Instructions to use sbcBI/sentiment_analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sbcBI/sentiment_analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sbcBI/sentiment_analysis")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sbcBI/sentiment_analysis") model = AutoModelForSequenceClassification.from_pretrained("sbcBI/sentiment_analysis") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4b88a3d5b6da711a08129fe2f4dead6d40fcab9e692e34b4828186b445478023
- Size of remote file:
- 268 MB
- SHA256:
- 23e0ab922c7d871688462750ea36e813b6f846fda7e3ea2cab93a19a44f64ef4
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