task_categories:
- feature-extraction
pretty_name: HPLT2-embeddings
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: ekk_Latn
data_files:
- split: train
path: ekk_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-embeddings
Dataset summary
HPLT2-embeddings is an extension of the HPLT2 dataset, annotated with document-level Snowflake's Arctic-embed-m-v2.0 embeddings for 35 languages, making the dataset useful for a variety of tasks, including document clustering, filtering, and other multilingual research.
Snowflake-arctic-embed-m-v2.0 has a sequence length limit of 8192 tokens, each document's embeddings are obtained by using the CLS token to embed each document.
The embeddings were computed as part of our 🦊 JQL: Judging Quality across Languages project and will be the basis for an upcoming high-quality subset of HPLT2. We believe that they can be useful for other multilingual research and applications.
For more details, see our paper Judging Quality Across Languages: A Multilingual Approach to Pretraining Data Filtering with Language Models.
Usage
You can load the dataset in Python using e.g.pandas:
import h5py
import pandas as pd
# Path to your .h5 file
file_path = "000_001_00000.h5" # <-- Replace with your actual file path
# Open the HDF5 file and load data
with h5py.File(file_path, "r") as f:
# Load the embeddings and document IDs from the "train" group
embeddings = f["train/embeddings"][:]
document_ids = f["train/document_id"][:]
# Convert document IDs from bytes (if needed)
if isinstance(document_ids[0], bytes):
document_ids = [doc_id.decode("utf-8") for doc_id in document_ids]
# Optionally: create a DataFrame (only if embeddings aren't too large for RAM)
df = pd.DataFrame(embeddings)
df.insert(0, "document_id", document_ids) # Add document_id as the first column
# Preview the DataFrame
print(df.head())
print(f"Loaded {len(df)} rows with shape {embeddings.shape[1]}-dimensional embeddings.")
Origin of the Dataset
This dataset, derived from HPLT2, includes web content collected from 2013 to 2024. 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}
}