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deberta-v3-instruct-text-detector

Token Classification
Transformers
English
deberta-v2
Inference Endpoints
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This model finds the text boundary between human and machine generated texts in partiall machine generated texts (i.e text completion and instruct models). This model works on just english texts , with great results on seen and unseen domains and generators.

Model Details

finetuned on M4GT dataset from deberta-v3-base

Model Description

With increasing usage of generative models for text generation and widespread use of machine generated texts in various domains, being able to distinguish between human written and machine generated texts is a significant challenge. While existing models and proprietary systems focus on identifying whether given text is entirely human written or entirely machine generated, only a few systems provide insights at sentence or paragraph level at likelihood of being machine generated at a non reliable accuracy level, working well only for a set of topics and generators. This paper introduces a novel approach for identifying which part(s) of a given text are machine generated at a word level while comparing results from different approaches and methods. We present a comparison with proprietary systems , performance of our model on unseen domains’ and generators’ texts. The findings reveal significant improvements in detection accuracy along with comparison on other aspects of detection capabilities. Finally we discuss potential avenues for improvement and implications of our work.

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  • Language(s) (NLP): English (EN)
  • License: MIT
  • Finetuned from model [optional]: microsoft/deberta-v3-base

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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