Instructions to use ghadeermobasher/BioNLP13CG-Modified-scibert-uncased_latest with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ghadeermobasher/BioNLP13CG-Modified-scibert-uncased_latest with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ghadeermobasher/BioNLP13CG-Modified-scibert-uncased_latest")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ghadeermobasher/BioNLP13CG-Modified-scibert-uncased_latest") model = AutoModelForTokenClassification.from_pretrained("ghadeermobasher/BioNLP13CG-Modified-scibert-uncased_latest") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9287e5a26b037450c156effaa4bcf5b5e496e87250f0598f9c8a4f10ea505c18
- Size of remote file:
- 2.86 kB
- SHA256:
- bdb4cf61b64e3caf26dc545e8b2979844aa5f34011d91953206faaec36e7761a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.