Update README.md

README.md

@@ -1,16 +1,21 @@
 ---
 language:
 - en
+license: apache-2.0
 ---
 
-# User Flow Text Classification 
+# LLM user flow classification 
 
-This model is a fined-tuned version of [nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large](https://huggingface.co/nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large).
-The quantized version in ONNX format can be found [here](https://huggingface.co/minuva/MiniLMv2-userflow-v2-onnx)
+This model identifies common events and patterns within the conversation flow. 
+Such events include, for example, complaint, when a user expresses dissatisfaction.
+The flow labels can serve as foundational elements for sophisticated LLM analytics.
 
-The 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.
+It is ONNX quantized and is a fined-tune of [MiniLMv2-L6-H384](https://huggingface.co/nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large).
+The base model can be found [here](https://huggingface.co/minuva/MiniLMv2-agentflow-v2)
+
+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). 
 
-This model should be used *only* for user dialogs.
 
 # Load the Model
 
@@ -79,4 +84,4 @@ pipe("This is wrong")
 
 # 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.