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Publication Year: 2026

51 to 60 of 1,365 Results

navez_spacy_off_the_shelf_nlp_sm_results.csv Aug 25, 2026 - Replication Data for: From nineteenth-century letters to entities: "a NER pipeline for French correspondence and its methodological lessons" - Article for Digital Humanities Benelux Journal Comma Separated Values - 157.0 KB - MD5: 1128164075ad2b33e1902c44b764d000 Model predictions generated by the off-the-shelf fr_core_news_sm model using the complete spaCy NLP pipeline (Workflow 2). Contains predicted entities, spans, labels and corresponding gold-standard annotations for the held-out test set.
nervaluate_counters_overall.tab Aug 25, 2026 - Replication Data for: From nineteenth-century letters to entities: "a NER pipeline for French correspondence and its methodological lessons" - Article for Digital Humanities Benelux Journal Tabular Data - 1.8 KB - 14 Variables, 16 Observations - UNF:6:/3uciuaa/yLyO27c+p2Cyg== Aggregated Nervaluate statistics reporting the numbers of correct, incorrect, partial, missed and spurious entity predictions on the held-out test set under the different evaluation scenarios (Strict, Exact, Partial and Type) for all transformer-based models.
per_tag_test.tab Aug 25, 2026 - Replication Data for: From nineteenth-century letters to entities: "a NER pipeline for French correspondence and its methodological lessons" - Article for Digital Humanities Benelux Journal Tabular Data - 12.5 KB - 14 Variables, 128 Observations - UNF:6:YzGwFRM6nYl9P3yPhkiCEg== Evaluation results for each transformer-based model and each entity type (PER, LOC, ORG, GRP, ART, EXH and LETT) on the held-out test set under the Nervaluate scenarios. It enables detailed comparison of model performance across annotation categories.
project-237-at-2025-05-25-08-36-f874dc2a-2.json Aug 25, 2026 - Replication Data for: From nineteenth-century letters to entities: "a NER pipeline for French correspondence and its methodological lessons" - Article for Digital Humanities Benelux Journal JSON - 198.6 KB - MD5: 72da0345995240cc733071556d1a647e Part 1 - JSON export from Label Studio containing manually annotated nineteenth-century French correspondence from the Navez Project. Includes entity annotations, transcriptions and project metadata. Used as source data for preprocessing and model training.
project-238-at-2025-05-25-08-34-694e26ec-2.json Aug 25, 2026 - Replication Data for: From nineteenth-century letters to entities: "a NER pipeline for French correspondence and its methodological lessons" - Article for Digital Humanities Benelux Journal JSON - 575.1 KB - MD5: 526f06d4f9ce02a351f068df4162d62b Part 2 - JSON export from Label Studio containing manually annotated nineteenth-century French correspondence from the Navez Project. Includes entity annotations, transcriptions and project metadata. Used as source data for preprocessing and model training.
significance_tests.tab Aug 25, 2026 - Replication Data for: From nineteenth-century letters to entities: "a NER pipeline for French correspondence and its methodological lessons" - Article for Digital Humanities Benelux Journal Tabular Data - 559 B - 6 Variables, 6 Observations - UNF:6:9YJ3w4uTtbYmfNKxJTh1UA== Results of statistical significance tests comparing the performance of the evaluated Named Entity Recognition models. Used to assess whether observed performance differences are statistically meaningful.
split_entity_counts.csv Aug 25, 2026 - Replication Data for: From nineteenth-century letters to entities: "a NER pipeline for French correspondence and its methodological lessons" - Article for Digital Humanities Benelux Journal Comma Separated Values - 162 B - MD5: ae69733d2f22cb00b53e031a03f99e3f Summary of the number of annotated entities per entity type in the training, development and test datasets. Used to document the composition of the gold-standard corpus and the experimental data splits.
test_predictions_camembertav2.tab Aug 25, 2026 - Replication Data for: From nineteenth-century letters to entities: "a NER pipeline for French correspondence and its methodological lessons" - Article for Digital Humanities Benelux Journal Tabular Data - 96.0 KB - 3 Variables, 17 Observations - UNF:6:6PxO9ELAf/ENQQAY98ekCQ== Named Entity Recognition predictions generated by the fine-tuned CamemBERTav2 model on the held-out test set. Includes predicted entity spans, labels and gold-standard annotations for model evaluation.
test_predictions_camembert.tab Aug 25, 2026 - Replication Data for: From nineteenth-century letters to entities: "a NER pipeline for French correspondence and its methodological lessons" - Article for Digital Humanities Benelux Journal Tabular Data - 99.0 KB - 3 Variables, 17 Observations - UNF:6:wwYLN6EuL27q8HvnSckZ9A== Named Entity Recognition predictions generated by the fine-tuned CamemBERT model on the held-out test set. Includes predicted entity spans, labels and gold-standard annotations for model evaluation.
test_predictions_dalembert.tab Aug 25, 2026 - Replication Data for: From nineteenth-century letters to entities: "a NER pipeline for French correspondence and its methodological lessons" - Article for Digital Humanities Benelux Journal Tabular Data - 96.3 KB - 3 Variables, 17 Observations - UNF:6:QOnGSP2nZCOfl3Bdo7Wayw== Named Entity Recognition predictions generated by the fine-tuned D'AlemBERT model on the held-out test set. Includes predicted entity spans, labels and gold-standard annotations for model evaluation.

navez_spacy_off_the_shelf_nlp_sm_results.csv

Aug 25, 2026 - Replication Data for: From nineteenth-century letters to entities: "a NER pipeline for French correspondence and its methodological lessons" - Article for Digital Humanities Benelux Journal

Comma Separated Values - 157.0 KB -

Model predictions generated by the off-the-shelf fr_core_news_sm model using the complete spaCy NLP pipeline (Workflow 2). Contains predicted entities, spans, labels and corresponding gold-standard annotations for the held-out test set.

nervaluate_counters_overall.tab

Tabular Data - 1.8 KB - 14 Variables, 16 Observations -

Aggregated Nervaluate statistics reporting the numbers of correct, incorrect, partial, missed and spurious entity predictions on the held-out test set under the different evaluation scenarios (Strict, Exact, Partial and Type) for all transformer-based models.

per_tag_test.tab

Tabular Data - 12.5 KB - 14 Variables, 128 Observations -

Evaluation results for each transformer-based model and each entity type (PER, LOC, ORG, GRP, ART, EXH and LETT) on the held-out test set under the Nervaluate scenarios. It enables detailed comparison of model performance across annotation categories.

project-237-at-2025-05-25-08-36-f874dc2a-2.json

JSON - 198.6 KB -

Part 1 - JSON export from Label Studio containing manually annotated nineteenth-century French correspondence from the Navez Project. Includes entity annotations, transcriptions and project metadata. Used as source data for preprocessing and model training.

project-238-at-2025-05-25-08-34-694e26ec-2.json

JSON - 575.1 KB -

Part 2 - JSON export from Label Studio containing manually annotated nineteenth-century French correspondence from the Navez Project. Includes entity annotations, transcriptions and project metadata. Used as source data for preprocessing and model training.

significance_tests.tab

Tabular Data - 559 B - 6 Variables, 6 Observations -

Results of statistical significance tests comparing the performance of the evaluated Named Entity Recognition models. Used to assess whether observed performance differences are statistically meaningful.

split_entity_counts.csv

Comma Separated Values - 162 B -

Summary of the number of annotated entities per entity type in the training, development and test datasets. Used to document the composition of the gold-standard corpus and the experimental data splits.

test_predictions_camembertav2.tab

Tabular Data - 96.0 KB - 3 Variables, 17 Observations -

Named Entity Recognition predictions generated by the fine-tuned CamemBERTav2 model on the held-out test set. Includes predicted entity spans, labels and gold-standard annotations for model evaluation.

test_predictions_camembert.tab

Tabular Data - 99.0 KB - 3 Variables, 17 Observations -

Named Entity Recognition predictions generated by the fine-tuned CamemBERT model on the held-out test set. Includes predicted entity spans, labels and gold-standard annotations for model evaluation.

test_predictions_dalembert.tab

Tabular Data - 96.3 KB - 3 Variables, 17 Observations -

Named Entity Recognition predictions generated by the fine-tuned D'AlemBERT model on the held-out test set. Includes predicted entity spans, labels and gold-standard annotations for model evaluation.

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