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81 to 90 of 3,929 Results

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.
test_predictions_europeana.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 - 96.1 KB - 3 Variables, 17 Observations - UNF:6:47AUFPWKuqAoGujhisXcrQ== Named Entity Recognition predictions generated by the fine-tuned Europeana BERT model on the held-out test set. Contains predicted entity spans, labels and corresponding gold-standard annotations used for the quantitative and qualitative evaluation of model performance.
test_scores_per_seed.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 - 4.9 KB - 13 Variables, 48 Observations - UNF:6:BULzL9VsIcllUNnVwyPBzg== Evaluation results of the transformer-based Named Entity Recognition models across multiple random seeds. Reports the test-set performance of each training run and was used to assess the robustness and reproducibility of the experimental results by calculating mean performance ac...
PARDONS.xlsx Aug 6, 2026 - Pardons MS Excel Spreadsheet - 252.2 KB - MD5: 8b8f50111036a0c5d3d25c90c67d576f
20260715_User Guide CSQ25.pdf Jul 16, 2026 - Core Study Questionnaire 2025 Adobe PDF - 519.8 KB - MD5: a2155500dd989372d08f21049283cdba CSQ25 user guide version 1.0
codebookCSQ25_SUF_en.html Jul 16, 2026 - Core Study Questionnaire 2025 HTML - 469.6 KB - MD5: 45212c473641c3674fb9f135f65a2d94 Codebook CSQ25 in English version 1.0

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 -

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.

test_predictions_europeana.tab

Tabular Data - 96.1 KB - 3 Variables, 17 Observations -

Named Entity Recognition predictions generated by the fine-tuned Europeana BERT model on the held-out test set. Contains predicted entity spans, labels and corresponding gold-standard annotations used for the quantitative and qualitative evaluation of model performance.

test_scores_per_seed.tab

Tabular Data - 4.9 KB - 13 Variables, 48 Observations -

Evaluation results of the transformer-based Named Entity Recognition models across multiple random seeds. Reports the test-set performance of each training run and was used to assess the robustness and reproducibility of the experimental results by calculating mean performance ac...

PARDONS.xlsx

Aug 6, 2026 - Pardons

MS Excel Spreadsheet - 252.2 KB -

20260715_User Guide CSQ25.pdf

Jul 16, 2026 - Core Study Questionnaire 2025

Adobe PDF - 519.8 KB -

CSQ25 user guide version 1.0

codebookCSQ25_SUF_en.html

Jul 16, 2026 - Core Study Questionnaire 2025

HTML - 469.6 KB -

Codebook CSQ25 in English version 1.0

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