distilbert-base-uncased-finetuned-sst-2-english
- Model Details - How to Get Started With the Model - Uses - Risks, Limitations and Biases - Training
Params
70 M
Context
512
Downloads 30d
2.9 M
Likes
1,006
Download history
daily snapshots · 106 days
▲ 193 K in the last 30 days (7.1%)
2.9 M2.6 M
Oct 12Oct 22Nov 1Nov 10
3.9 M2.6 M
Aug 13Sep 12Oct 12Nov 10
3.9 M2.6 M
Jul 28Sep 1Oct 6Nov 10
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 | 0.3 GB | 0.8 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
- DistilBertForSequenceClassification
- Parameters
- 70 M
- Tensor type
- F32
- Context length
- 512
- Vocabulary
- 30,522
- Licence
- apache-2.0
- First seen on the Hub
- 2022-03-02
- Training datasets
- sst2, glue
- glue (reported)
- 0.39013850688934
- sst2 (reported)
- 0.040652573108673
- Added to our catalog
- 2026-07-28
Compare with any text-classification model