Token Classification
Transformers
Safetensors
English
roberta
ner
named-entity-recognition
Eval Results (legacy)
Instructions to use guishe/nuner-v1_ontonotes5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use guishe/nuner-v1_ontonotes5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="guishe/nuner-v1_ontonotes5")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("guishe/nuner-v1_ontonotes5") model = AutoModelForTokenClassification.from_pretrained("guishe/nuner-v1_ontonotes5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from guishe/nuner-v1_ontonotes5: direct link, hf CLI and curl.
- Browser
- Download file 4.35 kB
-
https://huggingface.co/guishe/nuner-v1_ontonotes5/resolve/main/training_args.bin
- Command line
-
hf download hf://guishe/nuner-v1_ontonotes5/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/guishe/nuner-v1_ontonotes5/resolve/main/training_args.bin
4.35 kB
- Xet hash:
- be25cea0e8b0619dc86ab530800be50a298f5c70bdd07df6587dbcf6418d2f54
- Size of remote file:
- 4.35 kB
- SHA256:
- 01dfbae9b7fb93248723cfac69b20bd132c7109fbfd5163699a70392afa49549
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