Datasets:

LeFrenchNewsLab
/

frenchnews-7

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
name: string
source_split_articles: int64
num_articles: int64
num_publishers: int64
sha256: string
time_span: struct<start: timestamp[s], end: timestamp[s]>
  child 0, start: timestamp[s]
  child 1, end: timestamp[s]
splits: struct<train: int64, validation: int64, test: int64>
  child 0, train: int64
  child 1, validation: int64
  child 2, test: int64
split_seed: int64
label_distribution: struct<Société: int64, International: int64, Culture & Loisirs: int64, Politique: int64, Sport: in (... 54 chars omitted)
  child 0, Société: int64
  child 1, International: int64
  child 2, Culture & Loisirs: int64
  child 3, Politique: int64
  child 4, Sport: int64
  child 5, Économie: int64
  child 6, Sciences & Technologies: int64
annotation: struct<bucket_a_count: int64, bucket_a_pct: double, bucket_b_count: int64, bucket_b_pct: double, buc (... 125 chars omitted)
  child 0, bucket_a_count: int64
  child 1, bucket_a_pct: double
  child 2, bucket_b_count: int64
  child 3, bucket_b_pct: double
  child 4, bucket_a_method: string
  child 5, bucket_b_method: string
  child 6, bucket_b_iaa_kappa: double
  child 7, bucket_b_iaa_alpha: double
  child 8, bucket_b_iaa_n: int64
excluded_rows: struct<count: int64, reasons: struct<>>
  child 0, count: int64
  child 1, reasons: struct<>
label_mismatches: int64
rules: struct<culture: string, culture-et-savoir: string, culture-loisirs: string, arts-stars: string, arts (... 1433 chars omitted)
  child 0, culture: string
  child 1, culture-et-savoir: str
...
8, eureka: string
  child 39, sciences-nature: string
  child 40, societe: string
  child 41, sante: string
  child 42, environnement: string
  child 43, environnement-ecologie: string
  child 44, planete: string
  child 45, faits-divers: string
  child 46, faits_divers: string
  child 47, justice: string
  child 48, justice-faits-divers: string
  child 49, police-justice: string
  child 50, education: string
  child 51, campus: string
  child 52, meteo: string
  child 53, vie-quotidienne: string
  child 54, animaux: string
  child 55, feminisme: string
  child 56, religion: string
  child 57, immobilier: string
  child 58, vie-pro: string
  child 59, insolite: string
  child 60, sante-mentale: string
  child 61, sport: string
  child 62, sports: string
  child 63, rugby: string
  child 64, tennis: string
  child 65, cyclisme: string
  child 66, tour-de-france: string
  child 67, jo-paris-2024: string
  child 68, economie: string
  child 69, argent: string
  child 70, entreprises: string
  child 71, flash-eco: string
  child 72, conso-argent: string
  child 73, entrepreneurs: string
_description: string
_total_rules: int64
_counts_by_category: struct<Culture & Loisirs: int64, International: int64, Politique: int64, Sciences & Technologies: in (... 54 chars omitted)
  child 0, Culture & Loisirs: int64
  child 1, International: int64
  child 2, Politique: int64
  child 3, Sciences & Technologies: int64
  child 4, Société: int64
  child 5, Sport: int64
  child 6, Économie: int64
to
{'_description': Value('string'), '_total_rules': Value('int64'), 'rules': {'culture': Value('string'), 'culture-et-savoir': Value('string'), 'culture-loisirs': Value('string'), 'arts-stars': Value('string'), 'arts': Value('string'), 'cinema': Value('string'), 'livres': Value('string'), 'musique': Value('string'), 'gastronomie': Value('string'), 'voyages': Value('string'), 'voyage': Value('string'), 'art-de-vivre': Value('string'), 'festival-de-cannes': Value('string'), 'pop-culture': Value('string'), 'divertissement': Value('string'), 'sortir-paris': Value('string'), 'people': Value('string'), 'traditions-et-patrimoine': Value('string'), 'mode-design': Value('string'), 'boire-manger': Value('string'), 'international': Value('string'), 'monde': Value('string'), 'afrique': Value('string'), 'europe': Value('string'), 'ameriques': Value('string'), 'asie-pacifique': Value('string'), 'proche-orient': Value('string'), 'moyen-orient': Value('string'), 'elections-americaines': Value('string'), 'election-presidentielle-americaine': Value('string'), 'politique': Value('string'), 'elections': Value('string'), 'sciences': Value('string'), 'science': Value('string'), 'high-tech': Value('string'), 'high-tech-internet': Value('string'), 'tech-internet': Value('string'), 'tech-futurs': Value('string'), 'eureka': Value('string'), 'sciences-nature': Value('string'), 'societe': Value('string'), 'sante': Value('string'), 'environnement': Value('string'), 'environnement-ecologie': Value('string'), 'planete': Value('string'), 'faits-divers': Value('string'), 'faits_divers': Value('string'), 'justice': Value('string'), 'justice-faits-divers': Value('string'), 'police-justice': Value('string'), 'education': Value('string'), 'campus': Value('string'), 'meteo': Value('string'), 'vie-quotidienne': Value('string'), 'animaux': Value('string'), 'feminisme': Value('string'), 'religion': Value('string'), 'immobilier': Value('string'), 'vie-pro': Value('string'), 'insolite': Value('string'), 'sante-mentale': Value('string'), 'sport': Value('string'), 'sports': Value('string'), 'rugby': Value('string'), 'tennis': Value('string'), 'cyclisme': Value('string'), 'tour-de-france': Value('string'), 'jo-paris-2024': Value('string'), 'economie': Value('string'), 'argent': Value('string'), 'entreprises': Value('string'), 'flash-eco': Value('string'), 'conso-argent': Value('string'), 'entrepreneurs': Value('string')}, '_counts_by_category': {'Culture & Loisirs': Value('int64'), 'International': Value('int64'), 'Politique': Value('int64'), 'Sciences & Technologies': Value('int64'), 'Société': Value('int64'), 'Sport': Value('int64'), 'Économie': Value('int64')}}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 289, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 124, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              name: string
              source_split_articles: int64
              num_articles: int64
              num_publishers: int64
              sha256: string
              time_span: struct<start: timestamp[s], end: timestamp[s]>
                child 0, start: timestamp[s]
                child 1, end: timestamp[s]
              splits: struct<train: int64, validation: int64, test: int64>
                child 0, train: int64
                child 1, validation: int64
                child 2, test: int64
              split_seed: int64
              label_distribution: struct<Société: int64, International: int64, Culture & Loisirs: int64, Politique: int64, Sport: in (... 54 chars omitted)
                child 0, Société: int64
                child 1, International: int64
                child 2, Culture & Loisirs: int64
                child 3, Politique: int64
                child 4, Sport: int64
                child 5, Économie: int64
                child 6, Sciences & Technologies: int64
              annotation: struct<bucket_a_count: int64, bucket_a_pct: double, bucket_b_count: int64, bucket_b_pct: double, buc (... 125 chars omitted)
                child 0, bucket_a_count: int64
                child 1, bucket_a_pct: double
                child 2, bucket_b_count: int64
                child 3, bucket_b_pct: double
                child 4, bucket_a_method: string
                child 5, bucket_b_method: string
                child 6, bucket_b_iaa_kappa: double
                child 7, bucket_b_iaa_alpha: double
                child 8, bucket_b_iaa_n: int64
              excluded_rows: struct<count: int64, reasons: struct<>>
                child 0, count: int64
                child 1, reasons: struct<>
              label_mismatches: int64
              rules: struct<culture: string, culture-et-savoir: string, culture-loisirs: string, arts-stars: string, arts (... 1433 chars omitted)
                child 0, culture: string
                child 1, culture-et-savoir: str
              ...
              8, eureka: string
                child 39, sciences-nature: string
                child 40, societe: string
                child 41, sante: string
                child 42, environnement: string
                child 43, environnement-ecologie: string
                child 44, planete: string
                child 45, faits-divers: string
                child 46, faits_divers: string
                child 47, justice: string
                child 48, justice-faits-divers: string
                child 49, police-justice: string
                child 50, education: string
                child 51, campus: string
                child 52, meteo: string
                child 53, vie-quotidienne: string
                child 54, animaux: string
                child 55, feminisme: string
                child 56, religion: string
                child 57, immobilier: string
                child 58, vie-pro: string
                child 59, insolite: string
                child 60, sante-mentale: string
                child 61, sport: string
                child 62, sports: string
                child 63, rugby: string
                child 64, tennis: string
                child 65, cyclisme: string
                child 66, tour-de-france: string
                child 67, jo-paris-2024: string
                child 68, economie: string
                child 69, argent: string
                child 70, entreprises: string
                child 71, flash-eco: string
                child 72, conso-argent: string
                child 73, entrepreneurs: string
              _description: string
              _total_rules: int64
              _counts_by_category: struct<Culture & Loisirs: int64, International: int64, Politique: int64, Sciences & Technologies: in (... 54 chars omitted)
                child 0, Culture & Loisirs: int64
                child 1, International: int64
                child 2, Politique: int64
                child 3, Sciences & Technologies: int64
                child 4, Société: int64
                child 5, Sport: int64
                child 6, Économie: int64
              to
              {'_description': Value('string'), '_total_rules': Value('int64'), 'rules': {'culture': Value('string'), 'culture-et-savoir': Value('string'), 'culture-loisirs': Value('string'), 'arts-stars': Value('string'), 'arts': Value('string'), 'cinema': Value('string'), 'livres': Value('string'), 'musique': Value('string'), 'gastronomie': Value('string'), 'voyages': Value('string'), 'voyage': Value('string'), 'art-de-vivre': Value('string'), 'festival-de-cannes': Value('string'), 'pop-culture': Value('string'), 'divertissement': Value('string'), 'sortir-paris': Value('string'), 'people': Value('string'), 'traditions-et-patrimoine': Value('string'), 'mode-design': Value('string'), 'boire-manger': Value('string'), 'international': Value('string'), 'monde': Value('string'), 'afrique': Value('string'), 'europe': Value('string'), 'ameriques': Value('string'), 'asie-pacifique': Value('string'), 'proche-orient': Value('string'), 'moyen-orient': Value('string'), 'elections-americaines': Value('string'), 'election-presidentielle-americaine': Value('string'), 'politique': Value('string'), 'elections': Value('string'), 'sciences': Value('string'), 'science': Value('string'), 'high-tech': Value('string'), 'high-tech-internet': Value('string'), 'tech-internet': Value('string'), 'tech-futurs': Value('string'), 'eureka': Value('string'), 'sciences-nature': Value('string'), 'societe': Value('string'), 'sante': Value('string'), 'environnement': Value('string'), 'environnement-ecologie': Value('string'), 'planete': Value('string'), 'faits-divers': Value('string'), 'faits_divers': Value('string'), 'justice': Value('string'), 'justice-faits-divers': Value('string'), 'police-justice': Value('string'), 'education': Value('string'), 'campus': Value('string'), 'meteo': Value('string'), 'vie-quotidienne': Value('string'), 'animaux': Value('string'), 'feminisme': Value('string'), 'religion': Value('string'), 'immobilier': Value('string'), 'vie-pro': Value('string'), 'insolite': Value('string'), 'sante-mentale': Value('string'), 'sport': Value('string'), 'sports': Value('string'), 'rugby': Value('string'), 'tennis': Value('string'), 'cyclisme': Value('string'), 'tour-de-france': Value('string'), 'jo-paris-2024': Value('string'), 'economie': Value('string'), 'argent': Value('string'), 'entreprises': Value('string'), 'flash-eco': Value('string'), 'conso-argent': Value('string'), 'entrepreneurs': Value('string')}, '_counts_by_category': {'Culture & Loisirs': Value('int64'), 'International': Value('int64'), 'Politique': Value('int64'), 'Sciences & Technologies': Value('int64'), 'Société': Value('int64'), 'Sport': Value('int64'), 'Économie': Value('int64')}}
              because column names don't match

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FrenchNews-7

FrenchNews-7 is a French news topic-classification benchmark. This public release is a manifest-only artifact for reproducibility and benchmarking: it exposes URL-level and metadata-level information for 87,637 labeled articles from 13 publishers without redistributing article text.

What This Release Contains

This repository includes:

A manifest CSV with article identifiers, source URLs, publishers, labels, split assignments, dates, and years
Taxonomy files describing the seven-label classification scheme
Dataset statistics and publisher metadata
Lightweight reconstruction utilities for users who are legally authorized to refetch source pages themselves

This repository does not include:

Article body text
Headlines
Snippets
Summaries
Embeddings
Any other verbatim textual content from the source publishers

Intended Use

FrenchNews-7 is intended for research and benchmarking in French news topic classification, especially cross-publisher evaluation under a harmonized 7-label taxonomy. It is not intended as a universal topic ontology for all French text.

Recommended Model

The recommended deployment artifact for this dataset is the headline + body CamemBERT-base classifier released here:

LeFrenchNewsLab/camembert-base-frenchnews7

Dataset Summary

Dataset name: FrenchNews-7
Language: French
Task: 7-way topic classification
Articles: 87,637
Publishers: 13
Time span: 2005-04-14 to 2026-03-12
Public release format: manifest-only

Splits

Split	Articles
train	61,345
validation	13,146
test	13,146

Label Distribution

Label	Articles
Société	19,324
International	18,642
Culture & Loisirs	18,350
Politique	10,546
Sport	8,404
Économie	8,174
Sciences & Technologies	4,197

Publishers

20 Minutes
JDD
L'Express
L'Humanité
La Croix
Le Figaro
Le HuffPost
Le Monde
Le Parisien
Le Point
Ouest-France
Slate.fr
TF1 INFO

Manifest Schema

Main manifest file:

data/frenchnews7_manifest.csv

Required columns:

id
url
publisher
label
split
date
year

No text-bearing columns are included. In particular, the release excludes headline, body, snippet, and summary fields.

Example rows:

id,url,publisher,label,split,date,year
fn7-1089,https://www.lemonde.fr/economie/article/2025/06/23/...,Le Monde,Économie,validation,2025-06-23,2025
fn7-2969,https://www.lemonde.fr/idees/article/2025/06/13/...,Le Monde,Sciences & Technologies,test,2025-06-13,2025

Labels reflect the harmonized benchmark taxonomy and may not correspond literally to the URL path slug in all cases.

Label Taxonomy

Société: domestic social affairs, human interest, crime, health, education, and civil society
Culture & Loisirs: arts, cinema, music, television, books, leisure, and lifestyle
International: foreign affairs, geopolitics, and global events outside France
Politique: domestic politics, government, elections, institutions, and legislative affairs
Sport: all sports coverage regardless of discipline
Économie: business, finance, macroeconomics, markets, and corporate news
Sciences & Technologies: scientific research, technology, digital innovation, and environment-science topics

Limitations

The release is manifest-only and does not redistribute article text.
Labels reflect a harmonized cross-publisher editorial taxonomy rather than arbitrary fine-grained semantic annotation.
Reconstruction success may vary over time as publisher pages change or disappear.
Performance and coverage may vary across outlets, years, and topic distributions.

Legal Note

This repository is designed to support reproducibility without redistributing copyrighted news content. Users are responsible for ensuring that any downstream fetching, storage, processing, or redistribution of source material complies with applicable law, publisher terms, robots directives, and institutional policy.

The CC-BY-4.0 license applies only to the original manifest structure, labels, taxonomy definitions, and repository-authored materials released by the authors, and does not apply to the linked publisher content.

Reconstruction Note

The reconstruction/ folder provides lightweight starter utilities to refetch pages from the original URLs under the user's own legal and operational framework. These utilities are intentionally conservative and do not attempt aggressive scraping or text extraction. They are provided solely as a convenience for authorized users.

Citation

If you use FrenchNews-7 or the accompanying classifier, please cite the associated paper.

@misc{sobhy2026frenchnews7,
  title        = {FrenchNews-7: Benchmarking Cross-Publisher French News Topic Classification},
  author       = {Amr Sobhy},
  year         = {2026},
  note         = {Under review},
}

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Total file size:

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Models trained or fine-tuned on LeFrenchNewsLab/frenchnews-7

LeFrenchNewsLab/camembert-base-frenchnews7

Text Classification • 0.1B • Updated 26 days ago • 42