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")# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("Goutham-Vignesh/ContributionSentClassification-scibert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from Goutham-Vignesh/ContributionSentClassification-scibert: direct link, hf CLI and curl.
- Browser
- Download file 440 MB
-
https://huggingface.co/Goutham-Vignesh/ContributionSentClassification-scibert/resolve/refs%2Fpr%2F1/model.safetensors
- Command line
-
hf download hf://Goutham-Vignesh/ContributionSentClassification-scibert@refs/pr/1/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/Goutham-Vignesh/ContributionSentClassification-scibert/resolve/refs%2Fpr%2F1/model.safetensors
440 MB
- Xet hash:
- 4b534f0f9ad4aa801ada75fc359ab89e724db80a7f5eb54da4a463554bb702ee
- Size of remote file:
- 440 MB
- SHA256:
- d152205b8a7983b2f1fe66f84d3c7307360f09c15f97a1781cfc14106e2c38fb
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