---
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)
```