Datasets:
task_categories:
- text-ranking
pretty_name: HPLT2-JQL-Education
size_categories:
- n>1T
language:
- sq
- bg
- ca
- cs
- da
- de
- es
- et
- el
- eu
- fi
- fr
- gl
- ga
- hr
- hu
- hy
- is
- it
- lv
- lt
- mk
- nl
- pl
- pt
- ro
- sl
- sk
- sr
- tr
- sv
- nb
- nn
configs:
- config_name: als_Latn
data_files:
- split: train
path: als_Latn/*
- config_name: bul_Cyrl
data_files:
- split: train
path: bul_Cyrl/*
- config_name: cat_Latn
data_files:
- split: train
path: cat_Latn/*
- config_name: ces_Latn
data_files:
- split: train
path: ces_Latn/*
- config_name: dan_Latn
data_files:
- split: train
path: dan_Latn/*
- config_name: deu_Latn
data_files:
- split: train
path: deu_Latn/*
- config_name: est_Latn
data_files:
- split: train
path: est_Latn/*
- config_name: ell_Grek
data_files:
- split: train
path: ell_Grek/*
- config_name: eus_Latn
data_files:
- split: train
path: eus_Latn/*
- config_name: fin_Latn
data_files:
- split: train
path: fin_Latn/*
- config_name: fra_Latn
data_files:
- split: train
path: fra_Latn/*
- config_name: gle_Latn
data_files:
- split: train
path: gle_Latn/*
- config_name: glg_Latn
data_files:
- split: train
path: glg_Latn/*
- config_name: hrv_Latn
data_files:
- split: train
path: hrv_Latn/*
- config_name: hun_Latn
data_files:
- split: train
path: hun_Latn/*
- config_name: hye_Armn
data_files:
- split: train
path: hye_Armn/*
- config_name: isl_Latn
data_files:
- split: train
path: isl_Latn/*
- config_name: ita_Latn
data_files:
- split: train
path: ita_Latn/*
- config_name: lit_Latn
data_files:
- split: train
path: lit_Latn/*
- config_name: lvs_Latn
data_files:
- split: train
path: lvs_Latn/*
- config_name: mkd_Cyrl
data_files:
- split: train
path: mkd_Cyrl/*
- config_name: nld_Latn
data_files:
- split: train
path: nld_Latn/*
- config_name: nno_Latn
data_files:
- split: train
path: nno_Latn/*
- config_name: nob_Latn
data_files:
- split: train
path: nob_Latn/*
- config_name: pol_Latn
data_files:
- split: train
path: pol_Latn/*
- config_name: por_Latn
data_files:
- split: train
path: por_Latn/*
- config_name: ron_Latn
data_files:
- split: train
path: ron_Latn/*
- config_name: slk_Latn
data_files:
- split: train
path: slk_Latn/*
- config_name: slv_Latn
data_files:
- split: train
path: slv_Latn/*
- config_name: spa_Latn
data_files:
- split: train
path: spa_Latn/*
- config_name: srp_Cyrl
data_files:
- split: train
path: srp_Cyrl/*
- config_name: swe_Latn
data_files:
- split: train
path: swe_Latn/*
- config_name: tur_Latn
data_files:
- split: train
path: tur_Latn/*
- config_name: ukr_Cyrl
data_files:
- split: train
path: ukr_Cyrl/*
HPLT2-Edu-scores
Dataset summary
HPLT2-JQL-Education is a model-annotated language subset of HPLT2, spanning 35 languages. Our model-annotations allow for a filtering that achieves higher-quality training outcomes without excessively aggressive data reduction. The original FW2 heuristic filtering method serves as our baseline, providing reference points for both the volume of retained tokens and downstream model performance. For example, in the Spanish language case, applying the 0.6 threshold retains over 9% more tokens than FW2 train while still surpassing its quality .
HPLT2-Edu-scores was created based on scores assigned by a deep learning classifier trained to identify educational samples using Snowflake's Arctic-embed-m-v2.0 embeddings.
For all training ablations, we used dense decoder-only models with 2 billion parameters, following the LLaMA architecture. For more details, see our paper https://arxiv.org/abs/2505.22232.
The approach as described in the paper is easy to extend to other languages as well, and we might consider adding new languages to an upcoming version of the present dataset.
We also separately release the computed general-purpose embedding vectors for the the full sets of the original HPLT2 dataset, in the respective languages, as they can be useful for other applications beyond quality filtering: HPLT2-embeddings.
Dataset Structure
Data Fields
Each data entry includes:
score_Gemma_Snowflake: Quality score obtained by the Gemma-based Snowflake classifierscore_Llama_Snowflake: Quality score obtained by the Llama-based Snowflake classifierscore_Mistral_Snowflake: Quality score obtained by the Mistral-based Snowflake classifiersource_filename: Source filename of the original file.
Data Instance
{
"id": "0",
"file_path": "/leonardo_scratch/large/userexternal/mfromm00/data/raw_data/HPLT2/output/embeddings/als_Latn/als_Latn/000_000_00000.jsonl.h5",
"document_id": "29d82196d55803ab9c792e45b59919bf_0",
"source_filename": "als_Latn/als_Latn/000_000_00000.jsonl.h5",
"score_Gemma_Snowflake": 0.330078125,
"score_Llama_Snowflake": -0.34765625,
"score_Mistral_Snowflake": -0.390625
}
Origin of the Dataset
This dataset, derived from HPLT2, includes web content collected from 2012 to 2023. As HPLT2 is sourced from the broader internet, it may contain some personally identifiable information (PII), despite efforts to anonymize email addresses and public IP addresses during processing.
Considerations for Data Usage
For information on social impact, potential biases, and known limitations, please refer to the HPLT2 documentation.
Citation information
If you use this dataset in your research or applications, please use the following citation:
@article{ali2025judging,
title = {Judging Quality Across Languages: A Multilingual Approach to Pretraining Data Filtering with Language Models},
author = {
Mehdi Ali,
Manuel Brack,
Max Lübbering,
Elias Wendt,
Abbas Goher Khan,
Richard Rutmann,
Alex Jude,
Maurice Kraus,
Alexander Arno Weber,
Felix Stollenwerk,
David Kaczér,
Florian Mai,
Lucie Flek,
Rafet Sifa,
Nicolas Flores-Herr,
Joachim Köhler,
Patrick Schramowski,
Michael Fromm,
Kristian Kersting
},
year = {2025},
journal = {arXiv preprint arXiv:2505:22232}
}