Datasets:

zID4si
/

CLASSLA_web_sl_2_enriched

dataset_info:
  - config_name: default
    features:
      - name: id
        dtype: string
      - name: title
        dtype: string
      - name: url
        dtype: string
      - name: domain
        dtype: string
      - name: tld
        dtype: string
      - name: lang
        dtype: string
      - name: crawl_year
        dtype: int64
      - name: genre
        dtype: string
      - name: topic
        dtype: string
      - name: text
        dtype: string
      - name: eduscore
        dtype: float32
      - name: token_count
        dtype: int64
      - name: web_reg
        list: string
      - name: nvidia_domain
        dtype: string
      - name: minhash_sig
        list: int64
      - name: cluster_id
        dtype: int64
      - name: cluster_size
        dtype: int64
    splits:
      - name: train
        num_bytes: 21596319171
        num_examples: 4790039
    download_size: 13654355359
    dataset_size: 21596319171
  - config_name: filtered
    features:
      - name: id
        dtype: string
      - name: title
        dtype: string
      - name: url
        dtype: string
      - name: domain
        dtype: string
      - name: tld
        dtype: string
      - name: lang
        dtype: string
      - name: crawl_year
        dtype: int64
      - name: genre
        dtype: string
      - name: topic
        dtype: string
      - name: text
        dtype: string
      - name: eduscore
        dtype: float32
      - name: token_count
        dtype: int64
      - name: web_reg
        list: string
      - name: nvidia_domain
        dtype: string
      - name: minhash_sig
        list: int64
      - name: cluster_id
        dtype: int64
      - name: cluster_size
        dtype: int64
    splits:
      - name: train
        num_bytes: 17766245404
        num_examples: 3940533
    download_size: 12216020767
    dataset_size: 17766245404
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
  - config_name: filtered
    data_files:
      - split: train
        path: filtered/train-*
license: cc-by-sa-4.0
language:
  - sl
tags:
  - web-crawl
  - classla
pretty_name: CLASSLA-web-sl-2 Enriched & Filtered

CLASSLA-web-sl-2 Enriched

This repository contains an enriched version of the Slovenian web corpus CLASSLA-web.sl.2.0. The dataset has been annotated with extensive metadata regarding educational value, domain classification, and web registers.

It provides two configurations:

default: The full, raw enriched dataset (4.79M documents).
filtered: A high-quality subset optimized for LLM Continued Pre-training (CPT), filtered based on educational score and deduplicated.

Dataset Configurations

1. Default (default)

The complete dataset contains all documents from the crawl, enriched with metadata columns (eduscore, nvidia_domain, web_reg, cluster_id).

Documents: 4,790,039
Tokens: ~3.60 Billion

2. Filtered (filtered)

A strategic subset designed for training high-quality Language Models.

Filtration Logic:
- Quality Cutoff: Documents with eduscore < 1.6 are removed. This effectively filters out machine-generated spam, SEO boilerplate, and low-quality promotional content while preserving lyrical content, news, and forums.
- Deduplication (Cluster Flattening): For every MinHash cluster (cluster_size > 1), only the single document with the highest eduscore is retained.
Documents: ~3.9M (Estimated)
Tokens: ~3.33 Billion (92.5% of original volume)
Use Case: Recommended for LLM Continued Pre-training (CPT).

Data Fields

Standard Fields

id (string): Unique identifier of the document (e.g., CLASSLA-web.2.0.sl.1).
text (string): The main content of the web page.
title (string): The title of the web page.
url (string): The original URL.
domain (string): The domain name (e.g., rtvslo.si).
genre (string): The genre label - 10 values (X-GENRE classifier).
topic (string): The topic label - 18 values (IPTC news topic classifier).
crawl_year (int64): Year of the crawl (2024).
token_count (int64): Number of tokens in the text (calculated using the Zlatorog MoE tokenizer).

Enriched Metadata Fields

eduscore (float)

A quality score ranging from 0.0 to 5.0, predicting the educational value of the content.

Model: zID4si/slovenian-edu-classifier-slovlo
Interpretation: See model card

nvidia_domain (string)

The broad topic classification of the document.

Model: nvidia/domain-classifier
Values: News, Science, Health, Politics, Finance, Sports, Travel, etc.

web_reg (string or list)

The functional text variety (register) of the document.

Model: TurkuNLP/web-register-classification-multilingual
Mapping Table:

Code	Description	Code	Description
MT	Machine translated / generated	re	Recipe
ne	News report	en	Encyclopedia article
na	Narrative	ra	Research article
nb	Narrative blog	dtp	Description of a thing/person
sr	Sports report	fi	FAQ
op	Opinion	lt	Legal terms and conditions
ob	Opinion blog	rv	Review
ed	Editorial / News & opinion blog	rs	Religious blog / sermon
hi	How-to / Instructions	av	Advice
in	Informational description	ip	Informational persuasion
id	Interactive discussion (Forum)	ds	Description with intent to sell
ly	Lyrical (Poetry/Song)	sp	Spoken (Interview transcripts)

Deduplication Fields

minhash_sig (list[int]): A list of 128 integers representing the MinHash signature of the text, used for Locality Sensitive Hashing (LSH).
cluster_id (int64): The identifier of the cluster this document belongs to.
- If cluster_size == 1: The document is unique.
- If cluster_size > 1: The document is a near-duplicate of others with the same cluster_id.
cluster_size (int64): The total number of documents found in this specific cluster.

Usage

Loading the Filtered Dataset (Recommended)


from datasets import load_dataset

# Loads the CPT-optimized version (Eduscore >= 1.6 + Deduplicated)
dataset = load_dataset("zID4si/CLASSLA_web_sl_2_enriched", "filtered", split="train")

print(dataset)

Loading the Raw Enriched Dataset


# Loads the full dataset including spam and duplicates
dataset = load_dataset("zID4si/CLASSLA_web_sl_2_enriched", "default", split="train")

Source & Citation

The underlying text data is derived from the CLASSLA-web 2.0 corpus (sl subcorpus). Additional information: https://clarinsi.github.io/classla-web/

Kuzman Pungeršek, Taja; Rupnik, Peter and Ljubešić, Nikola, 2026, South Slavic web corpus collection CLASSLA-web 2.0, Slovenian language resource repository CLARIN.SI, ISSN 2820-4042, http://hdl.handle.net/11356/2079.

If you use the enriched metadata or the filtered subset, please also acknowledge this repository and the classification models used.

Author

Tomaž Savodnik, Zavod za informacijsko družbo (zID)

The dataset was created as part of research into the quality and diversity of Slovenian online corpora.