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_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 except that only chunks between 1750 and 2250 Meta Llama 3.1 tokens were kept.
The chunks were embedded using MongoDB/mdbr-leaf-mt and hierarchically clustered.