sup-SimCSE-VietNamese-phobert-base
1. Introduction 2. Pretrain model 3. Using SimeCSEVietnamese with sentences-transformers - Installation - Example usage 4. Using SimeCSEVietnamese with transformers - Installation - Example usage
Params
140 M
Context
256
Downloads 30d
351 K
Likes
30
Download history
daily snapshots · 53 days
▲ 68 K in the last 30 days (16.2%)
419 K351 K
Sep 25Oct 5Oct 15Oct 24
419 K351 K
Sep 2Sep 19Oct 7Oct 24
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
- RobertaForCL
- Parameters
- 140 M
- Tensor type
- F32
- Context length
- 256
- Vocabulary
- 64,001
- Layers / heads
- 12 / 12
- First seen on the Hub
- 2022-03-02
- Training datasets
- undisclosed
- Added to our catalog
- 2026-09-02
Compare with any sentence-similarity model