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:
- 872f330d05b827ed01fd665ae68be757da3e8cd5ea33bcf1f02ea12e572c7a46
- Size of remote file:
- 3.06 kB
- SHA256:
- bbd5f5c1c8b780c3f9c86a408867237d2c57d1fe91044c7a8933071d69edac2b
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