Longformer-base plagiarism detection open-source model - Freely detect machine-paraphrased plagiarized texts and uphold academic integrity

Longformer Base Plagiarism Detection

Developed by jpwahle

This model is trained using the Longformer architecture, specifically designed to detect machine-paraphrased plagiarized text, with significant application value in maintaining academic integrity.

Text Classification

Transformers

English#Academic Plagiarism Detection #Long Text Analysis #Machine Paraphrasing Recognition

Downloads 59.47k

Release Time : 3/2/2022

Model Overview

A plagiarism detection system fine-tuned on the Longformer-base-4096 pre-trained model, capable of identifying academically rewritten text using tools like SpinBot, achieving an average F1 score of 80.99%.

Model Features

Long Document Processing Capability

Utilizes sliding window attention mechanism to effectively process academic documents up to 4096 tokens in length.

Multi-Paraphrasing Tool Recognition

Optimized detection effectiveness for mainstream paraphrasing tools like SpinBot and SpinnerChief.

Academic Scenario Optimization

Excellent performance on academic texts such as preprints and dissertations (F1 score up to 99.68%).

Model Capabilities

Machine Paraphrased Text Recognition

Academic Plagiarism Detection

Long Text Semantic Analysis

Use Cases

Academic Integrity Maintenance

Thesis Plagiarism Detection

Identifies plagiarized content disguised using paraphrasing tools in student papers.

Achieves an F1 score of 99.68% for SpinBot-paraphrased text detection.

Publication Review Assistance

Assists journal editors in detecting potential plagiarism in submitted papers.

Outperforms traditional text-matching systems like Turnitin.

Education Quality Assurance

Homework Originality Check

Automatically screens student assignments for machine-paraphrased content.

Human evaluation consistency reaches 78.4%.

🚀 Longformer-base for Machine-Paraphrase Detection

This Longformer-base model is trained for machine-paraphrase detection, helping to identify plagiarized text and maintain academic integrity.

🚀 Quick Start

If you are using this model in your research work, please cite:

@InProceedings{10.1007/978-3-030-96957-8_34,
    author="Wahle, Jan Philip and Ruas, Terry and Folt{\'y}nek, Tom{\'a}{\v{s}} and Meuschke, Norman and Gipp, Bela",
    title="Identifying Machine-Paraphrased Plagiarism",
    booktitle="Information for a Better World: Shaping the Global Future",
    year="2022",
    publisher="Springer International Publishing",
    address="Cham",
    pages="393--413",
    abstract="Employing paraphrasing tools to conceal plagiarized text is a severe threat to academic integrity. To enable the detection of machine-paraphrased text, we     evaluate the effectiveness of five pre-trained word embedding models combined with machine learning classifiers and state-of-the-art neural language models. We analyze preprints of research papers, graduation theses, and Wikipedia articles, which we paraphrased using different configurations of the tools SpinBot and SpinnerChief. The best performing technique, Longformer, achieved an average F1 score of 80.99{\%} (F1=99.68{\%} for SpinBot and F1=71.64{\%} for SpinnerChief cases), while human evaluators achieved F1=78.4{\%} for SpinBot and F1=65.6{\%} for SpinnerChief cases. We show that the automated classification alleviates shortcomings of widely-used text-matching systems, such as Turnitin and PlagScan.",
    isbn="978-3-030-96957-8"
}

This is the checkpoint for Longformer-base after being trained on the Machine-Paraphrased Plagiarism Dataset.

💻 Usage Examples

Basic Usage

from transformers import AutoModelForSequenceClassification, AutoTokenizer

AutoModelForSequenceClassification("jpelhaw/longformer-base-plagiarism-detection")
AutoTokenizer.from_pretrained("jpelhaw/longformer-base-plagiarism-detection")

input = "Plagiarism is the representation of another author's writing, \
thoughts, ideas, or expressions as one's own work."

example = tokenizer.tokenize(input, add_special_tokens=True)

answer = model(**example)
                                
# "plagiarised"

📚 Documentation

Additional information about this model:

Property	Details
Thumbnail	url to a thumbnail used in social sharing
Tags	array, of, tags
Datasets	jpwahle/machine-paraphrase-dataset
Widget Text	Plagiarism is the representation of another author's writing, thoughts, ideas, or expressions as one's own work.

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