Instructions to use jdavit/bert-finetuned-ner-21 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jdavit/bert-finetuned-ner-21 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="jdavit/bert-finetuned-ner-21")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("jdavit/bert-finetuned-ner-21") model = AutoModelForTokenClassification.from_pretrained("jdavit/bert-finetuned-ner-21", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from jdavit/bert-finetuned-ner-21: direct link, hf CLI and curl.
- Browser
- Download file 5.11 kB
-
https://huggingface.co/jdavit/bert-finetuned-ner-21/resolve/main/training_args.bin
- Command line
-
hf download hf://jdavit/bert-finetuned-ner-21/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/jdavit/bert-finetuned-ner-21/resolve/main/training_args.bin
5.11 kB
- Xet hash:
- c30285440b0baa7825469b2d7782a821fb8a928769bd4c706a663bf8ae499b22
- Size of remote file:
- 5.11 kB
- SHA256:
- c1d30bb90170e78a996345824233da8c04f390a09b6e9e75e2ade17443aa74ec
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