Instructions to use kSaluja/test-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kSaluja/test-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="kSaluja/test-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("kSaluja/test-ner") model = AutoModelForTokenClassification.from_pretrained("kSaluja/test-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from kSaluja/test-ner: direct link, hf CLI and curl.
- Browser
- Download file 3.06 kB
-
https://huggingface.co/kSaluja/test-ner/resolve/main/training_args.bin
- Command line
-
hf download hf://kSaluja/test-ner/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/kSaluja/test-ner/resolve/main/training_args.bin
3.06 kB
- Xet hash:
- 9d921e56eb57afbd34ad495dbaea2e4ede6cd4a6caf3b30aa3f3cadef07433fd
- Size of remote file:
- 3.06 kB
- SHA256:
- 1f1e552121939a93ad79c16c70727a17b809289700c76d9bc5b6ac8da235f46a
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