David Snelling
563b983fcc
- Added new scripts under `brainy-models-package/scripts`:
- **`compress-models.js`**: Implements model compression with float16 and int8 precision to create optimized variants of Universal Sentence Encoder models.
- **`download-full-models.js`**: Downloads the complete Universal Sentence Encoder model for offline usage.
- **`download-model.js`**: Downloads reference files for TensorFlow Hub-based Universal Sentence Encoder.
- Introduced a demonstration script:
- **`demo-optional-model-bundling.js`**: Highlights the solution of bundling models to eliminate network dependency, ensuring reliability and offline capability.
- Key Features:
- **Compression**:
- Reduced model size with float16 (balanced precision and size) and int8 (low-memory environments) options.
- Generated compression summaries for quick insights into model variants and saved space.
- **Offline Reliability**:
- Bundled versions eliminate first-load delays, network dependencies, and failures.
- Ensures rapid initialization in offline and memory-constrained scenarios.
- **Dynamic Optimization**:
- Tailored optimization profiles for various use cases: general, low-memory, and high-performance.
- **Demonstration and Documentation**:
- Comprehensive demo showcasing benefits of bundled models over online loading.
- Examples for usage, testing, and integration with Brainy.
**Purpose**: Introduce essential scripts and tools to enable efficient, offline-ready model usage, streamlining the embedding workflow while ensuring reliability in production and resource-constrained environments.
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372 B
JSON
12 lines
No EOL
372 B
JSON
{
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"name": "universal-sentence-encoder",
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"version": "1.0.0",
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"description": "Universal Sentence Encoder model for text embeddings",
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"dimensions": 512,
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"date": "2025-08-01T21:59:56.632Z",
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"source": "tensorflow-models/universal-sentence-encoder",
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"savedLocally": true,
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"savedWith": "manual-embedding",
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"embeddingSize": 512,
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"approach": "tfhub-reference"
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} |