Instructions to use Granoladata/contrast_classifier_biobert_v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Granoladata/contrast_classifier_biobert_v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Granoladata/contrast_classifier_biobert_v3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Granoladata/contrast_classifier_biobert_v3") model = AutoModelForSequenceClassification.from_pretrained("Granoladata/contrast_classifier_biobert_v3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9766a452c18a00ccb54e8b226192f49d7f11f760e701dfe0ebedd84c7099b3e5
- Size of remote file:
- 4.98 kB
- SHA256:
- 5a07de3a7952bfbd3064fbb49b23ea6a7eb1294983734b0fe51a8ca16f04b98c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.