This model is a binary text classifier trained to identify AI-generated content from models such as GPT-4, GPT-3.5, Claude, and LLaMA. It improves over legacy GPT-2 detectors by adapting to modern generative patterns.

Developed by: Daksh Thakuria
Model type: Transformer-based sequence classification (RoBERTa-Large)
Language(s): English
License: Apache 2.0
Finetuned from model: Community RoBERTa GPT-2 Detector

Model Sources

Repository: https://huggingface.co/silentone0725/roberta-large-openai-detector-custom
Training Code: https://github.com/silentone12725/Ai-Gen-Text-Detect

Uses

Direct Use

Detecting AI-generated text in research, moderation, and academic integrity systems.

Downstream Use

Integration into content filtering pipelines, analytics tools, or research benchmarks.

Out-of-Scope Use

Legal/forensic authorship claims
Fully automated high-stakes decisions
Guaranteed detection after heavy paraphrasing

Bias, Risks, and Limitations

May misclassify creative or structured human writing
Performance drops under heavy paraphrasing
English-focused
Surface-text detector (no watermarking)

Recommendations

Use as a decision-support tool, not a final authority.

How to Get Started

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "silentone0725/roberta-large-openai-detector-custom"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

text = "Sample text to evaluate"
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
outputs = model(**inputs)
prediction = torch.argmax(outputs.logits, dim=1)
print("AI-generated" if prediction.item() == 1 else "Human-written")

Training Details

Training Data

Dataset: https://huggingface.co/datasets/silentone0725/ai-human-text-detection-v1
Contains human text + GPT-4, GPT-3.5, Claude, LLaMA outputs.

Training Procedure

Fine-tuned on Google Colab GPUs using PyTorch + HuggingFace Transformers.

Training Hyperparameters

Learning rate: 2e-5
Batch size: 8 (effective 16)
Epochs: 6
Mixed precision: FP16
Weight decay: 0.2
Dropout: 0.3

Evaluation

Metrics

Metric	Score
Accuracy	0.5904
Precision	0.5087
Recall	0.7524
F1 Score	0.6070
AUC	0.690

Environmental Impact

Hardware Type: NVIDIA T4 / A100
Cloud Provider: Google Colab
Compute Region: Global (Colab infrastructure)

Technical Specifications

Architecture

RoBERTa-Large transformer with classification head.

Software

PyTorch, Transformers, scikit-learn.

Citation

APA: Thakuria, D. (2026). AI-Generated Text Detection via Fine-Tuned RoBERTa-Large.

Model Card Authors

Daksh Thakuria

Model Card Contact

Via Hugging Face profile.