Update README.md

README.md

@@ -12,4 +12,42 @@ tags:
 - fine-tuning
 - chatbot
 - llm
----
+license: cdla-sharing-1.0
+---
+# fintech-chatbot-t5
+
+## Model Description
+This model was fine-tuned using a [retail banking chatbot dataset](https://huggingface.co/datasets/bitext/Bitext-retail-banking-llm-chatbot-training-dataset/tree/main). 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](https://huggingface.co/datasets/bitext/Bitext-retail-banking-llm-chatbot-training-dataset/tree/main)
+- **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:
+
+```python
+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]))
+
+