Token Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
Eval Results (legacy)
Instructions to use shed-e/NER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shed-e/NER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="shed-e/NER")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("shed-e/NER") model = AutoModelForTokenClassification.from_pretrained("shed-e/NER", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from shed-e/NER: direct link, hf CLI and curl.
- Browser
- Download file 3.31 kB
-
https://huggingface.co/shed-e/NER/resolve/main/training_args.bin
- Command line
-
hf download hf://shed-e/NER/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/shed-e/NER/resolve/main/training_args.bin
3.31 kB
- Xet hash:
- 69460db4423338460b698051d127e4da1dbbbe9915bac4c2d5f93fc399dacb9b
- Size of remote file:
- 3.31 kB
- SHA256:
- e2d1338c071be7feb20819efd8e72356fdd1bf5c537f90c62f34a348600471a7
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