Instructions to use andrewma5/bert-finetuned-ner-tutorial with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use andrewma5/bert-finetuned-ner-tutorial with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="andrewma5/bert-finetuned-ner-tutorial")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("andrewma5/bert-finetuned-ner-tutorial") model = AutoModelForTokenClassification.from_pretrained("andrewma5/bert-finetuned-ner-tutorial", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from andrewma5/bert-finetuned-ner-tutorial: direct link, hf CLI and curl.
- Browser
- Download file 431 MB
-
https://huggingface.co/andrewma5/bert-finetuned-ner-tutorial/resolve/main/tf_model.h5
- Command line
-
hf download hf://andrewma5/bert-finetuned-ner-tutorial/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/andrewma5/bert-finetuned-ner-tutorial/resolve/main/tf_model.h5
431 MB
- Xet hash:
- 3ec6b99eeb4a5b23703041d582749d04d7b0bbe32ac6ea83f21c502586025408
- Size of remote file:
- 431 MB
- SHA256:
- 2fca7a54e9e54db245a0bd87665620a81ed7a2784b01737a97ca43620eea4c23
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