Instructions to use Vamsi11/sentiment_analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Vamsi11/sentiment_analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Vamsi11/sentiment_analysis")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Vamsi11/sentiment_analysis") model = AutoModelForSequenceClassification.from_pretrained("Vamsi11/sentiment_analysis") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 721e1ec8522b7d82c43cda3356641df272d1da534d6662831b6916de28d40388
- Size of remote file:
- 5.18 kB
- SHA256:
- 20cb80842f21bbc0938b0673de27e417f18c7ca68949ae8cab4f5bd47b61ae20
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