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1 to 10 of 243 Results

development_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 - 167.4 KB - MD5: e2f0fbe2ba2975b0de463eb734e611ab Development (validation) partition of the corpus used for hyperparameter optimisation, model selection and intermediate evaluation during NER model training.
development_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 - 8.2 MB - MD5: 8d104a218fd93d5b17d6bf1671104d10 Python Pickle version of the development dataset used in the training workflows.
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_spacy_custom_trained_mismatches.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 - 1.6 MB - MD5: bd7431776ef54f320a6345ee2c39b5d0 Token-level comparison between predicted and gold-standard IOB labels for the custom-trained spaCy model. Used for the qualitative error analysis discussed in the article.
navez_spacy_custom_trained_results.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 - 155.9 KB - MD5: 519958789ccf7a67f90669ce21103d90 Predictions generated by the custom-trained fr_core_news_lg model after fine-tuning on the Navez gold-standard corpus. Includes all project-specific entity types (PER, LOC, ORG, GRP, ART, EXH and LETT).
navez_spacy_off_the_shelf_ner_lg_mismatches.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 MB - MD5: 19e45bf3f0974a05524450f94b2b93da Token-level comparison between predicted and gold-standard IOB labels for the off-the-shelf fr_core_news_lg model. Used for qualitative error analysis and identification of recurring recognition errors.
navez_spacy_off_the_shelf_ner_lg_results.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 - 156.9 KB - MD5: 7c28c59d7b17430f781573f98a1a5833 Model predictions generated by the off-the-shelf fr_core_news_lg model using the isolated NER component (Workflow 1). Contains predicted entities, spans, labels and corresponding gold-standard annotations for the held-out test set.
navez_spacy_off_the_shelf_ner_sm_mismatches.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 - 2.2 MB - MD5: fdd354911d784ba25ace9972ad447335 Token-level comparison between predicted and gold-standard IOB labels for the off-the-shelf fr_core_news_sm model. Used for qualitative error analysis and identification of recurring recognition errors.
navez_spacy_off_the_shelf_ner_sm_results.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 - 157.2 KB - MD5: 5f684b97b3c570eb8ada7751bdb6a075 Model predictions generated by the off-the-shelf fr_core_news_sm model using the isolated NER component (Workflow 1). Contains predicted entities, spans, labels and corresponding gold-standard annotations for the held-out test set.

development_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 - 167.4 KB -

Development (validation) partition of the corpus used for hyperparameter optimisation, model selection and intermediate evaluation during NER model training.

development_set-2.pkl

Unknown - 8.2 MB -

Python Pickle version of the development dataset used in the training workflows.

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_spacy_custom_trained_mismatches.csv

Comma Separated Values - 1.6 MB -

Token-level comparison between predicted and gold-standard IOB labels for the custom-trained spaCy model. Used for the qualitative error analysis discussed in the article.

navez_spacy_custom_trained_results.csv

Comma Separated Values - 155.9 KB -

Predictions generated by the custom-trained fr_core_news_lg model after fine-tuning on the Navez gold-standard corpus. Includes all project-specific entity types (PER, LOC, ORG, GRP, ART, EXH and LETT).

navez_spacy_off_the_shelf_ner_lg_mismatches.csv

Comma Separated Values - 2.0 MB -

Token-level comparison between predicted and gold-standard IOB labels for the off-the-shelf fr_core_news_lg model. Used for qualitative error analysis and identification of recurring recognition errors.

navez_spacy_off_the_shelf_ner_lg_results.csv

Comma Separated Values - 156.9 KB -

Model predictions generated by the off-the-shelf fr_core_news_lg model using the isolated NER component (Workflow 1). Contains predicted entities, spans, labels and corresponding gold-standard annotations for the held-out test set.

navez_spacy_off_the_shelf_ner_sm_mismatches.csv

Comma Separated Values - 2.2 MB -

Token-level comparison between predicted and gold-standard IOB labels for the off-the-shelf fr_core_news_sm model. Used for qualitative error analysis and identification of recurring recognition errors.

navez_spacy_off_the_shelf_ner_sm_results.csv

Comma Separated Values - 157.2 KB -

Model predictions generated by the off-the-shelf fr_core_news_sm model using the isolated NER component (Workflow 1). Contains predicted entities, spans, labels and corresponding gold-standard annotations for the held-out test set.

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