Instructions to use filippo-baglini/biobert_finetuned_ncbi4classes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use filippo-baglini/biobert_finetuned_ncbi4classes with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="filippo-baglini/biobert_finetuned_ncbi4classes")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("filippo-baglini/biobert_finetuned_ncbi4classes") model = AutoModelForTokenClassification.from_pretrained("filippo-baglini/biobert_finetuned_ncbi4classes", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from filippo-baglini/biobert_finetuned_ncbi4classes: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/filippo-baglini/biobert_finetuned_ncbi4classes/resolve/main/training_args.bin
- Command line
-
hf download hf://filippo-baglini/biobert_finetuned_ncbi4classes/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/filippo-baglini/biobert_finetuned_ncbi4classes/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- 8bc5be861fe7a5ad17f55248b3972a92f6975f11bab4c8c2b1a33dde498e5029
- Size of remote file:
- 5.3 kB
- SHA256:
- a577e4e82ab399c923b37ced1b7881f3e13ab38d067d8f3870db74976cb1a4ae
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