---
base_model: nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large
tags:
- generated_from_trainer
- text-classification
- multi-class-classification

metrics:
- accuracy
- f1
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. -->

# LLM agent flow classification 

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)

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).

# Load the Model

```py
from transformers import pipeline

pipe = pipeline(model='minuva/MiniLMv2-agentflow-v2', task='text-classification')
pipe("thats my mistake")
# [{'label': 'agent_apology_error_mistake', 'score': 0.9965628981590271}]
```
# Categories Explanation

<details>
  <summary>Click to expand!</summary>
  
    - OTHER: Responses or actions by the agent that do not fit into the predefined categories or are outside the scope of the specific interactions listed.

    - agent_apology_error_mistake: When the agent acknowledges an error or mistake in the information provided or in the handling of the request.

    - agent_apology_unsatisfactory: The agent expresses an apology for providing an unsatisfactory response or for any dissatisfaction experienced by the user.

    - agent_didnt_understand: Indicates that the agent did not understand the user's request or question.

    - agent_limited_capabilities: The agent communicates its limitations in addressing certain requests or providing certain types of information.

    - agent_refuses_answer: When the agent explicitly refuses to answer a question or fulfill a request, due to policy restrictions or ethical considerations.

    - image_limitations": The agent points out limitations related to handling or interpreting images.

    - no_information_doesnt_know": The agent indicates that it has no information available or does not know the answer to the user's question.

    - success_and_followup_assistance": The agent successfully provides the requested information or service and offers further assistance or follow-up actions if needed.
</details>

<br>


# Metrics in our private test dataset
| Model (params)    |    Loss      |    Accuracy |  F1 |
|--------------------|-------------|----------|--------| 
| minuva/MiniLMv2-agentflow-v2 (33M) |   0.1540 | 0.9616 | 0.9618 |

# Deployment

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.