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Julius1030/
fine-tuned-Qwen3-0.6B-Base-identify-LLM-Adoption

Name: fine-tuned-Qwen3-0.6B-Base-identify-LLM-Adoption
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language:

en license: apache-2.0 library_name: transformers pipeline_tag: text-classification tags:
llm-detection
ai-generated-text-detection
academic-writing
peer-review
qwen
lora
binary-classification base_model: Qwen/Qwen3-0.6B-Base model_type: qwen3

Qwen3-0.6B Academic LLM-Writing Detector

Model Summary

This model is a parameter-efficient LoRA fine-tuning of Qwen3-0.6B-Base for binary classification of academic text. It is designed to identify whether a text segment is more likely to be human-written or LLM-assisted / LLM-like.

The model was developed for research on AI-mediated scholarly communication, with downstream applications including the classification of:

article abstracts,
introduction sections,
peer-review reports.

The classifier outputs:

0: human-written / human-like
1: LLM-assisted / LLM-like

Intended Use

Primary intended use

This model is intended for:

research on the diffusion of LLM use in academic writing,
large-scale measurement of LLM-associated language patterns in scholarly corpora,
comparative analyses across article sections, journals, or peer-review texts.

Out-of-scope use

This model is not intended for:

plagiarism adjudication,
misconduct accusations against individual authors or reviewers,
legal, institutional, or disciplinary enforcement,
high-stakes decisions about individuals without additional human review.

Predictions should be interpreted as probabilistic indicators of LLM-associated writing style, not as definitive proof of AI use.

Training Data

The model was fine-tuned on the PAN25 Generative AI Detection Task 1 training data, using a binary text-classification objective.

The task setting is:

detect whether a text is human-written or generated / AI-assisted,
optimized as a supervised binary classification problem.

Training Procedure

Base model

Qwen3-0.6B-Base

Fine-tuning method

LoRA fine-tuning for sequence classification

Task

Binary sequence classification

Label definition

0: human-written / human-like
1: LLM-assisted / LLM-like

Best checkpoint

checkpoint-1000
or, if you prefer the merged release wording:
released checkpoint: final_best

Evaluation

On the validation split used during fine-tuning, the best checkpoint achieved:

Accuracy: 0.9980
Precision: 1.0000
Recall: 0.9970
F1: 0.9985

More specifically:

json

{
  "eval_loss": 0.007584796752780676,
  "eval_accuracy": 0.9980495959877403,
  "eval_precision": 1.0,
  "eval_recall": 0.9969723183391004,
  "eval_f1": 0.99848386398094,
  "eval_tp": 2305.0,
  "eval_tn": 1277.0,
  "eval_fp": 0.0,
  "eval_fn": 7.0
}

Julius1030/fine-tuned-Qwen3-0.6B-Base-identify-LLM-Adoption