Spaces:
Sleeping
Sleeping
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README.md
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# Synopsis Scorer with Privacy Protection
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This application evaluates the quality of text synopses against their source content while maintaining privacy through robust text anonymization techniques.
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### Prerequisites
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- Python 3.8+
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- At least
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### Installation
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```
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5. Download the Gemma model:
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The application will automatically download the quantized Gemma model on first run
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```
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mkdir -p models
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Download the model from this url: https://huggingface.co/google/gemma-3-4b-it-qat-q4_0-gguf/resolve/main/gemma-3-4b-it-q4_0.gguf and place it in "models" folder.
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```
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### Running the Application
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β βββ secrets.toml # Configuration secrets
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βββ models/ # Downloaded LLM models
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```
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---
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title: Synopsis Scorer
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emoji: π
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colorFrom: blue
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colorTo: indigo
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sdk: streamlit
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sdk_version: 1.31.0
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app_file: app.py
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pinned: false
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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# Synopsis Scorer with Privacy Protection
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This application evaluates the quality of text synopses against their source content while maintaining privacy through robust text anonymization techniques.
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### Prerequisites
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- Python 3.8+
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- At least 8GB RAM (recommended for LLM inference)
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- 2GB disk space
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### Installation
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```
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5. Download the Gemma model:
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The application will automatically download the quantized Gemma model on first run
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### Running the Application
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β βββ secrets.toml # Configuration secrets
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βββ models/ # Downloaded LLM models
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```
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