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41 to 50 of 3,896 Results

navez_spacy_off_the_shelf_ner_sm_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 - 1.1 KB - MD5: 1c6948aab25b26cdc34d89384e66544a Evaluation metrics for each entity type recognised by the off-the-shelf fr_core_news_sm model (PER, LOC and ORG). Enables comparison of model performance across entity categories.
navez_spacy_off_the_shelf_nlp_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 - 434 B - MD5: 0b166ca4796478707cf53976e9ed3da1 Overall evaluation results for the complete fr_core_news_lg NLP pipeline. Reports precision, recall and F1 scores under the Strict, Exact, Partial and Type evaluation scenarios using the Nervaluate framework.
navez_spacy_off_the_shelf_nlp_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 - 1.0 KB - MD5: 02002572ce6487ca896a367b54f4d998 Evaluation metrics for each entity type recognised by the complete fr_core_news_lg pipeline (PER, LOC and ORG). Enables comparison with the isolated NER workflow and the custom-trained model.
navez_spacy_off_the_shelf_nlp_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 - 438 B - MD5: 60738aaa0921c5c4de7de9e63ecca08a Overall evaluation results for the complete fr_core_news_sm NLP pipeline. Reports precision, recall and F1 scores under the Strict, Exact, Partial and Type evaluation scenarios using the Nervaluate framework.
navez_spacy_off_the_shelf_nlp_sm_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 - 1.0 KB - MD5: 0063ddb26e8f5f36183bac8e2cffb6d2 Evaluation metrics for each entity type recognised by the complete fr_core_news_sm pipeline (PER, LOC and ORG). Enables comparison with the isolated NER workflow.
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.
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.

navez_spacy_off_the_shelf_ner_sm_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 - 1.1 KB -

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

navez_spacy_off_the_shelf_nlp_lg_evalall-2.csv

Comma Separated Values - 434 B -

Overall evaluation results for the complete fr_core_news_lg NLP pipeline. Reports precision, recall and F1 scores under the Strict, Exact, Partial and Type evaluation scenarios using the Nervaluate framework.

navez_spacy_off_the_shelf_nlp_lg_evalpertype-2.csv

Comma Separated Values - 1.0 KB -

Evaluation metrics for each entity type recognised by the complete fr_core_news_lg pipeline (PER, LOC and ORG). Enables comparison with the isolated NER workflow and the custom-trained model.

navez_spacy_off_the_shelf_nlp_sm_evalall-2.csv

Comma Separated Values - 438 B -

Overall evaluation results for the complete fr_core_news_sm NLP pipeline. Reports precision, recall and F1 scores under the Strict, Exact, Partial and Type evaluation scenarios using the Nervaluate framework.

navez_spacy_off_the_shelf_nlp_sm_evalpertype-2.csv

Comma Separated Values - 1.0 KB -

Evaluation metrics for each entity type recognised by the complete fr_core_news_sm pipeline (PER, LOC and ORG). Enables comparison with the isolated NER workflow.

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.

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.

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