Instructions to use Ratul/sci_ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ratul/sci_ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Ratul/sci_ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Ratul/sci_ner") model = AutoModelForTokenClassification.from_pretrained("Ratul/sci_ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Ratul/sci_ner: direct link, hf CLI and curl.
- Browser
- Download file 437 MB
-
https://huggingface.co/Ratul/sci_ner/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Ratul/sci_ner/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Ratul/sci_ner/resolve/main/pytorch_model.bin
437 MB
- Xet hash:
- a6831fe60ff5b2e097a43e9f5ea23478fc0c9661bba0a1acf3903108be7d1d73
- Size of remote file:
- 437 MB
- SHA256:
- 9394b7382e88c60d3f18975d4a8f35c577180a0d4af81ff5f709854242dd6312
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