README.md

metadata

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
  • HuggingFaceFW/fineweb-edu
  • monology/pile-uncopyrighted
  • agentlans/common-crawl-sample
  • agentlans/wikipedia-paragraphs

Features

The quality scores of each text were assessed using

• HuggingFaceFW/fineweb-edu-classifier
• 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:

   • Weighted average of quality levels:
     • "Low" (0)
     • "Medium" (0.5)
     • "High" (1)
   • Weighted by predicted probabilities (result between 0 and 1)
   • Logit transformed
   • Yeo-Johnson transformed

4. Composite Quality Score:

   • First principal component of fineweb-edu and NVIDIA scores

All 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.

Figure 2: Distribution of quality scores across the five source datasets, highlighting quality differences

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