Instructions to use ghadeermobasher/BioRED-Dis-WLT-320-BlueBERT-100 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ghadeermobasher/BioRED-Dis-WLT-320-BlueBERT-100 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ghadeermobasher/BioRED-Dis-WLT-320-BlueBERT-100")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ghadeermobasher/BioRED-Dis-WLT-320-BlueBERT-100") model = AutoModelForTokenClassification.from_pretrained("ghadeermobasher/BioRED-Dis-WLT-320-BlueBERT-100") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d04a4af70549579efcc1ebbffe3ab1445d99e98633c14f19871e2bc40f59170a
- Size of remote file:
- 436 MB
- SHA256:
- 76d1d9e81698af1ed205e01a75b5b55eab89757689c977a3fe2847174f27ea43
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.