| language: en | |
| license: mit | |
| # Model Card | |
| Bank ACTION Classifier - DistilBERT | |
| Developed by: Richard Chai, https://www.linkedin.com/in/richardchai/ | |
| This model has been fine-tuned for Bank User Action/Intent Identification. | |
| Currently, it identifies the following actions: | |
| ['access', | |
| 'activate', | |
| 'apply', | |
| 'block', | |
| 'cancel', | |
| 'close', | |
| 'deposit', | |
| 'dispute', | |
| 'earn', | |
| 'exchange', | |
| 'find', | |
| 'inquire', | |
| 'link', | |
| 'open', | |
| 'pay', | |
| 'receive', | |
| 'redeem', | |
| 'refund', | |
| 'renew', | |
| 'report', | |
| 'reset', | |
| 'retrieve', | |
| 'schedule', | |
| 'select', | |
| 'transfer', | |
| 'unblock', | |
| 'unknown', | |
| 'unlink', | |
| 'update', | |
| 'verify', | |
| 'withdraw'] | |
| ## Model Details | |
| - **Model type**: Transformer-based (e.g., BERT, DistilBERT, etc.): DistilBERT | |
| - **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_action_clr_distilbert" | |
| clf = pipeline('text-classification', model="model_trained/distilbert", device=device) | |
| result = clf(f"['please tell me more about your fixed deposit.', 'I want to deposit money into my savings account.']") | |
| print(result) | |
| ``` | |