Open Detector is a BERT binary classification model for identifying whether academic papers are closer to human writing or AI writing style. It serves as an open alternative to expensive commercial detectors like Turnitin, suitable for students, individual researchers, and local deployment users.
🌐 Try Online: https://followsci.com/ai-detection
Commercial academic detection services like Turnitin are expensive and not friendly to students, researchers, and self-funded researchers. Paper detection should be transparent, fair, and explainable, not a commercial black box. Therefore, we open-source the model to provide the community with a transparent, low-cost solution.
AI's mission is to improve efficiency, not to return people to the era of handwriting.
AI writing far exceeds most human writers in vocabulary selection, syntactic structure, and logical coherence.
❌ "Writing like AI" ≠ Academic Misconduct.
Academic integrity should not be judged by language style, but should return to content authenticity.
Judging paper quality solely by style is absurd.
What we should really focus on is not "whether you used AI," but whether the content is authentic, reliable, and free of false generation.
In other words: We should detect AI hallucinations, not AI writing.
Trained on approximately 1.4 million data samples
- High Accuracy: Achieves 99.57% accuracy and 99.58% F1-score on academic text detection
- Low False Positive Rate: Only 0.82% false positive rate, minimizing incorrect accusations
- Exceptional Recall: 99.94% recall ensures AI-generated content is rarely missed
- Specialized for Academic Text: Optimized specifically for academic writing patterns
- BERT-based Architecture: Built on BERT-base-uncased for robust semantic understanding
Model files are available at https://huggingface.co/followsci/bert-ai-text-detector
pip install transformers torchfrom transformers import BertTokenizer, BertForSequenceClassification
import torch
model_name = "followsci/bert-ai-text-detector"
tokenizer = BertTokenizer.from_pretrained(model_name)
model = BertForSequenceClassification.from_pretrained(model_name)
model.eval()
text = "Your academic paragraph here..."
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
with torch.no_grad():
outputs = model(**inputs)
probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
ai_prob = probs[0][1].item() * 100
human_prob = probs[0][0].item() * 100
print(f"AI-generated: {ai_prob:.1f}% | Human: {human_prob:.1f}%")- Labels:
0 = Human,1 = AI - Max Length: 512 tokens
| Future Direction | Description |
|---|---|
| Fact Consistency Verification | Citation chain checking, literature search comparison |
| AI Hallucination Detection | Focus on distinguishing real vs. fabricated content |
| Citation Authenticity | Prevent "fake citations" and "model-generated references" |
| Academic Logic Consistency | Structure and reasoning verification |
| Multi-language Extension | Support Chinese, Japanese, and other languages |
Build a framework for "AI-assisted authentic academia," not an "anti-AI writing" tool.
We have also trained an academic paper humanization rewriting model:
- Maintains academic expression style
- Eliminates AI writing traces
- Avoids misjudgment by style detection
📌 This model can be used for free at https://followsci.com/ai-rewrite.
AI should not be judged, but should become a tool to support knowledge creation.
Our goal is not to punish AI, but to protect academic authenticity.
Thank you for reading. Welcome to Star ⭐ to support the open academic tools ecosystem.