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 Telco Dataset](https://huggingface.co/datasets/bitext/Bitext-telco-llm-chatbot-training-dataset) comprising various telco-related intents, including: set_usage_limits, activate_phone, check_mobile_payments, check_signal_coverage, invoices, and more. Totaling 25 intents, and each intent is represented by approximately 1000 examples.
+The model was fine-tuned on the [Bitext Telco Dataset](https://huggingface.co/datasets/bitext/Bitext-telco-llm-chatbot-training-dataset) comprising various telco-related intents, including: set_usage_limits, activate_phone, check_mobile_payments, check_signal_coverage, invoices, and more. Totaling 26 intents, and each intent is represented by approximately 1000 examples.
 
 This comprehensive training helps the model address a broad spectrum of telco-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 telco.