Instructions to use Supreeth/DeBERTa-Twitter-Emotion-Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Supreeth/DeBERTa-Twitter-Emotion-Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Supreeth/DeBERTa-Twitter-Emotion-Classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Supreeth/DeBERTa-Twitter-Emotion-Classification") model = AutoModelForSequenceClassification.from_pretrained("Supreeth/DeBERTa-Twitter-Emotion-Classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2091990d077f275daf00677b6d394da3d131649b2201ab050ab0d704c36a8f1e
- Size of remote file:
- 557 MB
- SHA256:
- 1abb32b62773e1c358016d984df316243d5ad090ffd085882772d8e704c3b666
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.