---
language:
- en
pipeline_tag: text-classification
tags:
- ai-detection
- roberta
- generated-text-detection
license: mit
datasets:
- artem9k/ai-text-detection-pile
base_model:
- FacebookAI/roberta-base
widget:
  - text: "Machine learning is a subset of artificial intelligence."
    example_title: "AI Example"
  - text: "I went to the park to walk my dog yesterday."
    example_title: "Human Example"
---

# My AI Text Detector (RoBERTa Base)

This model is a fine-tuned version of `roberta-base` trained to distinguish between human-written text and AI-generated text.

## Model Details
- **Developer:** ShivamVN
- **Base Model:** RoBERTa-base
- **License:** MIT
- **Finetuned on:** 80,000 samples from the `artem9k/ai-text-detection-pile` dataset.

## How to Use

You can use this model directly with the Hugging Face `transformers` library:

```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch

model_name = "ShivamVN/My-Ai-Text-Detector"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

text = "Artificial Intelligence is changing the world."
inputs = tokenizer(text, return_tensors="pt")

with torch.no_grad():
    logits = model(**inputs).logits

probabilities = torch.softmax(logits, dim=1)
print(f"AI Probability: {probabilities[0][1].item():.2f}")