-tds2: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/MU-NLPC/ahisto-ner-model-tds2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://MU-NLPC/ahisto-ner-model-tds2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/MU-NLPC/ahisto-ner-model-tds2/resolve/main/pytorch_model.bin
1.11 GB
- Xet hash:
- 8b1662c6f2e740a832215425e7434bcf11d8f61d87c5bd0838218079e293cf7b
- Size of remote file:
- 1.11 GB
- SHA256:
- 7baa5a6fd0bd8f10fe8abddcc77a8f5c710408b1074504849a91d553aa84e9ea
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