me5s_compressed
Compact multilingual sentence encoder compressed from intfloat/multilingual-e5-small (9x compression).
Model Details
| Property |
Value |
| Base model |
intfloat/multilingual-e5-small |
| Architecture |
bert (encoder) |
| Hidden dim |
384 (from 384) |
| Layers |
4 (from 12) |
| Intermediate |
1536 |
| Attention heads |
12 |
| Vocab size |
15,168 (from 250,037) |
| Parameters |
~13.1M |
| Model size (FP32) |
50.6MB |
| Compression |
9x |
| Distilled |
No |
Quick Start
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("me5s_compressed", trust_remote_code=True)
sentences = [
"Hello, how are you?",
"์๋
ํ์ธ์, ์ ์ง๋ด์ธ์?",
"ใใใซใกใฏใๅ
ๆฐใงใใ๏ผ",
"ไฝ ๅฅฝ๏ผไฝ ๅฅฝๅ๏ผ",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
MTEB Evaluation Results
Overall Average: 46.19%
| Task Group |
Average |
| Classification |
52.2% |
| Clustering |
30.4% |
| STS |
54.88% |
Classification
| Task |
Average |
Details |
| AmazonCounterfactualClassification |
67.37% |
en: 69.31%, en-ext: 67.39%, ja: 67.39%, de: 65.39% |
| Banking77Classification |
58.7% |
default: 58.7% |
| ImdbClassification |
57.14% |
default: 57.14% |
| MTOPDomainClassification |
66.84% |
en: 75.99%, es: 69.48%, hi: 68.15%, fr: 63.63%, th: 63.38% |
| MassiveIntentClassification |
31.12% |
en: 53.03%, zh-CN: 51.62%, it: 47.56%, pt: 47.28%, ja: 47.03% |
| MassiveScenarioClassification |
34.85% |
zh-CN: 59.05%, en: 58.06%, ja: 51.79%, it: 50.05%, vi: 49.68% |
| ToxicConversationsClassification |
55.82% |
default: 55.82% |
| TweetSentimentExtractionClassification |
45.74% |
default: 45.74% |
Clustering
| Task |
Average |
Details |
| ArXivHierarchicalClusteringP2P |
47.08% |
default: 47.08% |
| ArXivHierarchicalClusteringS2S |
48.29% |
default: 48.29% |
| BiorxivClusteringP2P.v2 |
17.24% |
default: 17.24% |
| MedrxivClusteringP2P.v2 |
24.42% |
default: 24.42% |
| MedrxivClusteringS2S.v2 |
21.55% |
default: 21.55% |
| StackExchangeClustering.v2 |
39.42% |
default: 39.42% |
| StackExchangeClusteringP2P.v2 |
31.85% |
default: 31.85% |
| TwentyNewsgroupsClustering.v2 |
13.35% |
default: 13.35% |
STS
| Task |
Average |
Details |
| BIOSSES |
56.68% |
default: 56.68% |
| SICK-R |
59.22% |
default: 59.22% |
| STS12 |
52.11% |
default: 52.11% |
| STS13 |
64.25% |
default: 64.25% |
| STS14 |
60.12% |
default: 60.12% |
| STS15 |
74.19% |
default: 74.19% |
| STS17 |
38.71% |
es-es: 74.88%, en-en: 74.6%, ar-ar: 63.98%, ko-ko: 58.54%, nl-en: 28.91% |
| STS22.v2 |
27.7% |
zh: 59.69%, fr: 57.76%, es: 56.19%, en: 50.93%, it: 50.09% |
| STSBenchmark |
60.91% |
default: 60.91% |
Training
Created via multi-method model compression (no additional training):
- Teacher:
intfloat/multilingual-e5-small (12L, 384d, 117M params)
- Layer pruning: 12 โ 4 layers (uniform selection)
- Hidden dim: 384 โ 384
- Vocab pruning: 250,037 โ 15,168 (90% cumulative frequency)
- Compression ratio: 9x
Supported Languages (18)
ko, en, ja, zh, es, fr, de, pt, it, ru, ar, hi, th, vi, id, tr, nl, pl
