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lm_p1
r2911/lm_p1
lm_p1 is a text classification model from r2911. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
This model is fine-tuned on a labeled dataset for AI-generated vs. Human-written text detection.
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- deberta-v2
- Access
- Public
- Created
- Nov 27, 2025
- Updated
- Nov 27, 2025
- SHA
- 1b976b85
Try a prompt
Base models
Model card
From the Hugging Face model README
🧠 AI Text Detector – DeBERTa v3 Large (Fine-tuned on Human vs AI Text)
This model is fine-tuned on a labeled dataset for AI-generated vs. Human-written text detection.
⚙️ Model Details
- Base Model:
microsoft/deberta-v3-large - Fine-tuned by: @abhinav
- Epochs: 4
- Learning Rate: 2e-05
- Batch Size: 8
- GPU: 80 GB A100
- Optimizer: AdamW (Fused)
- Scheduler: Cosine
- Mixed Precision: FP16
- Gradient Checkpointing: Enabled
📊 Evaluation Results (Test Set)
| Metric | Score |
|---|---|
| Accuracy | 0.993 |
| Human (0) – Precision | 1.000 |
| Human (0) – Recall | 0.986 |
| AI (1) – Precision | 0.988 |
| AI (1) – Recall | 1.000 |
🧮 Confusion Matrix
🚀 Example Inference
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
tokenizer = AutoTokenizer.from_pretrained("abhi099k/ai-text-detector-deberta-v3-large-h1")
model = AutoModelForSequenceClassification.from_pretrained("abhi099k/ai-text-detector-deberta-v3-large-h1")
text = "This text was likely written by an AI model."
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
probs = torch.nn.functional.softmax(outputs.logits, dim=1)
print(probs)
- Task
- Text Classification
- Library
- transformers
- Type
- deberta-v2
- License
- mit
- Created
- Nov 27, 2025
- Updated
- Nov 27, 2025