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--- |
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language: en |
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license: mit |
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--- |
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# Model Card |
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Bank Sentiment Classifier - distilBERT |
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Developed by: Richard Chai, https://www.linkedin.com/in/richardchai/ |
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This model has been fine-tuned for Bank User Sentiment Identification. |
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Currently, it identifies the following Sentiment: |
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'very negative': 0, |
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'negative': 1, |
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'neutral': 2, |
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'positive': 3, |
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'very positive': 4 |
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## Model Details |
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- **Model type**: Transformer-based (e.g., BERT, DistilBERT, etc.): distilBERT |
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- **Dataset**: Stanford Sentiment Treebank SST-5 or another sentiment dataset |
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- **Fine-tuning**: The model was fine-tuned for X epochs using a learning rate of Y on a dataset with Z samples. |
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## Usage |
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You can use this model to classify text sentiment as follows: |
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```python |
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from transformers import pipeline |
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# Check if GPU is available |
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device = 0 if torch.cuda.is_available() else -1 |
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model_checkpt = "richardchai/plp_sentiment_clr_distilbert" |
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clf = pipeline('text-classification', model="model_trained/distilbert", device=device) |
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result = clf(f"['please tell me more about your fixed deposit.', 'your savings rate is terrible!', 'Yay! I have finally paid off my loan!', 'I am rich, hurray!']") |
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print(result) |
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``` |
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