Text Classification
Transformers
Safetensors
English
roberta
ai-text-detection
voight-kampff
pan-2025
text-embeddings-inference
Instructions to use protagonist/roberta-eloquent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use protagonist/roberta-eloquent with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="protagonist/roberta-eloquent")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("protagonist/roberta-eloquent") model = AutoModelForSequenceClassification.from_pretrained("protagonist/roberta-eloquent") - Notebooks
- Google Colab
- Kaggle
File size: 800 Bytes
c1667ab | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | # eloquent26 generations
5 generators × 5 strategies × 66 topics = 1,650+ generations from the
ELOQUENT 2026 Voight-Kampff factor-isolation experiment.
## Files
- `generations.tar.gz` — full `out/` tree:
- `{generator}/{strategy}/{topic_id}.txt` — the texts
- `_references/{set}/*.txt` — human controls
- `scores/{detector}/{generator}/{strategy}/{topic_id}.json` — per-text scores
- `manifest.jsonl`, `analysis/*.csv`
- `scores.parquet` — 9.6k-row aggregated detector scores (for quick inspection)
## Strategies
`vanilla`, `imperfection`, `roundtrip`, `roundtrip_imperf`, `lost_in_translation`.
Round-trip language: Hindi (closed frontier), Chinese (Qwen family).
## License
For research use. Contact the author for any commercial use.
Repo: `protagonist/roberta-eloquent`.
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