Dataset Card for Sentence Paraphase Collections


Dataset Description
Repository: 
Paper:  DeTiME: Diffusion-Enhanced Topic Modeling using Encoder-decoder based LLM https://arxiv.org/abs/2310.15296


Leaderboard: 
Point of Contact: Weijie Xu


Dataset Summary
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.


Category	Count
Paraphrase 223241


Dataset Structure
Data Instances
An example of data as follows:
{'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.',
 '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.'}


Data Fields
The data fields are as follows:


input and output are paraphrase of a sentence or paragraph.


Dataset Creation
Curation Rationale
[More Information Needed]

Source Data
Initial Data Collection and Normalization
[More Information Needed]

Who are the source language producers?
[More Information Needed]

Annotations
Annotation process
[More Information Needed]

Who are the annotators?
[More Information Needed]

Personal and Sensitive Information
[More Information Needed]

Considerations for Using the Data
Social Impact of Dataset
[More Information Needed]

Discussion of Biases
[More Information Needed]

Other Known Limitations
[More Information Needed]

Additional Information
Dataset Curators
[More Information Needed]

Licensing Information
The dataset is available under the Creative Commons NonCommercial (CC BY-NC 4.0).

Citation Information
@misc{xu2023detime,
      title={DeTiME: Diffusion-Enhanced Topic Modeling using Encoder-decoder based LLM}, 
      author={Weijie Xu and Wenxiang Hu and Fanyou Wu and Srinivasan Sengamedu},
      year={2023},
      eprint={2310.15296},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
arxiv:2310.15296 region:us
{"dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 34347236, "num_examples": 223241}], "download_size": 21377198, "dataset_size": 34347236}}
TAGS
#arxiv-2310.15296 #region-us 

Dataset Card for Sentence Paraphase Collections


Dataset Description
Repository: 
Paper:  DeTiME: Diffusion-Enhanced Topic Modeling using Encoder-decoder based LLM URL


Leaderboard: 
Point of Contact: Weijie Xu


Dataset Summary
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.


Category	Count
Paraphrase 223241


Dataset Structure
Data Instances
An example of data as follows:
{'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.',
 '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.'}


Data Fields
The data fields are as follows:


input and output are paraphrase of a sentence or paragraph.


Dataset Creation
Curation Rationale


Source Data
Initial Data Collection and Normalization


Who are the source language producers?


Annotations
Annotation process


Who are the annotators?


Personal and Sensitive Information


Considerations for Using the Data
Social Impact of Dataset


Discussion of Biases


Other Known Limitations


Additional Information
Dataset Curators


Licensing Information
The dataset is available under the Creative Commons NonCommercial (CC BY-NC 4.0).

Citation Information
@misc{xu2023detime,
      title={DeTiME: Diffusion-Enhanced Topic Modeling using Encoder-decoder based LLM}, 
      author={Weijie Xu and Wenxiang Hu and Fanyou Wu and Srinivasan Sengamedu},
      year={2023},
      eprint={2310.15296},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
TAGS
#arxiv-2310.15296 #region-us 

passage: TAGS
#arxiv-2310.15296 #region-us 
