fintech-chatbot-t5
Model Description
This model was fine-tuned using a retail banking chatbot dataset. It is based on the T5-small architecture and is capable of answering common banking-related queries like account balances, transaction details, card activations, and more.
The model has been trained to generate responses to banking-related customer queries and is suited for use in automated customer service systems or virtual assistants.
Model Details
- Model Type: T5-small
- Training Dataset: retail banking chatbot dataset
- Tasks: Natural Language Generation (NLG)
- Languages Supported: English
Training Details
- Number of Epochs: 3
- Training Loss: 0.79
- Evaluation Loss: 0.46
- Evaluation Metric: Mean Squared Error
- Batch Size: 8
How to Use the Model
You can load and use this model with the following code:
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("cuneytkaya/fintech-chatbot-t5")
model = T5ForConditionalGeneration.from_pretrained("cuneytkaya/fintech-chatbot-t5")
input_text = "How can I activate my credit card?"
inputs = tokenizer.encode(input_text, return_tensors="pt")
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))