Model Card

Bank Sentiment Classifier - distilBERT 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.): 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:

from transformers import pipeline

# Check if GPU is available
device = 0 if torch.cuda.is_available() else -1

model_checkpt = "richardchai/plp_sentiment_clr_distilbert"
clf = pipeline('text-classification', model="model_trained/distilbert", 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!', 'I am rich, hurray!']")
print(result)