roberta-base-openai-detector
- Model Details - Uses - Risks, Limitations and Biases - Training - Evaluation - Environmental Impact - Technical Specifications - Citation Information - Model Card Authors - How To Get Started With the Model
Params
125 M
Context
512
Downloads 30d
381 K
Likes
138
Download history
daily snapshots · 20 days384 K223 K
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Can you run it?
Estimated VRAM at 8K context unless noted. Pick your hardware to see the verdict per quantization.
| File | Quant | Size | Est. VRAM | Verdict on RTX 4090 · 24 GB |
|---|---|---|---|---|
| model.safetensors | f32 | 0.5 GB | 1.1 GB | ✅ Runs comfortably |
Estimate: file size × 1.1 + KV cache at 8K + 0.5 GB overhead. Not a benchmark — how we calculate this.
Specifications
- Architecture
- RobertaForSequenceClassification
- Parameters
- 125 M
- Tensor type
- F32
- Context length
- 512
- Vocabulary
- 50,265
- Layers / heads
- 12 / 12
- Licence
- mit
- First seen on the Hub
- 2022-03-02
- Training datasets
- bookcorpus, wikipedia
- Added to our catalog
- 2026-10-05
Compare with any text-classification model