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31 to 40 of 3,896 Results

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_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.
navez_spacy_custom_trained_evalpertype-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 - 2.0 KB - MD5: d770abff6f7b4ac2791c99f080b2ccc9 Detailed evaluation metrics for each entity type recognised by the custom-trained model, including both standard and domain-specific categories (PER, LOC, ORG, GRP, ART, EXH and LETT).
navez_spacy_off_the_shelf_ner_lg_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 - 410 B - MD5: 27ce89c83902be5851a3c2a3a4c0cf30 Overall evaluation results for the off-the-shelf fr_core_news_lg model. Reports precision, recall and F1 scores under the Strict, Exact, Partial and Type evaluation scenarios using the Nervaluate framework.
navez_spacy_off_the_shelf_ner_lg_evalpertype-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 - 988 B - MD5: 95c489a1ea58b92b8d94ef795bc284ce Evaluation metrics for each entity type recognised by the off-the-shelf fr_core_news_lg model (PER, LOC and ORG). Enables comparison of model performance across entity categories.
navez_spacy_off_the_shelf_ner_sm_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 - 439 B - MD5: b1e463b5396ee3ecd1eb9256b33d3ccf Overall evaluation results for the off-the-shelf fr_core_news_sm model. Reports precision, recall and F1 scores under the Strict, Exact, Partial and Type evaluation scenarios using the Nervaluate framework.

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 -

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_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.

navez_spacy_custom_trained_evalpertype-2.csv

Comma Separated Values - 2.0 KB -

Detailed evaluation metrics for each entity type recognised by the custom-trained model, including both standard and domain-specific categories (PER, LOC, ORG, GRP, ART, EXH and LETT).

navez_spacy_off_the_shelf_ner_lg_evalall-2.csv

Comma Separated Values - 410 B -

Overall evaluation results for the off-the-shelf fr_core_news_lg model. Reports precision, recall and F1 scores under the Strict, Exact, Partial and Type evaluation scenarios using the Nervaluate framework.

navez_spacy_off_the_shelf_ner_lg_evalpertype-2.csv

Comma Separated Values - 988 B -

Evaluation metrics for each entity type recognised by the off-the-shelf fr_core_news_lg model (PER, LOC and ORG). Enables comparison of model performance across entity categories.

navez_spacy_off_the_shelf_ner_sm_evalall-2.csv

Comma Separated Values - 439 B -

Overall evaluation results for the off-the-shelf fr_core_news_sm model. Reports precision, recall and F1 scores under the Strict, Exact, Partial and Type evaluation scenarios using the Nervaluate framework.

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