Omkar2118/datasets huggingface dataset:
Autoencoder Paraphrase Dataset (AEPD)

This is a benchmark for neural paraphrase detection, to differentiate between original and machine-generated content.

####Training:
1,474,230 aligned paragraphs (98,282 original, 1,375,948 paraphrased with 3 models and 5 hyperparameter configurations each 98,282) extracted from 4,012 (English) Wikipedia articles.

####Testing:
```
BERT-large (cased): 
    arXiv             - Original - 20,966;     Paraphrased - 20,966; 
    Theses          - Original - 5,226;      Paraphrased - 5,226;
    Wikipedia      - Original - 39,241;     Paraphrased - 39,241;
    
RoBERTa-large (cased): 
    arXiv             - Original - 20,966;     Paraphrased - 20,966; 
    Theses          - Original - 5,226;      Paraphrased - 5,226;
    Wikipedia      - Original - 39,241;     Paraphrased - 39,241;

Longformer-large (uncased): 
    arXiv             - Original - 20,966;     Paraphrased - 20,966; 
    Theses          - Original - 5,226;      Paraphrased - 5,226;
    Wikipedia      - Original - 39,241;     Paraphrased - 39,241;
```

Introducing paper: Are Neural Language Models Good Plagiarists? A Benchmark for Neural Paraphrase Detection