Instructions to use jamie613/custom_BERT_NER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jamie613/custom_BERT_NER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="jamie613/custom_BERT_NER")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("jamie613/custom_BERT_NER") model = AutoModelForTokenClassification.from_pretrained("jamie613/custom_BERT_NER", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from jamie613/custom_BERT_NER: direct link, hf CLI and curl.
- Browser
- Download file 4.98 kB
-
https://huggingface.co/jamie613/custom_BERT_NER/resolve/main/training_args.bin
- Command line
-
hf download hf://jamie613/custom_BERT_NER/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/jamie613/custom_BERT_NER/resolve/main/training_args.bin
4.98 kB
- Xet hash:
- a8869053285554355ea19ec651c63102ea6e9d863ede4aa37a4deae579e0f62e
- Size of remote file:
- 4.98 kB
- SHA256:
- 00581e252bc0291dce0f52a5413ec366bf060865c5c06f7331038566167749de
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