README.md

metadata

base_model: nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
model-index:
  - name: MiniLMv2-L6-H384-distilled-from-RoBERTa-Large-agentflow-distil
    results: []

User Flow Text Classification

This model is a fined-tuned version of nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large. The quantized version in ONNX format can be found here

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 should be used only for agent dialogs.

Load the Model

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

Click to expand!

- 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.

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 repository to see how to easily deploy this (quantized) model in a serverless environment with fast CPU inference and light resource utilization.