Instructions to use Goutham-Vignesh/ContributionSentClassification-scibert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Goutham-Vignesh/ContributionSentClassification-scibert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Goutham-Vignesh/ContributionSentClassification-scibert")# Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("Goutham-Vignesh/ContributionSentClassification-scibert", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6f6883aae70a9650c0a4a09da04752d67f06ef4a3446cec483d0e701852fa236
- Size of remote file:
- 440 MB
- SHA256:
- a088019d8eb108c959fb50cbb6ad5e12081e1ebfa617ba0cef22608d9e944456
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.