embeddinggemma-300m-litert

17
—
by
kamalkraj
Embedding Model
OTHER
New
17 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 embeddinggemma-300m-litert 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

Prerequisitesbash
uv venv --python 3.12
source .venv/bin/activate

# Clone and setup the custom litert-torch tool
git clone https://github.com/kamalkraj/litert-torch.git
cd litert-torch
git checkout embedding_gemma

# Install dependencies
uv pip install -r requirements.txt
uv pip install -e .
Install dependenciesbash
hf download google/embeddinggemma-300m --local-dir embeddinggemma-300m

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