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gemma-student-gemma_emb_compressed_distilled
gomyk/gemma-student-gemma_emb_compressed_distilled
gemma-student-gemma_emb_compressed_distilled is a sentence similarity model from gomyk. Use it when you need a score for how close two texts are. It is set up for sentence-transformers. The card lists the license as gemma.
Compact multilingual sentence encoder compressed from google/embeddinggemma-300m (24x compression).
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12.5M
54.7 MB on disk
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At a glance
- Task
- Sentence Similarity
- Library
- sentence-transformers
- License
- gemma
- Model type
- gemma3_text
- Access
- Public
- Created
- Mar 27, 2026
- Updated
- Mar 27, 2026
- SHA
- 03ea3d2d
Model card
From the Hugging Face model README
gemma_emb_compressed_distilled (Distilled)
Compact multilingual sentence encoder compressed from google/embeddinggemma-300m (24x compression).
Model Details
| Property | Value |
|---|---|
| Base model | google/embeddinggemma-300m |
| Architecture | gemma3_text (decoder) |
| Hidden dim | 384 (from 768) |
| Layers | 4 (from 24) |
| Intermediate | 576 |
| Attention heads | 1 |
| KV heads | 1 |
| Vocab size | 19,485 (from 262,144) |
| Parameters | ~12.5M |
| Model size (FP32) | 47.7MB |
| Compression | 24x |
| Distilled | Yes (2-stage) |
Quick Start
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("gemma_emb_compressed_distilled", trust_remote_code=True)
sentences = [
"Hello, how are you?",
"안녕하세요, 잘 지내세요?",
"こんにちは、元気ですか？",
"你好，你好吗？",
]
embeddings = model.encode(sentences)
print(embeddings.shape) # (4, 384)
MTEB Evaluation Results
Overall Average: 25.82%
| Task Group | Average |
|---|---|
| Classification | 34.81% |
| Clustering | 28.29% |
| STS | 15.62% |
Classification
| Task | Average | Details |
|---|---|---|
| AmazonCounterfactualClassification | 54.73% | en-ext: 56.48%, en: 56.1%, de: 56.03%, ja: 50.33% |
| Banking77Classification | 12.88% | default: 12.88% |
| ImdbClassification | 51.34% | default: 51.34% |
| MTOPDomainClassification | 33.78% | th: 35.0%, es: 34.41%, fr: 33.67%, en: 33.27%, hi: 33.19% |
| MassiveIntentClassification | 15.53% | zh-CN: 54.26%, ja: 45.35%, zh-TW: 45.06%, ko: 40.29%, th: 17.34% |
| MassiveScenarioClassification | 20.81% | zh-CN: 66.69%, ja: 53.96%, zh-TW: 53.52%, ko: 49.29%, vi: 22.58% |
| ToxicConversationsClassification | 51.68% | default: 51.68% |
| TweetSentimentExtractionClassification | 37.77% | default: 37.77% |
Clustering
| Task | Average | Details |
|---|---|---|
| ArXivHierarchicalClusteringP2P | 47.02% | default: 47.02% |
| ArXivHierarchicalClusteringS2S | 48.8% | default: 48.8% |
| BiorxivClusteringP2P.v2 | 8.06% | default: 8.06% |
| MedrxivClusteringP2P.v2 | 18.8% | default: 18.8% |
| MedrxivClusteringS2S.v2 | 18.2% | default: 18.2% |
| StackExchangeClustering.v2 | 40.68% | default: 40.68% |
| StackExchangeClusteringP2P.v2 | 34.68% | default: 34.68% |
| TwentyNewsgroupsClustering.v2 | 10.04% | default: 10.04% |
STS
| Task | Average | Details |
|---|---|---|
| BIOSSES | 20.21% | default: 20.21% |
| SICK-R | 23.92% | default: 23.92% |
| STS12 | 12.98% | default: 12.98% |
| STS13 | 16.57% | default: 16.57% |
| STS14 | 1.39% | default: 1.39% |
| STS15 | 22.27% | default: 22.27% |
| STS17 | 26.2% | en-en: 42.85%, es-es: 41.09%, en-tr: 34.02%, ar-ar: 33.99%, it-en: 27.46% |
| STS22.v2 | 13.11% | fr-pl: 39.44%, en: 25.46%, es: 24.75%, de-fr: 21.99%, ar: 15.92% |
| STSBenchmark | 3.94% | default: 3.94% |
Distillation Impact
| Task | Before | After | Delta |
|---|---|---|---|
| AmazonCounterfactualClassification | 59.01% | 54.73% | -4.28%p |
| ArXivHierarchicalClusteringP2P | 45.54% | 47.02% | +1.48%p |
| ArXivHierarchicalClusteringS2S | 45.1% | 48.8% | +3.7%p |
| BIOSSES | -0.64% | 20.21% | +20.85%p |
| Banking77Classification | 19.07% | 12.88% | -6.19%p |
| BiorxivClusteringP2P.v2 | 8.07% | 8.06% | -0.01%p |
| ImdbClassification | 52.55% | 51.34% | -1.21%p |
| MTOPDomainClassification | 38.89% | 33.78% | -5.11%p |
| MassiveIntentClassification | 22.16% | 15.53% | -6.63%p |
| MassiveScenarioClassification | 23.12% | 20.81% | -2.31%p |
| MedrxivClusteringP2P.v2 | 19.06% | 18.8% | -0.26%p |
| MedrxivClusteringS2S.v2 | 17.57% | 18.2% | +0.63%p |
| SICK-R | 30.8% | 23.92% | -6.88%p |
| STS12 | 23.59% | 12.98% | -10.61%p |
| STS13 | 19.19% | 16.57% | -2.62%p |
| STS14 | 11.24% | 1.39% | -9.85%p |
| STS15 | 30.55% | 22.27% | -8.28%p |
| STS17 | 12.2% | 26.2% | +14.0%p |
| STS22.v2 | 15.19% | 13.11% | -2.08%p |
| STSBenchmark | 15.56% | 3.94% | -11.62%p |
| StackExchangeClustering.v2 | 41.53% | 40.68% | -0.85%p |
| StackExchangeClusteringP2P.v2 | 33.43% | 34.68% | +1.25%p |
| ToxicConversationsClassification | 50.12% | 51.68% | +1.56%p |
| TweetSentimentExtractionClassification | 36.28% | 37.77% | +1.49%p |
| TwentyNewsgroupsClustering.v2 | 8.85% | 10.04% | +1.19%p |
Training
Stage 1: Model Compression
- Teacher:
google/embeddinggemma-300m(24L, 768d, 303M params) - Compression: Layer pruning → Hidden dim reduction → Vocab pruning
- Result: 4L / 384d / 19,485 vocab
Stage 2: Two-Stage Knowledge Distillation
Compression ratio 24x requires progressive distillation:
- Stage 1: Teacher (303M) → Intermediate (~61M)
- MSE + Cosine Similarity loss
- MTEB task datasets (Classification/Clustering/STS)
- Stage 2: Intermediate → Final Student (12.5M)
- Same training objective
- AdamW (lr=2e-5, weight_decay=0.01), Cosine annealing
License
This model is a derivative of Google's Gemma. Gemma is provided under and subject to the Gemma Terms of Use found at ai.google.dev/gemma/terms. Use of this model must comply with the Gemma Prohibited Use Policy.
Supported Languages (18)
ko, en, ja, zh, es, fr, de, pt, it, ru, ar, hi, th, vi, id, tr, nl, pl
- Task
- Sentence Similarity
- Library
- sentence-transformers
- Type
- gemma3_text
- License
- gemma
- Languages
- ko, en, ja, zh, es, fr
- Created
- Mar 27, 2026
- Updated
- Mar 27, 2026