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Publication Year: 2026

21 to 30 of 1,336 Results

headline_test_summary.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 - 291 B - MD5: a6b7319263f4f7606f6483082613f61f Summary of the final evaluation results obtained on the held-out test set for all transformer-based models averaged across three different seeds.
hp_search.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 - 4.4 KB - 6 Variables, 72 Observations - UNF:6:zu0B79FNEke0HpLSL1WIKw== Results of the hyperparameter optimisation experiments performed for the transformer-based Named Entity Recognition models. Records the tested hyperparameter combinations and their corresponding evaluation scores used to identify the optimal training configuration.
learning_curve.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 - 186 B - 4 Variables, 4 Observations - UNF:6:fXKPhq8vFAp9d+v7Jlmpsg== Performance metrics recorded during model training across successive epochs. Used to analyse convergence behaviour, model learning dynamics and training stability.
length_stratified.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 - 994 B - 5 Variables, 16 Observations - UNF:6:/TrpFwgbWWGaD2ZI1dFDKQ== Evaluation results stratified by document length. Used to assess the influence of letter length on Named Entity Recognition performance across the transformer-based models.
length_summary.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 - 533 B - 5 Variables, 9 Observations - UNF:6:XKylVdddSgIVxmbYCdrnFg== Summary statistics describing the distribution of document subword lengths in the evaluation corpus splitsand their relationship to model performance. Used to assess the number of letters affected by preprocessing.
navez_gs_preprocessed-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 - 1015.3 KB - MD5: 0469ba07b6aa2663770de059d36ef03f Preprocessed version of the manually annotated corpus after cleaning and conversion from Label Studio. Contains token- and entity-level information used for model training and evaluation.
navez_gs_preprocessed-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 - 33.0 MB - MD5: 3a11affcc28d1dc3ac86f185c3f089a2 Python pickle version of the preprocessed gold-standard corpus, preserving data structures used directly in the notebooks.
Navez_ner_finetuning_weightedcrossentropy.ipynb 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 Jupyter Notebook - 4.6 MB - MD5: 8c2912db3ec1743bd4c24c1103a01c99 Notebook for fine-tuning and evaluating French transformer-based models for Named Entity Recognition on the Navez correspondence. It implements the experiments with CamemBERT, CamemBERTav2, D'AlemBERT and Europeana BERT, including weighted cross-entropy training, hyperparameter o...
navez_spacy_custom_trained_evalall-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 - 424 B - MD5: 2431b25f16ced713f3f94efd19eede8d Overall evaluation metrics for the custom-trained spaCy model, including the four project-specific entity types introduced during fine-tuning. Reports precision, recall and F1 under the Nervaluate evaluation scenarios.
navez_spacy_custom_trained_evalall_no_dk-2.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 - 462 B - 1 Variables, 4 Observations - UNF:6:yq7KQEdVC0k1T86KGkSn0g== Overall evaluation of the custom-trained model restricted to the standard spaCy entity types (PER, LOC and ORG). This output enables direct comparison with the off-the-shelf spaCy models presented in the article.

headline_test_summary.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 - 291 B -

Summary of the final evaluation results obtained on the held-out test set for all transformer-based models averaged across three different seeds.

hp_search.tab

Tabular Data - 4.4 KB - 6 Variables, 72 Observations -

Results of the hyperparameter optimisation experiments performed for the transformer-based Named Entity Recognition models. Records the tested hyperparameter combinations and their corresponding evaluation scores used to identify the optimal training configuration.

learning_curve.tab

Tabular Data - 186 B - 4 Variables, 4 Observations -

Performance metrics recorded during model training across successive epochs. Used to analyse convergence behaviour, model learning dynamics and training stability.

length_stratified.tab

Tabular Data - 994 B - 5 Variables, 16 Observations -

Evaluation results stratified by document length. Used to assess the influence of letter length on Named Entity Recognition performance across the transformer-based models.

length_summary.tab

Tabular Data - 533 B - 5 Variables, 9 Observations -

Summary statistics describing the distribution of document subword lengths in the evaluation corpus splitsand their relationship to model performance. Used to assess the number of letters affected by preprocessing.

navez_gs_preprocessed-2.csv

Comma Separated Values - 1015.3 KB -

Preprocessed version of the manually annotated corpus after cleaning and conversion from Label Studio. Contains token- and entity-level information used for model training and evaluation.

navez_gs_preprocessed-2.pkl

Unknown - 33.0 MB -

Python pickle version of the preprocessed gold-standard corpus, preserving data structures used directly in the notebooks.

Navez_ner_finetuning_weightedcrossentropy.ipynb

Jupyter Notebook - 4.6 MB -

Notebook for fine-tuning and evaluating French transformer-based models for Named Entity Recognition on the Navez correspondence. It implements the experiments with CamemBERT, CamemBERTav2, D'AlemBERT and Europeana BERT, including weighted cross-entropy training, hyperparameter o...

navez_spacy_custom_trained_evalall-2.csv

Comma Separated Values - 424 B -

Overall evaluation metrics for the custom-trained spaCy model, including the four project-specific entity types introduced during fine-tuning. Reports precision, recall and F1 under the Nervaluate evaluation scenarios.

navez_spacy_custom_trained_evalall_no_dk-2.tab

Tabular Data - 462 B - 1 Variables, 4 Observations -

Overall evaluation of the custom-trained model restricted to the standard spaCy entity types (PER, LOC and ORG). This output enables direct comparison with the off-the-shelf spaCy models presented in the article.

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