| --- |
| 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](https://huggingface.co/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](https://huggingface.co/agentlans/GIST-small-markov-slop-detector). |
|
|
| ### 3. Clustering |
|
|
| * Post-filtering, data reduction and organization were performed via **Agglomerative Clustering** utilizing [MongoDB/mdbr-leaf-mt](https://huggingface.co/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](https://huggingface.co/datasets/openbmb/Ultra-FineWeb) | 28842 | |
| | [HuggingFaceFW/fineweb-edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu) | 25499 | |
| | [epfml/FineWeb-HQ](https://huggingface.co/datasets/epfml/FineWeb-HQ) | 20403 | |
| | [Zyphra/Zyda-2](https://huggingface.co/datasets/Zyphra/Zyda-2) | 19156 | |
| | [EssentialAI/essential-web-v1.0](https://huggingface.co/datasets/EssentialAI/essential-web-v1.0) | 6092 | |
| | [allenai/dolma3_pool](https://huggingface.co/datasets/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 |
| |
| ```json |
| { |
| "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)**. |
| |
