README.md

metadata

license: apache-2.0
language:
  - en
base_model:
  - google/embeddinggemma-300m
pipeline_tag: sentence-similarity
library_name: sentence-transformers
tags:
  - mteb
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  - transformers
  - pytorch
model-index:
  - name: Supertron-embedding-300M
    results:
      - task:
          type: STS
          name: STSBenchmark
        dataset:
          name: MTEB STSBenchmark
          type: mteb/STSBenchmark
          config: default
          split: test
        metrics:
          - type: cos_sim_spearman
            value: 87.1012
      - task:
          type: STS
          name: STS12
        dataset:
          name: MTEB STS12
          type: mteb/STS12
          config: default
          split: test
        metrics:
          - type: cos_sim_spearman
            value: 80.1767
      - task:
          type: STS
          name: BIOSSES
        dataset:
          name: MTEB BIOSSES
          type: mteb/BIOSSES
          config: default
          split: test
        metrics:
          - type: cos_sim_spearman
            value: 82.9778
      - task:
          type: Retrieval
          name: NFCorpus
        dataset:
          name: MTEB NFCorpus
          type: mteb/NFCorpus
          config: default
          split: test
        metrics:
          - type: ndcg_at_10
            value: 37.074
      - task:
          type: Classification
          name: AmazonCounterfactualClassification
        dataset:
          name: MTEB AmazonCounterfactualClassification
          type: mteb/AmazonCounterfactualClassification
          config: default
          split: test
        metrics:
          - type: accuracy
            value: 83.3415625
      - task:
          type: Clustering
          name: TwentyNewsgroupsClustering.v2
        dataset:
          name: MTEB TwentyNewsgroupsClustering.v2
          type: mteb/TwentyNewsgroupsClustering.v2
          config: default
          split: test
        metrics:
          - type: v_measure
            value: 50.01057211780597

Supertron-embedding-300M: High-Efficiency Semantic Representation Model

Model Description

Supertron-embedding-300M is a high-performance, compact embedding model fine-tuned from the google/embeddinggemma-300m architecture. It is specifically designed to provide state-of-the-art semantic representations for Retrieval-Augmented Generation (RAG), semantic search, and document clustering applications while maintaining a low computational footprint suitable for production environments.

• Developed by: Surpem
• Model Type: Sentence Transformer
• Architecture: Gemma-based Dense Transformer
• Base Model: google/embeddinggemma-300m
• License: Apache 2.0
• Language: English (en)

Results

Supertron-embedding-300M demonstrates competitive performance across the Massive Text Embedding Benchmark (MTEB). It is particularly effective in Semantic Textual Similarity (STS) tasks, outperforming many larger models in its weight class.

Task Category	Task Name	Metric	Score
Semantic Similarity	STSBenchmark	cos_sim_spearman	87.10
Semantic Similarity	STS12	cos_sim_spearman	80.18
Semantic Similarity	BIOSSES	cos_sim_spearman	82.98
Retrieval	NFCorpus	NDCG@10	37.07
Classification	AmazonCounterfactual	Accuracy	83.34
Clustering	TwentyNewsgroups	V-Measure	50.01

Get Started

This model can be easily integrated using the sentence-transformers library.

from sentence_transformers import SentenceTransformer

model_id = "surpem/Supertron-embedding-300M"

# Load the model
model = SentenceTransformer(model_id)

# Define target text
sentences = [
    "The financial results exceeded market expectations.",
    "The company reported better than expected quarterly earnings."
]

# Compute embeddings
embeddings = model.encode(sentences)

# Calculate cosine similarity
similarity = model.similarity(embeddings[0], embeddings[1])
print(f"Semantic Similarity: {similarity.item():.4f}")
Training Procedure
Hyperparameters
Precision: bfloat16

Max Sequence Length: 256 tokens

Optimizer: AdamW

Batch Size: 256

Learning Rate: 2e-5

Citation
Code-Snippet
@misc{surpem2026supertron,
      title={Supertron-embedding-300M: High-Efficiency Semantic Representation Model},
      author={Surpem},
      year={2026},
      url={[https://huggingface.co/surpem/Supertron-embedding-300M](https://huggingface.co/surpem/Supertron-embedding-300M)},
}