language:
- en
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 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.