Update README.md

README.md

@@ -59,7 +59,8 @@ This model utilizes the `MistralForCausalLM` architecture with a `LlamaTokenizer
 
 ## Training Data
 
-The model was fine-tuned on the [Bitext Travel Dataset](https://huggingface.co/datasets/bitext/Bitext-travel-llm-chatbot-training-dataset) comprising various travel-related intents, including: book_flight, choose_seat, check_arrival_time, book_trip, purchase_flight_insurance, check_cancellation_fee, check_baggage_allowance, and more. Totaling 33 intents, and each intent is represented by approximately 1000 examples. This comprehensive training helps the model address a broad spectrum of travel-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 travel.
+The model was fine-tuned on the [Bitext Travel Dataset](https://huggingface.co/datasets/bitext/Bitext-travel-llm-chatbot-training-dataset) comprising various travel-related intents, including: book_flight, choose_seat, check_arrival_time, book_trip, purchase_flight_insurance, check_cancellation_fee, check_baggage_allowance, and more.
+Totaling 33 intents, and each intent is represented by approximately 1000 examples. This comprehensive training helps the model address a broad spectrum of travel-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 travel.
 
 ## Training Procedure