Update README.md

README.md

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 ## Model Description
 
-This model, "Mistral-7B-Events-Ticketing", is a fine-tuned version of the [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2), specifically tailored for the Events and Ticketing domains. It is optimized to answer questions and assist users with various Events and Ticketing-related procedures. It has been trained using hybrid synthetic data generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools.
+This model, "Mistral-7B-Events-Ticketing", is a fine-tuned version of the [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2), specifically tailored for the Events and Ticketing domains. It is optimized to answer questions and assist users with various Events-related and Ticketing-related procedures. It has been trained using hybrid synthetic data generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools.
  
 The goal of this model is to show that a generic verticalized model makes customization for a final use case much easier. An overview of this approach can be found at: [From General-Purpose LLMs to Verticalized Enterprise Models](https://www.bitext.com/blog/general-purpose-models-verticalized-enterprise-genai/)
 
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 ## Model Architecture
 
-This model utilizes the `MistralForCausalLM` architecture with a `LlamaTokenizer`, ensuring it retains the foundational capabilities of the base model while being specifically enhanced for events and ticketing-related interactions.
+This model utilizes the `MistralForCausalLM` architecture with a `LlamaTokenizer`, ensuring it retains the foundational capabilities of the base model while being specifically enhanced for events-related and ticketing-related interactions.
 
 ## Training Data
 
-The model was fine-tuned on the [Bitext Events and Ticketing Dataset](https://huggingface.co/datasets/bitext/Bitext-events-ticketing-llm-chatbot-training-dataset) comprising various events and ticketing-related intents, including: book_hotel, store_luggage, book_parking_space, get_refund, search_hotel, host_event, check_in, and more. Totaling 25 intents, and each intent is represented by approximately 1000 examples.
+The model was fine-tuned on the [Bitext Events and Ticketing Dataset](https://huggingface.co/datasets/bitext/Bitext-events-ticketing-llm-chatbot-training-dataset) comprising various events-related and ticketing-related intents, including: book_hotel, store_luggage, book_parking_space, get_refund, search_hotel, host_event, check_in, and more. Totaling 25 intents, and each intent is represented by approximately 1000 examples.
 
-This comprehensive training helps the model address a broad spectrum of events and ticketing-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 events and ticketing.
+This comprehensive training helps the model address a broad spectrum of events-related and ticketing-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 events and ticketing.
 
 ## Training Procedure