Model Card for IDMGSP-Galactica-TRAIN+GPT3
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+gpt3 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
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- 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
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- Repository: https://github.com/qwenzo/-IDMGSP
- Paper: [More Information Needed]
Uses
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Direct Use
from transformers import AutoTokenizer, OPTForSequenceClassification, pipeline
model = OPTForSequenceClassification.from_pretrained("tum-nlp/IDMGSP-Galactica-TRAIN+GPT3")
tokenizer = AutoTokenizer.from_pretrained("tum-nlp/IDMGSP-Galactica-TRAIN+GPT3")
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, and SCIgen 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) |
|---|---|---|---|---|---|---|
| TRAIN plus GPT-3 (TRAIN+GPT3) | 8k | 2k | 2k | 2k | 2k | 1.2k |
Training Procedure
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Preprocessing [optional]
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Training Hyperparameters
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Speeds, Sizes, Times [optional]
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Evaluation
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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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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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Glossary [optional]
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