Instructions to use Prience91/ner_model_output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Prience91/ner_model_output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Prience91/ner_model_output")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Prience91/ner_model_output") model = AutoModelForTokenClassification.from_pretrained("Prience91/ner_model_output", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Prience91/ner_model_output: direct link, hf CLI and curl.
- Browser
- Download file 5.2 kB
-
https://huggingface.co/Prience91/ner_model_output/resolve/main/training_args.bin
- Command line
-
hf download hf://Prience91/ner_model_output/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Prience91/ner_model_output/resolve/main/training_args.bin
5.2 kB
- Xet hash:
- 8229864cc80d87f38c187b656c1909ddc6a9a943e5a649cd7f708ede04c8c586
- Size of remote file:
- 5.2 kB
- SHA256:
- 1d88e26e17ae31ce9a772c36d1c551776ce3f2a1f26c28eff8be7e860aa32fc3
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