Model Card for IDMGSP-Galactica-TRAIN

A fine-tuned Galactica model to detect machine-generated scientific papers based on their abstract, introduction, and conclusion.

This model is trained on the train dataset found in https://huggingface.co/datasets/tum-nlp/IDMGSP.

this model card is WIP, please check the repository, the dataset card and the paper for more details.

Model Details

Model Description

• Developed by: Technical University of Munich (TUM)
• Model type: [More Information Needed]
• Language(s) (NLP): English
• License: [More Information Needed]
• Finetuned from model [optional]: Galactica

Model Sources

• Repository: https://github.com/qwenzo/-IDMGSP
• Paper: [More Information Needed]

Uses

Direct Use

from transformers import AutoTokenizer, OPTForSequenceClassification, pipeline

model = OPTForSequenceClassification.from_pretrained("tum-nlp/IDMGSP-Galactica-TRAIN")
tokenizer = AutoTokenizer.from_pretrained("tum-nlp/IDMGSP-Galactica-TRAIN")
reader = pipeline("text-classification", model=model, tokenizer = tokenizer)
reader(
'''
Abstract:
....

Introduction:
....

Conclusion:
...'''
)

Downstream Use [optional]

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Training Details

Training Data

The training dataset comprises scientific papers generated by the Galactica, GPT-2, SCIgen, and ChatGPT models, as well as papers extracted from the arXiv database.

The provided table displays the sample counts from each source utilized in constructing the training dataset. The dataset could be found in https://huggingface.co/datasets/tum-nlp/IDMGSP.

Dataset	arXiv (real)	ChatGPT (fake)	GPT-2 (fake)	SCIgen (fake)	Galactica (fake)	GPT-3 (fake)
Standard train (TRAIN)	8k	2k	2k	2k	2k	-

Training Procedure

Preprocessing [optional]

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Training Hyperparameters

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Speeds, Sizes, Times [optional]

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

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• Hours used: [More Information Needed]
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• Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

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Software

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Citation [optional]

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APA:

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