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Publication Year: 2026

111 to 120 of 1,392 Results

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...
test_set-2.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 - 117.5 KB - MD5: c079a2cd638f0797e2b2ec4e06348e29 Held-out test split used exclusively for final evaluation of the trained NER models.
test_set-2.pkl 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 Unknown - 6.8 MB - MD5: ebeb69df35b8eecb39e9ce2342040c8d The file contains manually annotated letter transcriptions and their token-level entity labels. For each letter, it includes the manuscript identifier, full transcription, tokenized text, annotated entities and entity types, character and token spans, remapped annotations, and IO...
training_set-2.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 - 730.5 KB - MD5: 8da5d01228c4818dddce6a97272b18be Held-out test partition of the gold-standard corpus used exclusively for final evaluation of the NER models after training.
training_set-2.pkl 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 Unknown - 24.8 MB - MD5: 050192c781468337fb7718810e8316b7 Python Pickle version of the held-out test dataset used in the evaluation pipeline.

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 -

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...

test_set-2.csv

Comma Separated Values - 117.5 KB -

Held-out test split used exclusively for final evaluation of the trained NER models.

test_set-2.pkl

Unknown - 6.8 MB -

The file contains manually annotated letter transcriptions and their token-level entity labels. For each letter, it includes the manuscript identifier, full transcription, tokenized text, annotated entities and entity types, character and token spans, remapped annotations, and IO...

training_set-2.csv

Comma Separated Values - 730.5 KB -

Held-out test partition of the gold-standard corpus used exclusively for final evaluation of the NER models after training.

training_set-2.pkl

Unknown - 24.8 MB -

Python Pickle version of the held-out test dataset used in the evaluation pipeline.

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