Upload L6_bottom_distilled (distilled) from MiniLM-L12

ec6f8fd verified 4 months ago

6.87 kB

language:
  - ko
  - en
  - ja
  - zh
  - es
  - fr
  - de
  - pt
  - it
  - ru
  - ar
  - hi
  - th
  - vi
  - id
  - tr
  - nl
  - pl
tags:
  - sentence-transformers
  - multilingual
  - layer-pruning
  - vocab-pruning
  - knowledge-distillation
  - minilm-l12
library_name: sentence-transformers
pipeline_tag: sentence-similarity
license: apache-2.0

L6_bottom_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	6 / 12
Layer indices	[0, 1, 2, 3, 4, 5]
Strategy	6 layers, bottom half (syntactic-focused)
Parameters	106,825,344
Model size (FP32)	98.1MB
Distilled	Yes

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  1 ← L1         │
  ├─────────────────────────┤    ├─────────────────────────┤
  │  Layer  2               │ ──►  │  Layer  2 ← L2         │
  ├─────────────────────────┤    ├─────────────────────────┤
  │  Layer  3               │ ──►  │  Layer  3 ← L3         │
  ├─────────────────────────┤    ├─────────────────────────┤
  │  Layer  4               │ ──►  │  Layer  4 ← L4         │
  ├─────────────────────────┤    ├─────────────────────────┤
  │  Layer  5               │ ──►  │  Layer  5 ← L5         │
  ├─────────────────────────┤    ├─────────────────────────┤
  │  Layer  6               │  ╳   │                         │
  ├ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─┤    │                         │
  │  Layer  7               │  ╳   │                         │
  ├ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─┤    │                         │
  │  Layer  8               │  ╳   │                         │
  ├ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─┤    │                         │
  │  Layer  9               │  ╳   │                         │
  ├ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─┤    │                         │
  │  Layer 10               │  ╳   │                         │
  ├ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─┤    │                         │
  │  Layer 11               │  ╳   │                         │
  └────────────┬────────────┘    └────────────┬────────────┘
               │                              │
  ┌────────────┴────────────┐    ┌────────────┴────────────┐
  │  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_bottom_distilled", trust_remote_code=True)

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

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

Training

Stage 1: Layer Pruning

Teacher: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 (12 layers, 384d)
Selected layers: [0, 1, 2, 3, 4, 5] (6 layers, bottom half (syntactic-focused))
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