---
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_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_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
- feature-extraction
language:
- en
---
# High Quality Text (Longer) Dataset

This is [agentlans/high-quality-text](https://huggingface.co/datasets/agentlans/high-quality-text)
except that only chunks between 1750 and 2250 Meta Llama 3.1 tokens were kept.

The chunks were embedded using [MongoDB/mdbr-leaf-mt](https://huggingface.co/MongoDB/mdbr-leaf-mt)
and hierarchically clustered.