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  size_categories:
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  - 1K<n<10K
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  ---
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- labels: 'ask_clarify', 'neutral_feedback', 'ask_repeat', 'positive_feedback', 'negative_feedback', 'ask_confirmation', 'stop'
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  size_categories:
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  - 1K<n<10K
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  ---
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+ # Dataset: Core Intents
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+ The **Core Intents** dataset contains feedback-related utterances classified into seven key categories. This dataset is designed to help train voice assistants in handling fundamental types of feedback and adjusting their behavior accordingly.
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+ ## Labels
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+ The dataset includes the following labels:
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+ - **ask_clarify**: Requests for clarification, where the user seeks clearer explanations.
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+ - **ask_confirmation**: Requests for confirmation, where the user asks to validate a previous command or statement.
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+ - **ask_repeat**: Requests for repetition, where the user asks for something to be repeated.
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+ - **negative_feedback**: Negative responses or corrections, indicating dissatisfaction or disagreement.
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+ - **positive_feedback**: Positive responses, expressing satisfaction or agreement.
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+ - **neutral_feedback**: Neutral responses, where the user shows indifference or general acceptance.
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+ - **stop**: Commands indicating the user wants to halt an action or process.
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+ ## Examples
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+ Sample utterances for each label:
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+ - **ask_clarify**:
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+ - "your response was not clear"
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+ - "your words were not so clear to me"
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+ - **ask_confirmation**:
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+ - "can you check and confirm my last command please"
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+ - "can you check and confirm it"
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+ - **ask_repeat**:
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+ - "would you try again please"
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+ - "would you try what you said again"
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+ - **negative_feedback**:
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+ - "command wrong"
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+ - "dammit, I did not say it"
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+ - **positive_feedback**:
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+ - "thanks, that's amazing"
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+ - "that is excellent, much appreciated"
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+ - **neutral_feedback**:
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+ - "anything is fine"
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+ - "any one would be good to me"
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+
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+ ## Purpose
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+ This dataset aims to cover core interactions a voice assistant should manage to provide responsive and adaptive behavior in real-time communication. It can be used in tasks such as:
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+ - Intent classification
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+ - Dialogue system training
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+ - Feedback management in voice assistants