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  ├── multilabel : multilabel dataset (including training and testing split)
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  └── README.md : documentation and card metadata
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  ```
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- **Note:** To safeguard user identity and uphold the integrity of this dataset, all user mentions have been anonymized as "@user," and any references to external websites have been omitted
 
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  ## Annotation and voting process
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  To generate the binary matrices, we employed a straightforward voting process. Three distinct evaluations were assigned to each document. In cases where a document received two or more identical classifications, the adopted value is set to 1; otherwise, it is marked as 0.Raw data can be checked into the repository in the [project repository](https://github.com/Silly-Machine/TuPy-Dataset)
 
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  ├── multilabel : multilabel dataset (including training and testing split)
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  └── README.md : documentation and card metadata
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  ```
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+ ## Security measures
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+ safeguard user identity and uphold the integrity of this dataset, all user mentions have been anonymized as "@user," and any references to external websites have been omitted
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  ## Annotation and voting process
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  To generate the binary matrices, we employed a straightforward voting process. Three distinct evaluations were assigned to each document. In cases where a document received two or more identical classifications, the adopted value is set to 1; otherwise, it is marked as 0.Raw data can be checked into the repository in the [project repository](https://github.com/Silly-Machine/TuPy-Dataset)