Instructions to use dexay/Ner2HgF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dexay/Ner2HgF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="dexay/Ner2HgF")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("dexay/Ner2HgF") model = AutoModelForTokenClassification.from_pretrained("dexay/Ner2HgF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from dexay/Ner2HgF: direct link, hf CLI and curl.
- Browser
- Download file 1.45 GB
-
https://huggingface.co/dexay/Ner2HgF/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://dexay/Ner2HgF/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/dexay/Ner2HgF/resolve/main/pytorch_model.bin
1.45 GB
- Xet hash:
- 849deada310c4f63a4ada288408ae4afb9b7de5424cc5771afb4ca5773a72435
- Size of remote file:
- 1.45 GB
- SHA256:
- 44a158960f5d35247afd822e0180fa8cdfcdcb049027c104a11442f7f0ca891f
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