|
|
--- |
|
|
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 |
