Update README.md

README.md

@@ -8,19 +8,23 @@ metrics:
 model-index:
 - name: MiniLMv2-L6-H384-distilled-from-RoBERTa-Large-agentflow-distil
   results: []
+license: apache-2.0
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 should probably proofread and complete it, then remove this comment. -->
 
-# User Flow Text Classification 
+# LLM agent 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).
+This model identifies common events and patterns within the conversation flow. 
+Such events include an apology, where the LLM acknowledges a mistake.
+The flow labels can serve as foundational elements for sophisticated LLM analytics.
+
+It is a fined-tuned version of [MiniLMv2-L6-H384](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-agentflow-v2-onnx)
 
-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.
+This model is *only* for the LLM agent texts in the dialog. For the user texts [use this model](https://huggingface.co/minuva/MiniLMv2-userflow-v2).
 
-This model should be used only for agent dialogs.
 # Load the Model
 
 ```py
@@ -64,4 +68,4 @@ pipe("thats my mistake")
 
 # 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.