gte_L4_uniform

Lightweight sentence encoder created from alibaba-NLP/gte-multilingual-base via layer pruning + vocabulary pruning.

Model Details

Property	Value
Teacher	alibaba-NLP/gte-multilingual-base
Architecture	GTE-multilingual (pruned)
Hidden dim	768
Layers	4 / 12
Layer indices	[0, 4, 7, 11]
Strategy	4 layers, evenly spaced from GTE-multilingual (12L)
Parameters	220,757,760
Model size (FP32)	277.7MB
Distilled	No

Architecture

==============================================================
  TEACHER: GTE-multilingual  →  STUDENT: 4L / 57,376 vocab
==============================================================

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

  ┌─────────────────────────┐    ┌─────────────────────────┐
  │   Input Tokens          │    │   Input Tokens          │
  └────────────┬────────────┘    └────────────┬────────────┘
               │                              │
  ┌────────────┴────────────┐    ┌────────────┴────────────┐
  │  Embeddings             │    │  Embeddings (pruned)    │
  │  vocab: 250,048         │    │  vocab:  57,376         │
  │  dim:  768              │    │  dim:  768              │
  └────────────┬────────────┘    └────────────┬────────────┘
               │                              │
  ┌─────────────────────────┐    ┌─────────────────────────┐
  │  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           │
  │  → 768d embedding       │    │  → 768d embedding       │
  └─────────────────────────┘    └─────────────────────────┘

  Size: 1058.2MB (FP32)           →  277.7MB (FP32)
  Params: 277,405,440        →  72,785,664
  Reduction: 73.8%
==============================================================

Quick Start

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("gte_L4_uniform", trust_remote_code=True)

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

embeddings = model.encode(sentences)
print(embeddings.shape)  # (3, 768)

MTEB Evaluation Results

Overall Average: 45.57%

Task Group	Average
Classification	55.62%
Clustering	30.05%
STS	50.42%

Classification

Task	Average	Details
AmazonCounterfactualClassification	65.24%	en: 67.1%, en-ext: 66.57%, de: 65.49%
Banking77Classification	68.58%	default: 68.58%
ImdbClassification	63.28%	default: 63.28%
MTOPDomainClassification	68.67%	en: 78.94%, es: 71.23%, hi: 69.49%
MassiveIntentClassification	35.71%	zh-CN: 56.02%, en: 55.74%, ja: 51.76%
MassiveScenarioClassification	37.58%	zh-CN: 61.24%, en: 59.46%, ja: 53.4%
ToxicConversationsClassification	57.84%	default: 57.84%
TweetSentimentExtractionClassification	48.1%	default: 48.1%

Clustering

Task	Average	Details
ArXivHierarchicalClusteringP2P	50.97%	default: 50.97%
ArXivHierarchicalClusteringS2S	43.38%	default: 43.38%
BiorxivClusteringP2P.v2	20.78%	default: 20.78%
MedrxivClusteringP2P.v2	26.37%	default: 26.37%
MedrxivClusteringS2S.v2	20.98%	default: 20.98%
StackExchangeClustering.v2	34.36%	default: 34.36%
StackExchangeClusteringP2P.v2	31.55%	default: 31.55%
TwentyNewsgroupsClustering.v2	12.03%	default: 12.03%

STS

Task	Average	Details
BIOSSES	42.61%	default: 42.61%
SICK-R	55.11%	default: 55.11%
STS12	47.97%	default: 47.97%
STS13	65.61%	default: 65.61%
STS14	57.02%	default: 57.02%
STS15	64.76%	default: 64.76%
STS17	17.95%	es-es: 68.69%, en-en: 63.86%, ko-ko: 55.96%
STS22.v2	40.55%	zh: 65.02%, es: 58.47%, it: 55.59%
STSBenchmark	62.23%	default: 62.23%

Training

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

Teacher: alibaba-NLP/gte-multilingual-base (12 layers, 768d)
Layer selection: [0, 4, 7, 11] - 4 layers, evenly spaced from GTE-multilingual (12L)
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

gomyk/gte-student-gte_L4_uniform