metadata
language:
- en
dataset_info:
features:
- name: text
dtype: string
- name: id
dtype: string
- name: metadata
struct:
- name: date
dtype: timestamp[us]
- name: dump
dtype: string
- name: file_path
dtype: string
- name: int_score
dtype: int64
- name: language
dtype: string
- name: language_score
dtype: float64
- name: score
dtype: float64
- name: token_count
dtype: int64
- name: url
dtype: string
splits:
- name: train
num_bytes: 5292938151.266562
num_examples: 999245
download_size: 2716629909
dataset_size: 5292938151.266562
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
Qwark Corpus
1.3B+ high quality tokens from the internet, based on HuggingFaceTB/smollm-corpus's fineweb-edu-dedup subset as well as FineMath-4+.
Filtering process:
| Step | Description | Rows |
|---|---|---|
| 1. Stream dataset until 600K samples have been selected from SmolLM Corpus | Keep only items with score >= 3.5 | 600,000 |
| 2. Remove items with length > 50,000 | Filter items exceeding 50,000 characters in length | 597,142 |
| 3. Combine with a selection of 4,000 TED transcripts | Add educational TED talk transcripts to the dataset | 601,147 |
| 4. Stream 400K samples from FineMath-4+ | Keep only items with score >= 4.0 | 1,001,147 |
| 5. Remove items with length > 50,000 | Filter items exceeding 50,000 characters in length | 999,245 |