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  The dataset consist of pairs of text passages, where a typical passage is about a sentence long, however, a passage may also be longer or shorter than a sentence. Thus, each example includes two text passages (string), a manually annotated label to indicate the paraphrase type (string), and additional metadata. The dataset includes three different configurations: `plain`, `classification`, and `generation`. The `plain` configuration loads the original data without any additional preprocessing or transformations, while the `classification` configuration directly builds the data in a form suitable for training a paraphrase classifier, where each example is doubled in the data with different directions (text1, text2, label) --> (text2, text1, label) taking care of the label flipping as well if needed (paraphrases with directionality flag < or >). In the `generation` configuration, the examples are preprocessed to be directly suitable for the paraphrase generation task. In here, paraphrases not suitable for generation are discarded (negative, and highly context-dependent paraphrases), and directional paraphrases are provided so that the generation goes from more detailed passage to the more general one in order to prevent model hallucination (i.e. model learning to introduce new information). The rest of the paraphrases are provided in both directions (text1, text2, label) --> (text2, text1, label).
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  Each pair in the `plain` and `classification` configurations will include fields:
 
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  `id`: Identifier of the paraphrase pair (string)
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  `gem_id`: Identifier of the paraphrase pair in the GEM dataset (string)
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  `goeswith`: Identifier of the document from which the paraphrase was extracted, can be `not available` in case the source of the paraphrase is not from document-structured data. All examples with the same `goeswith` value (other than `not available`) should be kept together in any train/dev/test split; most users won't need this (string)
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  The dataset consist of pairs of text passages, where a typical passage is about a sentence long, however, a passage may also be longer or shorter than a sentence. Thus, each example includes two text passages (string), a manually annotated label to indicate the paraphrase type (string), and additional metadata. The dataset includes three different configurations: `plain`, `classification`, and `generation`. The `plain` configuration loads the original data without any additional preprocessing or transformations, while the `classification` configuration directly builds the data in a form suitable for training a paraphrase classifier, where each example is doubled in the data with different directions (text1, text2, label) --> (text2, text1, label) taking care of the label flipping as well if needed (paraphrases with directionality flag < or >). In the `generation` configuration, the examples are preprocessed to be directly suitable for the paraphrase generation task. In here, paraphrases not suitable for generation are discarded (negative, and highly context-dependent paraphrases), and directional paraphrases are provided so that the generation goes from more detailed passage to the more general one in order to prevent model hallucination (i.e. model learning to introduce new information). The rest of the paraphrases are provided in both directions (text1, text2, label) --> (text2, text1, label).
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  Each pair in the `plain` and `classification` configurations will include fields:
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  `id`: Identifier of the paraphrase pair (string)
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  `gem_id`: Identifier of the paraphrase pair in the GEM dataset (string)
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  `goeswith`: Identifier of the document from which the paraphrase was extracted, can be `not available` in case the source of the paraphrase is not from document-structured data. All examples with the same `goeswith` value (other than `not available`) should be kept together in any train/dev/test split; most users won't need this (string)