Deberta Ai Detector v3

# DeBERTa-v3-Large AI vs Human Text Detector This model is fine-tuned to classify whether a given piece of text was written by a human or generated by Artificial Intelligence (AI). It is based on the powerful `microsoft/deberta-v3-large` architecture, making it highly accurate in detecting synthetic text across various domains. ## Model Details ### Model Description This model was trained to solve the growing need for academic integrity and content authenticity in the era of generative AI. By leveraging the advanced contextual understanding of DeBERTa-v3-large, the model distinguishes between human-written nuances and AI-generated patterns. - **Developed by:** [Md Rakib Ali](https://github.com/mdrakibali) & [Tushar Imran](https://github.com/tushar-454) - **Model type:** Text Classification (Sequence Classification) - **Language(s) (NLP):** English - **License:** MIT - **Finetuned from model:** `microsoft/deberta-v3-large` ### Model Sources - **Base Model:** [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) - **Dataset:** [acmc/multi_domain_ai_human_text](https://huggingface.co/datasets/acmc/multi_domain_ai_human_text) ## Uses ### Direct Use The model can be used directly for identifying AI-generated text. It is highly suitable for: - Academic institutions (detecting AI-generated essays or assignments). - Publishers and editors (verifying the authenticity of submissions). - General users wanting to verify if a text is human-written or machine-generated. ### Out-of-Scope Use - The model should not be used as the *sole* evidence to penalize students or professionals without human review. - It may not perform optimally on heavily edited AI text or languages other than English. ## How to Get Started with the Model Use the code below to get started with the model using the `transformers` library: ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch model_name = "YOUR_USERNAME/YOUR_MODEL_NAME" # Replace with your actual repo name tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSequenceClassification.from_pretrained(model_name) text = "In the era of artificial intelligence, take-home writing assignments have become difficult to police." inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=256) with torch.no_grad(): logits = model(**inputs).logits probs = torch.nn.functional.softmax(logits, dim=-1) predicted_class = torch.argmax(probs).item() # Assuming 0: Human, 1: AI (Adjust based on your label mapping) labels = ["Human", "AI"] print(f"Prediction: {labels[predicted_class]}") print(f"Probabilities: {probs.tolist()}") ``` ## Training Details ### Training Data The model was fine-tuned using the `acmc/multi_domain_ai_human_text` dataset. This dataset contains a diverse mix of human-written and AI-generated texts from various domains, ensuring the model generalizes well across different writing styles. ## Training Results (Summary) The model achieved highly robust results across 3 epochs, showing strong generalization capabilities with an evaluation accuracy of 95.56%. | Epoch | Training Loss | Eval Accuracy | F1 Score | Precision | Recall | |-------|---------------|----------------|----------|-----------|--------| | 1 | ~1.0 | 92.12% | 0.916 | 0.979 | 0.861 | | 2 | 0.273 | 95.46% | 0.955 | 0.943 | 0.968 | | 3 | — | 95.56% | 0.956 | — | — | --- library_name: transformers tags: - text-classification - ai-detection - deberta-v3 - pytorch datasets: - acmc/multi_domain_ai_human_text --- ## Technical Specifications ### Model Architecture and Objective - **Architecture:** DeBERTa-v3-Large - **Objective:** Binary sequence classification (Human vs. AI). ### Compute Infrastructure The model was fine-tuned using a high-performance cloud GPU environment. #### Hardware - **Cloud Provider:** RunPod - **GPU:** 1x NVIDIA A40 - **VRAM:** 48 GB GDDR6 - **System RAM:** 64 GB #### Software - **Frameworks:** PyTorch, Transformers (Hugging Face) - **Environment:** CUDA-enabled environment ## Bias, Risks, and Limitations - **False Positives:** Like all AI detectors, this model may occasionally flag human-written text as AI. - **Recommendations:** Always use the model's prediction as a signal rather than an absolute verdict. Human judgment should always be part of the final decision-making process.