Instructions to use knkarthick/Sentiment-Analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use knkarthick/Sentiment-Analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="knkarthick/Sentiment-Analysis")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("knkarthick/Sentiment-Analysis") model = AutoModelForSequenceClassification.from_pretrained("knkarthick/Sentiment-Analysis") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- de96942de0344f84515de75d9e26290d81909535c2bfaa2513a5f520433cf3a4
- Size of remote file:
- 501 MB
- SHA256:
- 4d24a3e32a88ed1c4e5b789fc6644e2e767500554e954b27dccf52a8e762cbae
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