Update README.md

README.md

@@ -60,7 +60,7 @@ This model utilizes the `MistralForCausalLM` architecture with a `LlamaTokenizer
 
 ## Training Data
 
-The model was fine-tuned on the [Bitext Insurance Dataset](https://huggingface.co/datasets/bitext/Bitext-insurance-llm-chatbot-training-dataset) comprising various insurance-related intents, including: buy_insurance_policy, schedule_appointment, check_payments, calculate_insurance_quote, negotiate_settlement, information_home_insurance, and more. Totaling 39 intents, and each intent is represented by approximately 1000 examples.
+The model was fine-tuned on the [Bitext Insurance Dataset](https://huggingface.co/datasets/bitext/Bitext-insurance-llm-chatbot-training-dataset) comprising various insurance-related intents, including: buy_insurance_policy, schedule_appointment, check_payments, calculate_insurance_quote, negotiate_settlement, and more. Totaling 39 intents, and each intent is represented by approximately 1000 examples.
 
 This comprehensive training helps the model address a broad spectrum of insurance-related questions effectively. The dataset follows the same structured approach as our dataset published on Hugging Face as [bitext/Bitext-customer-support-llm-chatbot-training-dataset](https://huggingface.co/datasets/bitext/Bitext-customer-support-llm-chatbot-training-dataset), but with a focus on insurance.
 
@@ -70,7 +70,7 @@ This comprehensive training helps the model address a broad spectrum of insuranc
 
 - **Optimizer**: AdamW
 - **Learning Rate**: 0.0002 with a cosine learning rate scheduler
-- **Epochs**: 4
+- **Epochs**: 1
 - **Batch Size**: 4
 - **Gradient Accumulation Steps**: 4
 - **Maximum Sequence Length**: 8192 tokens