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---
license: cc
task_categories:
- text-classification
- feature-extraction
language:
- en
---
# Text Quality Assessment Dataset
## Overview
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.
## Dataset Details
- **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)
## Features
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)
1. **Text Length**:
- Measured in characters
- Box-Cox transformed
2. **Fineweb-edu Classifier Score**:
- Raw logits
- Yeo-Johnson transformed
3. **NVIDIA Quality Score**:
- Logits of "High" quality level - logits of "Low" quality level
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
- 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
**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%"/>
**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%"/>
## Applications
- Benchmarking text quality across various domains
- Training robust text quality assessment models
- Analyzing dataset quality for diverse NLP tasks
## Limitations
- Based on existing classifiers, may inherit their biases
- The current quality definition may not capture all aspects of text quality
## Ethics and Privacy
- No personal information is included in the dataset
- Users should appropriately credit the source datasets when using this compilation |