Instructions to use Kaludi/Reviews-Sentiment-Analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kaludi/Reviews-Sentiment-Analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Kaludi/Reviews-Sentiment-Analysis")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Kaludi/Reviews-Sentiment-Analysis") model = AutoModelForSequenceClassification.from_pretrained("Kaludi/Reviews-Sentiment-Analysis") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6d68e0b85abcb0485e5016ae11ecf05f3f66b2573e33a12c5e5c908444480f1b
- Size of remote file:
- 738 MB
- SHA256:
- 383b5d28d8fb1ba8369e7abda10783a063b5dc6cb4a1d2cfd3af119509d8ac19
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