Instructions to use Group209/Sentiment_Analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Group209/Sentiment_Analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Group209/Sentiment_Analysis")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Group209/Sentiment_Analysis", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d030b5d1e3429648991b67e3c920b40ebf277328e4bc40bf6c0a604d6577c581
- Size of remote file:
- 438 MB
- SHA256:
- 541f60e3c6004919c332fbfe146ec10d84c5a86c37a6aaf7ccc47bc9b8e55377
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.