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---
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)
```
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