Model Card for ai-text-detector-model

Model Details

Model Description

This model is a fine-tuned DistilBERT sequence classification model to detect whether a given text is AI-generated (e.g., by ChatGPT, GPT-2/3) or Human-written.
It was trained on a combination of AI-generated texts and human-authored content.

• Developed by: Ahmed Iqbal
• Funded by [optional]: Self-funded
• Shared by: Ahmed Iqbal
• Model type: Transformer-based binary classifier (DistilBERT)
• Language(s) (NLP): English
• License: MIT (you may change this to Apache 2.0 if preferred)
• Finetuned from model: distilbert-base-uncased

Model Sources

• Repository: https://huggingface.co/ahmediqbal/ai-text-detector-model
• Demo [optional]: Can be easily built using Hugging Face Inference API or Gradio

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Uses

Direct Use

• Detect whether a text is AI-generated or Human-written.
• Useful in applications like plagiarism detection, content moderation, or authenticity checking.

Downstream Use

• Can be integrated into web apps for AI content detection.
• Can be further fine-tuned with domain-specific data (e.g., academic writing, creative writing).

Out-of-Scope Use

• Should not be used for high-stakes scenarios (e.g., exams, hiring, legal decisions).
• May not generalize well to languages other than English.
• Not reliable for adversarially modified text (e.g., humanized AI text).

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Bias, Risks, and Limitations

• Bias: Model may misclassify some human-written texts that resemble AI style.
• Risks: Over-reliance on automated detection may lead to false accusations.
• Limitations: Works best on English text only. Accuracy may decrease for very long or domain-specific texts.

Recommendations

• Always use this model as supportive evidence, not as a sole decision-maker.
• Combine with human review in critical cases.

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How to Get Started with the Model

from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline

model_id = "ahmediqbal/ai-text-detector-model"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)

classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)

text = "This is a sample sentence."
print(classifier(text))