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
Update README.md
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README.md
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- text: "I am so impressed with this product! The quality is outstanding and it has exceeded all of my expectations. The customer service team was also incredibly helpful and responsive to any questions I had. I highly recommend this product to anyone in need of a top-notch, reliable solution."
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example_title: "Positive Example 2"
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datasets:
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- Kaludi
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co2_eq_emissions:
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emissions: 24.76716845191504
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- text: "I am so impressed with this product! The quality is outstanding and it has exceeded all of my expectations. The customer service team was also incredibly helpful and responsive to any questions I had. I highly recommend this product to anyone in need of a top-notch, reliable solution."
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example_title: "Positive Example 2"
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datasets:
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- Kaludi/data-reviews-sentiment-analysis
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co2_eq_emissions:
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emissions: 24.76716845191504
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