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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

A flow label 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 user dialogs.

Load the Model 

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

Click to expand!

  • 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.


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