embedinggemma_arkts

3
—
by
hreyulog
Embedding Model
OTHER
New
3 downloads
Early-stage
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Quick Summary

AI model with specialized capabilities.

Training Data Analysis

🟡 Average (4.3/10)

Researched training datasets used by embedinggemma_arkts with quality assessment

Specialized For

general
science
multilingual
reasoning

Training Datasets (3)

common crawl
🔴 2.5/10
general
science
Key Strengths
  • •Scale and Accessibility: At 9.5+ petabytes, Common Crawl provides unprecedented scale for training d...
  • •Diversity: The dataset captures billions of web pages across multiple domains and content types, ena...
  • •Comprehensive Coverage: Despite limitations, Common Crawl attempts to represent the broader web acro...
Considerations
  • •Biased Coverage: The crawling process prioritizes frequently linked domains, making content from dig...
  • •Large-Scale Problematic Content: Contains significant amounts of hate speech, pornography, violent c...
wikipedia
🟡 5/10
science
multilingual
Key Strengths
  • •High-Quality Content: Wikipedia articles are subject to community review, fact-checking, and citatio...
  • •Multilingual Coverage: Available in 300+ languages, enabling training of models that understand and ...
  • •Structured Knowledge: Articles follow consistent formatting with clear sections, allowing models to ...
Considerations
  • •Language Inequality: Low-resource language editions have significantly lower quality, fewer articles...
  • •Biased Coverage: Reflects biases in contributor demographics; topics related to Western culture and ...
arxiv
🟡 5.5/10
science
reasoning
Key Strengths
  • •Scientific Authority: Peer-reviewed content from established repository
  • •Domain-Specific: Specialized vocabulary and concepts
  • •Mathematical Content: Includes complex equations and notation
Considerations
  • •Specialized: Primarily technical and mathematical content
  • •English-Heavy: Predominantly English-language papers

Explore our comprehensive training dataset analysis

View All Datasets

Code Examples

Usagebash
pip install -U sentence-transformers
Usagepython
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("hreyulog/embedinggemma_arkts")
# Run inference
queries = [
    "Transform an array of points with all matrices. VERY IMPORTANT: Keep\nmatrix order \"value-touch-offset\" when transforming.\n\n@param pts",
]
documents = [
    "public pointValuesToPixel(pts: number[]) {\n    this.mMatrixValueToPx.mapPoints(pts);\n    this.mViewPortHandler.getMatrixTouch().mapPoints(pts);\n    this.mMatrixOffset.mapPoints(pts);\n  }",
    'makeNode(uiContext: UIContext): FrameNode {\n    this.rootNode = new FrameNode(uiContext);\n    if (this.rootNode !== null) {\n      this.rootRenderNode = this.rootNode.getRenderNode();\n    }\n    return this.rootNode;\n  }',
    'export interface OnlineLunarYear {\n  year: number;\n  zodiac: string;\n  ganzhi: string;\n  leapMonth: number;\n  isLeapYear: boolean;\n  leapMonthDays?: number;\n  solarTerms: SolarTermInfo[];\n  festivals: LunarFestival[];\n}',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 768] [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[ 0.8923,  0.0264, -0.0212]])

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