| --- |
| license: cc |
| task_categories: |
| - text-classification |
| - feature-extraction |
| language: |
| - en |
| --- |
| # Text Quality Assessment Dataset |
|
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| ## Overview |
|
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| This dataset is designed to assess text quality robustly across various domains for NLP and AI applications. It provides a composite quality score based on multiple classifiers, offering a more comprehensive evaluation of text quality beyond educational domains. |
|
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| ## Dataset Details |
|
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| - **Size**: 100,000 sentences |
| - **Source**: 20,000 sentences from each of 5 different datasets |
| - [allenai/c4](https://huggingface.co/datasets/) |
| - [HuggingFaceFW/fineweb-edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu) |
| - [monology/pile-uncopyrighted](https://huggingface.co/datasets/monology/pile-uncopyrighted) |
| - [agentlans/common-crawl-sample](https://huggingface.co/datasets/agentlans/common-crawl-sample) |
| - [agentlans/wikipedia-paragraphs](https://huggingface.co/datasets/agentlans/wikipedia-paragraphs) |
|
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| ## Features |
|
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| The quality scores of each text were assessed using |
| - [HuggingFaceFW/fineweb-edu-classifier](https://huggingface.co/HuggingFaceFW/fineweb-edu-classifier) |
| - [nvidia/quality-classifier-deberta](https://huggingface.co/nvidia/quality-classifier-deberta) |
|
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| 1. **Text Length**: |
| - Measured in characters |
| - Box-Cox transformed |
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| 2. **Fineweb-edu Classifier Score**: |
| - Raw logits |
| - Yeo-Johnson transformed |
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| 3. **NVIDIA Quality Score**: |
| - Logits of "High" quality level - logits of "Low" quality level |
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| 5. **Composite Quality Score**: |
| - First principal component of fineweb-edu and NVIDIA scores |
| - Adjusted for length using linear regression with the transformed text length |
|
|
| ## Key Insights |
|
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| - Fineweb-edu and NVIDIA scores show weak correlation |
| - Composite quality score correlates with both individual scores |
| - Clear quality differences observed across the 5 source datasets |
|
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| **Figure 1**: Correlation between individual scores (fineweb-edu and NVIDIA) and the composite quality score. Each point represents a single row of text. |
| <img src="https://huggingface.co/datasets/agentlans/text-quality/resolve/main/CorrelationPlot.png" alt="Quality score scatterplot" width="50%"/> |
|
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| **Figure 2**: Distribution of quality scores across the five source datasets, highlighting quality differences |
| <img src="https://huggingface.co/datasets/agentlans/text-quality/resolve/main/QualityDistribution.png" alt="Quality score scatterplot" width="75%"/> |
|
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| ## Applications |
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| - Benchmarking text quality across various domains |
| - Training robust text quality assessment models |
| - Analyzing dataset quality for diverse NLP tasks |
|
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| ## Limitations |
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| - Based on existing classifiers, may inherit their biases |
| - The current quality definition may not capture all aspects of text quality |
|
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| ## Ethics and Privacy |
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| - No personal information is included in the dataset |
| - Users should appropriately credit the source datasets when using this compilation |
