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--- |
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license: apache-2.0 |
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datasets: |
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- stanfordnlp/imdb |
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language: |
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- en |
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metrics: |
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- accuracy |
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library_name: transformers |
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tags: |
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- sentiment-analysis |
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- movie-analysis |
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- sentiment |
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- distilbert |
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- movie-reviews |
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--- |
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## Model Description |
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This model is a `distilbert-base-uncased` fine-tuned for sentiment analysis on the IMDb movie review dataset. The model is trained to classify movie reviews into positive or negative sentiment. |
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## Intended Use |
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The model is intended for sentiment analysis tasks, specifically to classify the sentiment of English-language movie reviews. It can be used by developers or data scientists who wish to include sentiment analysis features in their applications. |
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## Training Data |
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The model was fine-tuned on the IMDb movie review dataset available from the Hugging Face datasets library. The dataset consists of 50,000 movie reviews from IMDb, labeled as positive or negative. |
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## Training Procedure |
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The model was fine-tuned for 2 epochs with a batch size of 8, Adam optimizer with a learning rate of 2e-5. |
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## Ethical Considerations |
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This model may inherit biases present in the IMDb dataset, and its predictions should be reviewed with critical consideration, especially if used in sensitive contexts. |
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## Sample Usage in Python |
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Here's how you can use this model in Python: |
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```python |
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from transformers import pipeline |
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# Load the sentiment analysis pipeline |
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classifier = pipeline('sentiment-analysis', model='sarahai/movie-sentiment-analysis') |
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# Analyze sentiment |
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review = "I really enjoyed this movie from start to finish!" |
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result = classifier(review) |
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print(result) |
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``` |
