L4_uniform_distilled (Distilled)

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

Model Details

Property	Value
Teacher	sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
Architecture	MiniLM-L12 (pruned)
Hidden dim	384
Layers	4 / 12
Layer indices	[0, 4, 7, 11]
Strategy	4 layers, evenly spaced (compact)
Parameters	103,283,328
Model size (FP32)	84.6MB
Distilled	Yes

Architecture

==============================================================
  TEACHER: MiniLM-L12  →  STUDENT: 4L / 38,755 vocab
==============================================================

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

  ┌─────────────────────────┐    ┌─────────────────────────┐
  │   Input Tokens          │    │   Input Tokens          │
  └────────────┬────────────┘    └────────────┬────────────┘
               │                              │
  ┌────────────┴────────────┐    ┌────────────┴────────────┐
  │  Embeddings             │    │  Embeddings (pruned)    │
  │  vocab: 250,002         │    │  vocab:  38,755         │
  │  dim:  384              │    │  dim:  384              │
  └────────────┬────────────┘    └────────────┬────────────┘
               │                              │
  ┌─────────────────────────┐    ┌─────────────────────────┐
  │  Layer  0               │ ──►  │  Layer  0 ← L0         │
  ├─────────────────────────┤    ├─────────────────────────┤
  │  Layer  1               │  ╳   │                         │
  ├ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─┤    │                         │
  │  Layer  2               │  ╳   │                         │
  ├ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─┤    │                         │
  │  Layer  3               │  ╳   │                         │
  ├─────────────────────────┤    ├─────────────────────────┤
  │  Layer  4               │ ──►  │  Layer  1 ← L4         │
  ├─────────────────────────┤    ├─────────────────────────┤
  │  Layer  5               │  ╳   │                         │
  ├ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─┤    │                         │
  │  Layer  6               │  ╳   │                         │
  ├─────────────────────────┤    ├─────────────────────────┤
  │  Layer  7               │ ──►  │  Layer  2 ← L7         │
  ├─────────────────────────┤    ├─────────────────────────┤
  │  Layer  8               │  ╳   │                         │
  ├ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─┤    │                         │
  │  Layer  9               │  ╳   │                         │
  ├ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─┤    │                         │
  │  Layer 10               │  ╳   │                         │
  ├─────────────────────────┤    ├─────────────────────────┤
  │  Layer 11               │ ──►  │  Layer  3 ← L11        │
  └────────────┬────────────┘    └────────────┬────────────┘
               │                              │
  ┌────────────┴────────────┐    ┌────────────┴────────────┐
  │  Mean Pooling           │    │  Mean Pooling           │
  │  → 384d embedding       │    │  → 384d embedding       │
  └─────────────────────────┘    └─────────────────────────┘

  Size: 448.0MB (FP32)           →  84.6MB (FP32)
  Params: 117,451,392        →  22,164,480
  Reduction: 81.1%
==============================================================

Quick Start

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("L4_uniform_distilled", 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: 54.62%

Task Group	Average
Classification	58.95%
Clustering	36.16%
STS	67.19%

Classification

Task	Average	Details
AmazonCounterfactualClassification	65.7%	en: 68.34%, de: 67.09%, en-ext: 66.57%
Banking77Classification	78.43%	default: 78.43%
ImdbClassification	60.43%	default: 60.43%
MTOPDomainClassification	74.26%	en: 83.96%, es: 78.09%, fr: 76.42%
MassiveIntentClassification	34.72%	en: 65.57%, zh-CN: 62.56%, ja: 62.19%
MassiveScenarioClassification	40.37%	en: 71.61%, zh-CN: 69.22%, ja: 67.48%
ToxicConversationsClassification	61.37%	default: 61.37%
TweetSentimentExtractionClassification	56.29%	default: 56.29%

Clustering

Task	Average	Details
ArXivHierarchicalClusteringP2P	51.36%	default: 51.36%
ArXivHierarchicalClusteringS2S	46.3%	default: 46.3%
BiorxivClusteringP2P.v2	26.63%	default: 26.63%
MedrxivClusteringP2P.v2	30.15%	default: 30.15%
MedrxivClusteringS2S.v2	25.7%	default: 25.7%
StackExchangeClustering.v2	46.56%	default: 46.56%
StackExchangeClusteringP2P.v2	35.85%	default: 35.85%
TwentyNewsgroupsClustering.v2	26.74%	default: 26.74%

STS

Task	Average	Details
BIOSSES	53.2%	default: 53.2%
SICK-R	73.61%	default: 73.61%
STS12	72.11%	default: 72.11%
STS13	75.62%	default: 75.62%
STS14	72.2%	default: 72.2%
STS15	80.86%	default: 80.86%
STS17	48.49%	en-en: 81.73%, es-es: 75.65%, ar-ar: 57.76%
STS22.v2	49.17%	fr: 69.99%, es-en: 65.46%, zh: 63.87%
STSBenchmark	79.48%	default: 79.48%

Distillation Impact

Task	Before	After	Delta
AmazonCounterfactualClassification	67.02%	65.7%	-1.32%p
ArXivHierarchicalClusteringP2P	49.93%	51.36%	+1.43%p
ArXivHierarchicalClusteringS2S	46.08%	46.3%	+0.22%p
Banking77Classification	69.18%	78.43%	+9.25%p
BiorxivClusteringP2P.v2	21.47%	26.63%	+5.16%p
BIOSSES	45.64%	53.2%	+7.56%p
ImdbClassification	59.38%	60.43%	+1.05%p
MassiveIntentClassification	36.9%	34.72%	-2.18%p
MassiveScenarioClassification	39.51%	40.37%	+0.86%p
MedrxivClusteringP2P.v2	26.05%	30.15%	+4.1%p
MedrxivClusteringS2S.v2	22.94%	25.7%	+2.76%p
MTOPDomainClassification	71.48%	74.26%	+2.78%p
SICK-R	62.01%	73.61%	+11.6%p
StackExchangeClustering.v2	41.23%	46.56%	+5.33%p
StackExchangeClusteringP2P.v2	32.19%	35.85%	+3.66%p
STS12	57.85%	72.11%	+14.26%p
STS13	65.48%	75.62%	+10.14%p
STS14	60.39%	72.2%	+11.81%p
STS15	73.93%	80.86%	+6.93%p
STS17	46.29%	48.49%	+2.2%p
STS22.v2	37.34%	49.17%	+11.83%p
STSBenchmark	65.38%	79.48%	+14.1%p
ToxicConversationsClassification	62.02%	61.37%	-0.65%p
TweetSentimentExtractionClassification	49.43%	56.29%	+6.86%p
TwentyNewsgroupsClustering.v2	16.43%	26.74%	+10.31%p

Training

Stage 1: Layer Pruning

Teacher: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 (12 layers, 384d)
Selected layers: [0, 4, 7, 11] (4 layers, evenly spaced (compact))
Vocabulary pruning applied

Stage 2: Knowledge Distillation

Method: MSE + Cosine Similarity loss
Data: MTEB Classification/Clustering/STS task datasets
Optimizer: AdamW (lr=2e-5, weight_decay=0.01)
Schedule: Cosine annealing over 3 epochs

Supported Languages (18)

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

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Safetensors

Model size

22.2M params

Tensor type

F32