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--- |
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language: |
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- en |
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base_model: |
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- answerdotai/ModernBERT-base |
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pipeline_tag: text-classification |
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tags: |
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- text |
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- text classification |
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- LLM |
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- LLM text detection |
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- Detection |
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- detector |
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--- |
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# LLM_Detector_Preview_model |
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**Preview release of an LLM-generated text detector.** |
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## Model Description |
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This model is designed to classify text as Human, Mixed, or AI-generated. It is based on a sequence classification architecture and was trained on a mix of human and AI-generated texts. The model can be used for document, sentence, and token-level analysis. |
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- **Architecture:** ModernBERT (or compatible Transformer) |
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- **Labels:** |
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- 0: Human |
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- 1: Mixed |
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- 2: AI |
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## Intended Use |
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- **For research and curiosity only.** |
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- Not for academic, legal, medical, or high-stakes use. |
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- Results are easy to bypass and may be unreliable. |
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## Limitations & Warnings |
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- This model is **experimental** and not clinically accurate. |
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- It can produce false positives and false negatives. |
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- Simple paraphrasing or editing can fool the detector. |
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- Do not use for academic integrity, hiring, or legal decisions. |
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## How It Works |
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The model analyzes text and predicts the likelihood of it being human-written, mixed, or AI-generated. It uses statistical patterns learned from training data, but these patterns are not foolproof and can be circumvented. |
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## Example Usage |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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import torch |
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tokenizer = AutoTokenizer.from_pretrained('Donnyed/LLM_Detector_Preview_model') |
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model = AutoModelForSequenceClassification.from_pretrained('Donnyed/LLM_Detector_Preview_model') |
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text = "Paste your text here." |
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inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512) |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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probs = torch.softmax(outputs.logits, dim=1) |
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pred = torch.argmax(probs, dim=1).item() |
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print('Prediction:', pred) |
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print('Probabilities:', probs) |
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``` |
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## Files Included |
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- `model.safetensors` — Model weights |
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- `config.json` — Model configuration |
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- `tokenizer.json`, `tokenizer_config.json`, `special_tokens_map.json` — Tokenizer files |
