Datasets:

agentlans
/

low-readability-text

language:
  - en
license: odc-by
task_categories:
  - text-generation
  - translation
tags:
  - text-simplification
  - information-extraction
  - readability
  - fine-web
configs:
  - config_name: sample_k10
    data_files:
      - path:
          - sample_k10.jsonl.zst
        split: train
  - config_name: sample_k100
    data_files:
      - path:
          - sample_k100.jsonl.zst
        split: train
  - config_name: sample_k1000
    data_files:
      - path:
          - sample_k1000.jsonl.zst
        split: train
  - config_name: sample_k10000
    data_files:
      - path:
          - sample_k10000.jsonl.zst
        split: train
  - config_name: sample_k100000
    default: true
    data_files:
      - path:
          - sample_k100000.jsonl.zst
        split: train
  - config_name: sample_k20
    data_files:
      - path:
          - sample_k20.jsonl.zst
        split: train
  - config_name: sample_k200
    data_files:
      - path:
          - sample_k200.jsonl.zst
        split: train
  - config_name: sample_k2000
    data_files:
      - path:
          - sample_k2000.jsonl.zst
        split: train
  - config_name: sample_k20000
    data_files:
      - path:
          - sample_k20000.jsonl.zst
        split: train
  - config_name: sample_k50
    data_files:
      - path:
          - sample_k50.jsonl.zst
        split: train
  - config_name: sample_k500
    data_files:
      - path:
          - sample_k500.jsonl.zst
        split: train
  - config_name: sample_k5000
    data_files:
      - path:
          - sample_k5000.jsonl.zst
        split: train
  - config_name: sample_k50000
    data_files:
      - path:
          - sample_k50000.jsonl.zst
        split: train

Low Readability Text Dataset

This dataset consists of high-complexity English web text with an estimated readability at or above the U.S. Grade 12 level. The content typically features advanced, highly technical prose or verbose syntactical structures, making it well-suited for researching complex language understanding and automation.

Primary Use Cases

Text Simplification: Training and evaluating models to translate complex text into plain English.
Information Extraction (IE): Benchmarking NLP systems on dense, complex text structures that general human readers find difficult to parse.
Readability Assessment: Training classifiers to recognize specialized, collegiate, or advanced reading levels.

Dataset Creation & Methodology

1. Extraction & Chunking

Text was ingested from major web corpuses and broken into chunks of 1024 GPT-4 tokens, featuring a 10-token overlap between consecutive segments to maintain contextual continuity.
50,000 chunks per source dataset were initially isolated where the GIST-small-readability classifier scored the text at ≥ 12 (equivalent to U.S. high school senior/university freshman level or higher).

2. Quality Filtering

The initial pool was filtered using the agentlans/GIST-all-MiniLM-L6-v2-quality-v3 model. Only chunks with a quality score > 1.0 were retained.
In addition, Markov chain-generated text were filtered out using agentlans/GIST-small-markov-slop-detector.

3. Clustering

Post-filtering, data reduction and organization were performed via Agglomerative Clustering utilizing MongoDB/mdbr-leaf-mt embeddings to group similar semantic contexts.

Data Splits & Source Composition

The current primary split is sample_k100000. The distribution of source rows is outlined below:

Source	Rows
openbmb/Ultra-FineWeb en	28842
HuggingFaceFW/fineweb-edu	25499
epfml/FineWeb-HQ	20403
Zyphra/Zyda-2	19156
EssentialAI/essential-web-v1.0	6092
allenai/dolma3_pool *	8

Low retention due to high prevalence of spam

Dataset Structure

Features

text (String): The 1024-token text block.
grade (Float): The assigned reading grade level (minimum 12.0).
source (String): The originating dataset identifier.

Example Row

{
  "text": "the patents , Galium-Arsenide 935 nm 730 nm?270-275nm? 310-320nm and 760nm (H2O) 230-240nm (N2) 155-160nm (o2) Water vapor-nitrogen absorption at CO(2) laser frequencies. Peterson JC, Thomas ME, Nordstrom RJ, Damon EK, Long RK. \"...a series of pressure-broadened water vapor absorption measurements at 27 CO(2) laser frequencies between 935 cm(-1) and 1082 cm(-1)\" D20 (Deterium heavy water) - shift H2O from 760nm to 1000nm Balmer series or Balmer lines in atomic physics, is the designation of one of a set of six different named series describing the spectral line emissions of the hydrogen atom...",
  "grade": 12.0,
  "source": "Zyphra/Zyda-2"
}

Limitations & Considerations

⚠️ Usage Warnings:

Boundary Fragmentation: Because the dataset relies on rigid token-based windowing (1024 tokens), text blocks may start or end mid-sentence or mid-paragraph.
Domain Generality: The texts represent a broad, general-purpose web crawl. While complex, the text does not strictly represent deep, peer-reviewed domains like academic law or professional medicine.
Web Noise & Accuracy: The corpus stems from unverified internet pages. It contains messy syntax, optical character recognition/scraping artifacts, and may include scientifically or historically inaccurate claims.

Licence

This dataset is made available under the Open Data Commons Attribution License (ODC-By v1.0).