Text Classification
Transformers
TensorBoard
Safetensors
deberta-v2
ai-text-detection
gradienttrace
text-embeddings-inference
Instructions to use Yashhh999/gradientTrace with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Yashhh999/gradientTrace with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Yashhh999/gradientTrace")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AIDetector tokenizer = AutoTokenizer.from_pretrained("Yashhh999/gradientTrace") model = AIDetector.from_pretrained("Yashhh999/gradientTrace", device_map="auto") - Notebooks
- Google Colab
- Kaggle
gradientTrace
Binary AI-text detector with a microsoft/deberta-v3-large backbone, trained on prompt-paired
human/model text with humanized-text augmentation and generator-disjoint splits.
Held-out evaluation
| slice | n | AUROC | TPR @ 1% FPR |
|---|---|---|---|
| val | 15216 | 1.0000 | 0.999 |
| test_indomain | 15181 | 0.9999 | 1.000 |
| test_ood_generators | 16000 | 1.0000 | 1.000 |
Full report: eval_report.json. Data manifest: data_manifest.json.
Intended use
Estimate P(machine-generated) for passages up to 512 tokens. Scores are evidence, not proof: false positives occur on atypical human writing, and paraphrased/humanized machine text is the known hard failure mode. Do not use as the sole basis for academic-integrity or moderation decisions.
Training
Runtime: Tesla T4; precision fp16; effective batch
64. Full configuration is in config.json and in the
gradientTrace repository.
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Model tree for Yashhh999/gradientTrace
Base model
microsoft/deberta-v3-large