---
language:
- en
license: apache-2.0
---

# LLM user flow classification 

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.

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). 


# Load the Model

```py
from transformers import pipeline

pipe = pipeline(model='minuva/MiniLMv2-userflow-v2', task='text-classification')
pipe("This is wrong")
# [{'label': 'model_wrong_or_try_again', 'score': 0.9729849100112915}] 
```
# Categories Explanation

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

  - agrees_praising_thanking: When the user agrees with the provided information, offers praise, or expresses gratitude.

  - asks_source: The user requests the source of the information or the basis for the answer provided.

  - continue: Indicates a prompt for the conversation to proceed or continue without a specific directional change.

   - continue_or_finnish_code: Signals either to continue with the current line of discussion or code execution, or to conclude it.

  - improve_or_modify_answer: The user requests an improvement or modification to the provided answer.

  -  lack_of_understandment: Reflects the user's or agent confusion or lack of understanding regarding the information provided.

   - model_wrong_or_try_again: Indicates that the model's response was incorrect or unsatisfactory, suggesting a need to attempt another answer.

   - more_listing_or_expand: The user requests further elaboration, expansion from the given list by the agent.

   - repeat_answers_or_question: The need to reiterate a previous answer or question.

  - request_example: The user asks for examples to better understand the concept or answer provided.

  - user_complains_repetition: The user notes that the information or responses are repetitive, indicating a need for new or different content.

  - user_doubts_answer: The user expresses skepticism or doubt regarding the accuracy or validity of the provided answer.

  - user_goodbye: The user says goodbye to the agent.

  - user_reminds_question: The user reiterates the question.

  - user_wants_agent_to_answer: The user explicitly requests a response from the agent, when the agent refuses to do so.

  - user_wants_explanation: The user seeks an explanation behind the information or answer provided.

  - user_wants_more_detail: Indicates the user's desire for more comprehensive or detailed information on the topic.

  - user_wants_shorter_longer_answer: The user requests that the answer be condensed or expanded to better meet their informational needs.

  - user_wants_simplier_explanation: The user seeks a simpler, more easily understood explanation.

  - user_wants_yes_or_no: The user is asking for a straightforward affirmative or negative answer, without additional detail or explanation.
</details>

<br>


# Metrics in our private test dataset
| Model (params)    |    Loss      |    Accuracy |  F1 |
|--------------------|-------------|----------|--------| 
| minuva/MiniLMv2-userflow-v2 (33M) |   0.6738 | 0.7236 | 0.7313 |

# 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.