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README.md
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size_categories:
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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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## 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
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