silentone0725/roberta-large-openai-detector-custom

Model Card for roberta-large-openai-detector-custom

This model detects AI-generated vs human-written text using a fine-tuned RoBERTa-Large architecture trained on modern LLM outputs.

Model Details

Model Description

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

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

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.