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--- |
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dataset_info: |
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features: |
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- name: input |
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dtype: string |
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- name: output |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 34347236 |
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num_examples: 223241 |
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download_size: 21377198 |
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dataset_size: 34347236 |
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--- |
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Dataset Card for Sentence Paraphase Collections |
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Dataset Description |
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Repository: |
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Paper: DeTiME: Diffusion-Enhanced Topic Modeling using Encoder-decoder based LLM https://arxiv.org/abs/2310.15296 |
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Leaderboard: |
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Point of Contact: Weijie Xu |
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Dataset Summary |
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Sentence_Paraphase is a combination of sentences paraphase tasks from various sources such as paraphase using ChatGPT, Paraphrase Adversaries from Word Scrambling (PAWS) and STS benchmark. We filtered out pairs that are detected as non english, too short or not have high similarity score. |
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Category Count |
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Paraphrase 223241 |
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Dataset Structure |
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Data Instances |
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An example of data as follows: |
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{'input': 'U.S. prosecutors have arrested more than 130 individuals and have seized more than $17 million in a continuing crackdown on Internet fraud and abuse.', |
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'output': 'More than 130 people have been arrested and $17 million worth of property seized in an Internet fraud sweep announced Friday by three U.S. government agencies.'} |
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Data Fields |
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The data fields are as follows: |
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input and output are paraphrase of a sentence or paragraph. |
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Dataset Creation |
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Curation Rationale |
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[More Information Needed] |
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Source Data |
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Initial Data Collection and Normalization |
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[More Information Needed] |
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Who are the source language producers? |
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[More Information Needed] |
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Annotations |
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Annotation process |
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[More Information Needed] |
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Who are the annotators? |
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[More Information Needed] |
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Personal and Sensitive Information |
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[More Information Needed] |
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Considerations for Using the Data |
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Social Impact of Dataset |
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[More Information Needed] |
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Discussion of Biases |
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[More Information Needed] |
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Other Known Limitations |
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[More Information Needed] |
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Additional Information |
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Dataset Curators |
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[More Information Needed] |
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Licensing Information |
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The dataset is available under the Creative Commons NonCommercial (CC BY-NC 4.0). |
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Citation Information |
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@misc{xu2023detime, |
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title={DeTiME: Diffusion-Enhanced Topic Modeling using Encoder-decoder based LLM}, |
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author={Weijie Xu and Wenxiang Hu and Fanyou Wu and Srinivasan Sengamedu}, |
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year={2023}, |
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eprint={2310.15296}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
