Instructions to use Group209/Sentiment_Analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Group209/Sentiment_Analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Group209/Sentiment_Analysis")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Group209/Sentiment_Analysis", dtype="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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- sentiment-analysis
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- transformers
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- Transformateurs
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---
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Sentiment Analysis Model for Hotel Reviews
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Limitations and Bias
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The model is trained on English data, so it might not perform well on reviews in other languages.
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Furthermore, it might be biased towards certain phrases or words that are commonly used in the training dataset.
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- sentiment-analysis
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- transformers
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- Transformateurs
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pipeline_tag: text-classification
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---
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Sentiment Analysis Model for Hotel Reviews
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Limitations and Bias
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The model is trained on English data, so it might not perform well on reviews in other languages.
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Furthermore, it might be biased towards certain phrases or words that are commonly used in the training dataset.
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