ehsantorabi/aitextd-models

AITextD — Persian AI-Generated Text Detection Models

Trained checkpoints for the AITextDetection project: detecting whether Persian text was written by a human or generated by AI.

This repository mirrors the models/ directory of the source project one-to-one, so a single download drops every serving artifact into place:

# from the project root, after cloning the GitHub repo
hf download ehsantorabi/aitextd-models --local-dir models/

What is inside

Sixteen registered models cover four families — classical ML on TF-IDF (+ stylometric features), deep networks trained from scratch, fine-tuned LLMs, and a System One decision model — plus the shared serving artifacts (tfidf_vectorizer.joblib, the GPT-2 and ParsBERT tokenizers, the raw Persian GPT-2 LM used for the training-free perplexity signal, and the fine-tuned Laya decision model). The weighted ensemble is computed live by the serving stack from these checkpoints; it is not a file.

Model	id	Artifact	Architecture	Family	Ensemble weight	Test accuracy	Test F1
BiLSTM	bilstm	lstmModel.pt	BiLSTM	Deep learning	0.7	0.9832	0.9831
TextCNN	textcnn	textcnnModel.pt	TextCNN	Deep learning	0.5	0.9905	0.9905
TextCNN (RLCD fine-tune)	textcnn_rlcd	textcnnRlcdModel.pt	TextCNN + RLCD fine-tune	Deep learning	0.0	0.9900	0.9900
Attention BiLSTM	attention_bilstm	attnBilstmModel.pt	BiLSTM + additive attention pooling	Deep learning	0.5	0.9881	0.9881
GPT-2 Transformer (fine-tuned)	gpt2_transformer	gpt2ClassifierModel.pt	GPT2ForSequenceClassification (full fine-tune)	Deep learning	3.0	0.9969	0.9969
ParsBERT (Bidirectional Encoder)	parsbert	parsbertClassifierModel.pt	BertModel + linear head on [CLS]	Deep learning	3.0	0.9983	0.9983
System One (Decision Head)	system_one	systemOneModel.pt	ParsBERT (fine-tuned encoder) + typed decision heads (choice/score/noul)	Deep learning	0.6	0.9893	0.9893
SVM	svm	svModel.joblib	LinearSVC	Classical ML	1.0	0.9967	0.9967
Logistic Regression	logreg	logregModel.joblib	LogisticRegression	Classical ML	1.0	0.9964	0.9964
Naive Bayes	naive_bayes	nbModel.joblib	MultinomialNB	Classical ML	1.0	0.9810	0.9809
Random Forest	random_forest	rfModel.joblib	RandomForestClassifier	Classical ML	1.0	0.9886	0.9886
Gradient Boosting	gradient_boosting	gbModel.joblib	GradientBoostingClassifier	Classical ML	1.0	0.9912	0.9912
Laya (System One, fine-tuned)	laya	laya_persian (directory)	Laya multilingual (mmBERT-base) + typed decision heads (RLCD fine-tune)	Deep learning	2.0	0.9993	0.9993
MLP	mlp	mlpModel.pt	MLP (2 hidden layers)	Deep learning	0.5	0.9964	0.9965
CharCNN	charcnn	charcnnModel.pt	CharCNN (character-level TextCNN)	Deep learning	0.5	0.9983	0.9983
Tiny Transformer (from scratch)	tinytransformer	tinyTransformerModel.pt	Transformer encoder (2 layers, from scratch)	Deep learning	0.5	0.9891	0.9891

Artifacts not included

• models/laya_multilingual/ — the untouched convaiinnovations/laya multilingual (mmBERT-base) base checkpoint; only needed to re-run the fine-tuning notebook, so it is not duplicated here. Serving uses the fine-tuned laya_persian/ above.
• models/laya_persian/checkpoint_latest/ — a redundant mid-training checkpoint of laya_persian (identical final weights); skipped.

Evaluation protocol

Metrics above are test-set results from the project's leak-free v2 protocol (group-aware splits, paraphrase-attack and domain-shift conditions). Frozen evidence, per-model confusion matrices and full tables live in the GitHub repo under reports/ and models.json.

Base-model attribution

• ParsBERT classifier → HooshvareLab/bert-fa-base-uncased
• GPT-2 classifier + perplexity LM → HooshvareLab/gpt2-fa
• Laya System One decision model → convaiinnovations/laya (multilingual, Apache-2.0)

Usage

The checkpoints are consumed by the project's FastAPI serving stack (aitextd/, models.json), not by a single pipeline(...). See the GitHub repo for the serving API, the six training notebooks, the web front end and tests:

uv sync
hf download ehsantorabi/aitextd-models --local-dir models/
uv run python server.py          # API on :8000