Hugging Face's logo

Datasets:
agentlans
/
high-quality-text

Tasks:
Text Generation
Text Classification
Modalities:
Text
Formats:
json
Languages:
English
Size:
100K - 1M
Tags:
pretraining
language modelling
Libraries:
Datasets
pandas
License:
Dataset card Data Studio Files
xet
Community
1
high-quality-text / README.md
agentlans's picture
agentlans
Update README.md
0f76b87 verified
preview code
|
raw
history blame contribute delete
3.62 kB
metadata
configs:
  - config_name: all
    data_files:
      - path:
          - all.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
    data_files:
      - path:
          - sample_k100000.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_k200000
    data_files:
      - path:
          - sample_k200000.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
license: odc-by
task_categories:
  - text-generation
  - text-classification
language:
  - en
tags:
  - pretraining
  - language modelling

High Quality Text Dataset

A curated collection of English-language texts for AI training and research.

Sources

Each dataset was processed as follows:

  1. Split into approximately 2 000-token chunks using the LLaMA 3.1 tokenizer.
  2. Cleaned by normalizing spaces, punctuation, and characters, and replacing emails and phone numbers with placeholders.
  3. Scored using the agentlans/GIST-all-MiniLM-L6-v2-quality-v3 classifier:
    • Only chunks with a quality score greater than 1 were included.
  4. Removed exact duplicates.

After filtering, 100 000 chunks per source were included in the final dataset.

Clustering

Agglomerative clustering was applied using embeddings from the Snowflake/snowflake-arctic-embed-xs model at multiple cluster counts: 100, 200, 500, 1 000, 2 000, 5 000, 10 000, 20 000, 50 000, 100 000, and 200 000 clusters, enabling flexible dataset configurations.

Example Entry 

{
  "text": "Dr. Louise Glew has been appointed the Global Lead Scientist for WWF's Global Science Team. Louise's research focuses on understanding the social and ecological impacts of conservation interventions [...]",
  "quality": 2.0699,
  "source": "openbmb/Ultra-FineWeb"
}

Limitations

  • Primarily focuses on academic, educational, and pedagogical content intended for a general audience.
  • May include outdated, unreliable, or controversial information (such as self-published material, pseudoscience, or conspiracy theories).
  • Quality scores evaluate syntax and tone, but do not guarantee factual accuracy.
  • Occasional repetition may occur (for example, dictionary entries or geographic distance calculations).
  • Entries might be interrupted mid-word or mid-sentence.

Licence

Provided under the Open Data Commons Attribution License (ODC-BY).