README.md

metadata

language:
  - fr
  - de
task_categories:
  - token-classification
tags:
  - historical-newspapers
  - named-entity-recognition
  - impresso
pretty_name: Impresso Media Sources Dataset
license: other

Impresso Media Sources Dataset

Curated token-classification data for news-agency and radio-station mentions in Impresso historical newspaper text.

The v0.1 data is derived from the legacy French/German HIPE-style news-agency annotations, converted to JSONL, manually reviewed against the current model's dev/test disagreements, and updated according to annotation guidelines v2.0. The current guidelines annotate every explicit canonical media-source organization mention, not only source-attribution uses.

Files

data/train.jsonl
data/validation.jsonl
data/test.jsonl
label_map.json
dataset_summary.json
audit/curation_summary.json
audit/curation_changes.jsonl
audit/curation_changes_tags.tsv

Each JSONL row represents one document/article. The published data/*.jsonl files intentionally use a compact training schema rather than the full converted HIPE payload, so they are easy to load with datasets and inspect in the Hub viewer:

• id, language, newspaper, date, year, document_id
• text
• tokens
• token_start_offsets, token_end_offsets
• token_labels, token_label_ids
• entities
• quality_flags
• legacy

The canonical annotations are the BIO labels in token_labels plus the resolved span records in entities. The row-level legacy object is only for tracing back to the converted HIPE source and may contain source_format, source_file, and the original news_agency_as_source values. The latter is historical provenance from the thesis-era annotation semantics and should not be treated as the current target annotation.

Important metadata fields:

• newspaper: source newspaper/media identifier from the original annotation metadata, for example DTT.
• legacy.source_format: original annotation format, for example hipe-tsv.
• legacy.source_file: original converted annotation file, for example data/annotated_data/de/newsagency-data-dev-de.tsv.
• legacy.news_agency_as_source: document-level source-attribution QIDs from the thesis-era HIPE metadata. This is provenance only; the current gold annotation is in token_labels and entities.
• quality_flags: non-fatal import or curation warnings. has_forbidden_legacy_labels means the original row contained labels excluded by the current label policy, such as unk, unresolved ag, or pers.ind.articleauthor; these labels were removed from the trainable annotation.

Fields intentionally excluded from the public training rows:

• token_nel, token_ocr, token_render, token_segment_ids: token-level HIPE side channels used for conversion/debugging, not model training.
• segments: segment and IIIF metadata; useful for traceability, too large for the primary training table.
• sentences: derived sentence spans; redundant for current token-window training.
• entities[].label_original and entities[].status: legacy normalization/audit fields; the current label is entities[].label.

audit/curation_changes_tags.tsv is a compact CoNLL-like review file with TOKEN, BEFORE_NERTAG, and AFTER_NERTAG columns for the manually reviewed changes. It is intended for human audit, not model training.

Loading

from datasets import load_dataset

dataset = load_dataset(
    "impresso-project/impresso-mediaagencies-ner-dataset",
    data_files={
        "train": "data/train.jsonl",
        "validation": "data/validation.jsonl",
        "test": "data/test.jsonl",
    },
)

Label Policy

Trainable labels use two namespaces:

• org.ent.pressagency.<canonical_id>
• org.ent.radiostation.<canonical_id>

Forbidden legacy labels such as unk, unresolved bare ag, org.ent.pressagency.ag, and pers.ind.articleauthor are excluded from the trainable label space.

The source copy of this card lives in the workbench. Publishing scripts copy it into the Hugging Face dataset repository.

License

License metadata is set to other until the final redistribution terms for the converted annotations and Impresso text snippets are confirmed.