Updated readme

README.md

@@ -1,16 +1,16 @@
 ---
 language:
 - en
+license: apache-2.0
 ---
 
-# Text Classification of conversation flow
+# LLM user flow classification
 
-This a ONNX quantized model and is fined-tuned version of [nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large](https://huggingface.co/nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large).
-The original model can be found [here](https://huggingface.co/minuva/MiniLMv2-userflow-v2)
+This a ONNX quantized model and is fined-tuned version of [MiniLMv2-L6-H384](https://huggingface.co/nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large).
 
-This model identifies common events and patterns within the conversation flow. Such events include an apology, where the agent acknowledges a mistake, and a complaint, when a user expresses dissatisfaction.
+This model identifies common events and patterns within the conversation flow. Such events include, for example, complaint, when a user expresses dissatisfaction.
 
-This model should be used *only* for user dialogs.
+This model is used *only* for the user texts. For the LLM texts in the dialog use this [agent model](https://huggingface.co/minuva/MiniLMv2-agentflow-v2-onnx). 
 
 
 # Optimum
@@ -195,4 +195,4 @@ res
 
 # Deployment
 
-Check [our repository](https://github.com/minuva/flow-cloudrun) to see how to easily deploy this (quantized) model in a serverless environment with fast CPU inference and light resource utilization.
+Check [our llm-flow-classification repository](https://github.com/minuva/llm-flow-classification) for a FastAPI and ONNX based server to deploy this model on CPU devices.