L6_uniform

Lightweight sentence encoder created from sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 via layer pruning + vocabulary pruning.

Model Details

Property	Value
Teacher	sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
Architecture	MiniLM-L12 (pruned)
Hidden dim	384
Layers	6 / 12
Layer indices	[0, 2, 4, 7, 9, 11]
Strategy	6 layers, evenly spaced (general-purpose)
Parameters	106,825,344
Model size (FP32)	98.1MB
Distilled	No

Architecture

==============================================================
  TEACHER: MiniLM-L12  →  STUDENT: 6L / 38,775 vocab
==============================================================

            TEACHER                        STUDENT          
  ───────────────────────────    ───────────────────────────

  ┌─────────────────────────┐    ┌─────────────────────────┐
  │   Input Tokens          │    │   Input Tokens          │
  └────────────┬────────────┘    └────────────┬────────────┘
               │                              │
  ┌────────────┴────────────┐    ┌────────────┴────────────┐
  │  Embeddings             │    │  Embeddings (pruned)    │
  │  vocab: 250,002         │    │  vocab:  38,775         │
  │  dim:  384              │    │  dim:  384              │
  └────────────┬────────────┘    └────────────┬────────────┘
               │                              │
  ┌─────────────────────────┐    ┌─────────────────────────┐
  │  Layer  0               │ ──►  │  Layer  0 ← L0         │
  ├─────────────────────────┤    ├─────────────────────────┤
  │  Layer  1               │  ╳   │                         │
  ├─────────────────────────┤    ├─────────────────────────┤
  │  Layer  2               │ ──►  │  Layer  1 ← L2         │
  ├─────────────────────────┤    ├─────────────────────────┤
  │  Layer  3               │  ╳   │                         │
  ├─────────────────────────┤    ├─────────────────────────┤
  │  Layer  4               │ ──►  │  Layer  2 ← L4         │
  ├─────────────────────────┤    ├─────────────────────────┤
  │  Layer  5               │  ╳   │                         │
  ├ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─┤    │                         │
  │  Layer  6               │  ╳   │                         │
  ├─────────────────────────┤    ├─────────────────────────┤
  │  Layer  7               │ ──►  │  Layer  3 ← L7         │
  ├─────────────────────────┤    ├─────────────────────────┤
  │  Layer  8               │  ╳   │                         │
  ├─────────────────────────┤    ├─────────────────────────┤
  │  Layer  9               │ ──►  │  Layer  4 ← L9         │
  ├─────────────────────────┤    ├─────────────────────────┤
  │  Layer 10               │  ╳   │                         │
  ├─────────────────────────┤    ├─────────────────────────┤
  │  Layer 11               │ ──►  │  Layer  5 ← L11        │
  └────────────┬────────────┘    └────────────┬────────────┘
               │                              │
  ┌────────────┴────────────┐    ┌────────────┴────────────┐
  │  Mean Pooling           │    │  Mean Pooling           │
  │  → 384d embedding       │    │  → 384d embedding       │
  └─────────────────────────┘    └─────────────────────────┘

  Size: 448.0MB (FP32)           →  98.1MB (FP32)
  Params: 117,451,392        →  25,714,176
  Reduction: 78.1%
==============================================================

Quick Start

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("L6_uniform", trust_remote_code=True)

sentences = [
    "Hello, how are you?",
    "안녕하세요",
    "Bonjour, comment allez-vous?",
]

embeddings = model.encode(sentences)
print(embeddings.shape)  # (3, 384)

MTEB Evaluation Results

Overall Average: 53.16%

Task Group	Average
Classification	58.9%
Clustering	34.38%
STS	64.74%

Classification

Task	Average	Details
AmazonCounterfactualClassification	68.27%	de: 69.95%, en: 69.4%, en-ext: 68.78%
Banking77Classification	73.53%	default: 73.53%
ImdbClassification	60.64%	default: 60.64%
MTOPDomainClassification	75.11%	en: 84.23%, es: 78.09%, th: 76.25%
MassiveIntentClassification	37.62%	en: 62.82%, zh-CN: 60.31%, ja: 58.99%
MassiveScenarioClassification	41.45%	en: 68.66%, zh-CN: 67.6%, ja: 63.51%
ToxicConversationsClassification	61.36%	default: 61.36%
TweetSentimentExtractionClassification	53.21%	default: 53.21%

Clustering

Task	Average	Details
ArXivHierarchicalClusteringP2P	50.12%	default: 50.12%
ArXivHierarchicalClusteringS2S	46.66%	default: 46.66%
BiorxivClusteringP2P.v2	25.42%	default: 25.42%
MedrxivClusteringP2P.v2	28.32%	default: 28.32%
MedrxivClusteringS2S.v2	25.33%	default: 25.33%
StackExchangeClustering.v2	44.13%	default: 44.13%
StackExchangeClusteringP2P.v2	33.07%	default: 33.07%
TwentyNewsgroupsClustering.v2	22.01%	default: 22.01%

STS

Task	Average	Details
BIOSSES	57.32%	default: 57.32%
SICK-R	69.91%	default: 69.91%
STS12	66.88%	default: 66.88%
STS13	71.42%	default: 71.42%
STS14	68.52%	default: 68.52%
STS15	79.84%	default: 79.84%
STS17	53.52%	en-en: 82.46%, es-es: 78.22%, ko-ko: 66.78%
STS22.v2	40.57%	zh: 59.49%, es: 58.65%, fr: 57.4%
STSBenchmark	74.69%	default: 74.69%

Training

Created via layer pruning + vocabulary pruning (no additional training):

Teacher: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 (12 layers, 384d)
Layer selection: [0, 2, 4, 7, 9, 11] - 6 layers, evenly spaced (general-purpose)
Vocab pruning: Corpus-based filtering for target languages

Supported Languages (18)

ko, en, ja, zh, es, fr, de, pt, it, ru, ar, hi, th, vi, id, tr, nl, pl

Downloads last month: 3

Safetensors

Model size

25.7M params

Tensor type

F32