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--- |
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license: mit |
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language: |
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- en |
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base_model: |
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- distilbert/distilbert-base-uncased |
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--- |
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# DistilBERT Sentiment Analysis Model |
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This model is a fine-tuned version of **DistilBERT** for sentiment analysis on the **IMDb** dataset. It classifies movie reviews as either **positive** or **negative** based on the text content. |
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## Model Details |
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- **Model Type**: DistilBERT (a smaller and faster variant of BERT) |
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- **Task**: Sentiment Analysis |
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- **Dataset**: IMDb dataset containing movie reviews with labels (positive/negative) |
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- **Fine-Tuned On**: IMDb dataset |
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## Model Performance |
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This model was fine-tuned on the IMDb dataset for sentiment classification, achieving good performance for binary sentiment classification tasks (positive/negative). |
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## Usage |
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To use this model, you can load it from the Hugging Face Model Hub using the `transformers` library: |
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```python |
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from transformers import pipeline |
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# Load the model |
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classifier = pipeline('sentiment-analysis', model='dorukan/distilbert-base-uncased-bert-finetuned-imdb') |
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# Example usage |
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result = classifier("This movie was amazing!") |
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print(result) |
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``` |
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This will output a sentiment prediction for the given text. |
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## License |
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This model is licensed under the MIT License. For more information, see the LICENSE file. |
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## Acknowledgments |
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- **DistilBERT**: A smaller version of BERT, created by the Hugging Face team. |
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- **IMDb Dataset**: A collection of movie reviews used for sentiment classification, widely used in NLP tasks. |
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You can find more details about the model at the [Hugging Face model page](https://huggingface.co/dorukan/distil-bert-imdb). |
