| --- |
| 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 |
|
|
| ```text |
| data/train.jsonl |
| data/validation.jsonl |
| data/test.jsonl |
| label_map.json |
| DATASET_STATISTICS.md |
| ``` |
|
|
| `DATASET_STATISTICS.md` is a generated, human-readable release report with split sizes, token and mention totals, language and entity-family distributions, date and newspaper coverage, per-label frequencies, and the train/validation/test document-overlap check. |
|
|
| 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` |
| - `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` and `source_file`. |
|
|
| 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`. |
| - `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. |
| - `entities[].wikidata_url`: canonical Wikidata URL when available. Raw NEL/QID provenance is not part of the public training contract. |
| - `entities[].ocr_correction`: optional object present only when OCR/transcript evidence corrected the visible entity surface. |
| - Public `entities[]` do not include synthetic entity IDs. If a stable entity reference is needed, derive it from row `id`, character offsets, and `label`. |
|
|
| 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. |
| - `token_label_ids`: integer labels are derived from `token_labels` and `label_map.json` by training code when needed. |
| - `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. |
| - `legacy.news_agency_as_source`: thesis-era document-level source-attribution provenance. It mixes QIDs with sentinels such as `_`, `unk`, and `NIL`, and is not part of the current annotation target. |
| - `entities[].entity_id`, `entities[].nel`, `entities[].normalized_surface`, `entities[].has_ocr_correction`, `entities[].max_ocr_levenshtein`, `entities[].label_original`, and `entities[].status`: legacy normalization/linking/OCR/audit fields. The current label is `entities[].label`; current entity links use `entities[].wikidata_url`; compact OCR corrections use `entities[].ocr_correction`. |
|
|
| Workbench/audit artifacts such as curation operation files, TSV materializations, tokenization migration reports, and model-specific quality diagnostics are intentionally not part of the Hugging Face dataset payload. |
|
|
| ## Loading |
|
|
| ```python |
| 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. |
|
|
