gte_compressed
Compact multilingual sentence encoder compressed from alibaba-NLP/gte-multilingual-base (26x compression).
Model Details
| Property | Value |
|---|---|
| Base model | alibaba-NLP/gte-multilingual-base |
| Architecture | new (encoder) |
| Hidden dim | 384 (from 768) |
| Layers | 4 (from 12) |
| Intermediate | 1536 |
| Attention heads | 6 |
| Vocab size | 8,675 (from 250,048) |
| Parameters | ~10.6M |
| Model size (FP32) | 48.8MB |
| Compression | 26x |
| Distilled | No |
Quick Start
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("gte_compressed", trust_remote_code=True)
sentences = [
"Hello, how are you?",
"안녕하세요, 잘 지내세요?",
"こんにちは、元気ですか？",
"你好，你好吗？",
]
embeddings = model.encode(sentences)
print(embeddings.shape) # (4, 384)
MTEB Evaluation Results
Overall Average: 29.76%
| Task Group | Average |
|---|---|
| Classification | 34.67% |
| Clustering | 27.32% |
| STS | 27.56% |
Classification
| Task | Average | Details |
|---|---|---|
| AmazonCounterfactualClassification | 56.65% | en: 60.42%, en-ext: 59.21%, ja: 53.79%, de: 53.19% |
| Banking77Classification | 23.46% | default: 23.46% |
| ImdbClassification | 53.23% | default: 53.23% |
| MTOPDomainClassification | 29.15% | es: 33.54%, th: 29.9%, en: 29.72%, hi: 29.19%, de: 28.41% |
| MassiveIntentClassification | 12.03% | zh-CN: 22.47%, en: 20.25%, id: 18.92%, vi: 18.23%, tr: 17.5% |
| MassiveScenarioClassification | 15.96% | zh-CN: 26.89%, en: 22.5%, id: 21.64%, ms: 21.62%, es: 21.45% |
| ToxicConversationsClassification | 49.59% | default: 49.59% |
| TweetSentimentExtractionClassification | 37.27% | default: 37.27% |
Clustering
| Task | Average | Details |
|---|---|---|
| ArXivHierarchicalClusteringP2P | 46.6% | default: 46.6% |
| ArXivHierarchicalClusteringS2S | 46.16% | default: 46.16% |
| BiorxivClusteringP2P.v2 | 9.23% | default: 9.23% |
| MedrxivClusteringP2P.v2 | 19.99% | default: 19.99% |
| MedrxivClusteringS2S.v2 | 18.6% | default: 18.6% |
| StackExchangeClustering.v2 | 38.67% | default: 38.67% |
| StackExchangeClusteringP2P.v2 | 31.97% | default: 31.97% |
| TwentyNewsgroupsClustering.v2 | 7.32% | default: 7.32% |
STS
| Task | Average | Details |
|---|---|---|
| BIOSSES | 14.57% | default: 14.57% |
| SICK-R | 39.12% | default: 39.12% |
| STS12 | 33.18% | default: 33.18% |
| STS13 | 33.48% | default: 33.48% |
| STS14 | 30.91% | default: 30.91% |
| STS15 | 36.95% | default: 36.95% |
| STS17 | 13.25% | en-en: 47.75%, es-es: 45.85%, ar-ar: 28.57%, ko-ko: 25.03%, en-ar: 12.16% |
| STS22.v2 | 10.85% | zh: 41.25%, it: 35.25%, ar: 32.81%, es: 32.76%, tr: 18.57% |
| STSBenchmark | 35.75% | default: 35.75% |
Training
Created via multi-method model compression (no additional training):
- Teacher:
alibaba-NLP/gte-multilingual-base(12L, 768d, 277M params) - Layer pruning: 12 → 4 layers (uniform selection)
- Hidden dim: 768 → 384
- Vocab pruning: 250,048 → 8,675 (90% cumulative frequency)
- Compression ratio: 26x
Supported Languages (18)
ko, en, ja, zh, es, fr, de, pt, it, ru, ar, hi, th, vi, id, tr, nl, pl