README.md

---
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)
```