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