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Publication Year: 2026

11 to 20 of 1,365 Results

03_navez_NER_spacy_custom-trained_dk.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 - 575.5 KB - MD5: 5b3ef0c547261ba0b3323ce0fd2ca50b Implements Workflow 3 described in the article. The notebook fine-tunes fr_core_news_lg on the Navez gold-standard corpus by extending the default spaCy model with the domain-specific entity types ART, EXH, GRP and LETT, in addition to the standard PER, LOC and ORG categories. It...
best_hps.json 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 JSON - 474 B - MD5: a34a656006d26bd49168cb552f29955f JSON file containing the final hyperparameter configuration selected for each transformer model (CamemBERT, CamemBERTav2, D'AlemBERT and Europeana BERT) after hyperparameter optimisation.
combined_eval_scores3.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 - 1.4 KB - 13 Variables, 24 Observations - UNF:6:IHdDRnEBZ4B81/KMQBqVKA== Consolidated spreadsheet containing the evaluation scores of all experiments performed in this study. Facilitates comparison between off-the-shelf spaCy models, the custom-trained spaCy model, and the transformer-based models across different evaluation scenarios and entity types...
cv_results.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 - 6 Variables, 20 Observations - UNF:6:72/ohGHOKcR5yXwDUXrubA== Cross-validation results for the transformer-based NER models. Reports evaluation metrics across five training runs with the best hyperparameters per model to assess model robustness.
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.
errors_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 - 13.8 KB - 10 Variables, 159 Observations - UNF:6:dX1zhbyrPPk1DreziVEOHw== Prediction errors produced by the fine-tuned CamemBERTav2 model. Used for qualitative comparison with the other transformer-based NER models.
errors_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 - 43.3 KB - 10 Variables, 529 Observations - UNF:6:LKJL6zjeIoC7+p0mefydwg== Prediction errors produced by the fine-tuned CamemBERT model. Documents incorrectly recognised, partially recognised, missed and spurious entities for qualitative error analysis.
errors_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 - 16.0 KB - 10 Variables, 185 Observations - UNF:6:+TqzKZgt8g2h9mleixtmLw== Prediction errors produced by the fine-tuned D'AlemBERT model. Documents representative recognition errors analysed in the accompanying study.
errors_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 - 14.0 KB - 10 Variables, 161 Observations - UNF:6:r0qXPIXovg15bXXLwViguQ== Prediction errors produced by the fine-tuned Europeana BERT model. Supports the qualitative evaluation and comparison of transformer-based Named Entity Recognition models.

03_navez_NER_spacy_custom-trained_dk.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 - 575.5 KB -

Implements Workflow 3 described in the article. The notebook fine-tunes fr_core_news_lg on the Navez gold-standard corpus by extending the default spaCy model with the domain-specific entity types ART, EXH, GRP and LETT, in addition to the standard PER, LOC and ORG categories. It...

best_hps.json

JSON - 474 B -

JSON file containing the final hyperparameter configuration selected for each transformer model (CamemBERT, CamemBERTav2, D'AlemBERT and Europeana BERT) after hyperparameter optimisation.

combined_eval_scores3.tab

Tabular Data - 1.4 KB - 13 Variables, 24 Observations -

Consolidated spreadsheet containing the evaluation scores of all experiments performed in this study. Facilitates comparison between off-the-shelf spaCy models, the custom-trained spaCy model, and the transformer-based models across different evaluation scenarios and entity types...

cv_results.tab

Tabular Data - 1.8 KB - 6 Variables, 20 Observations -

Cross-validation results for the transformer-based NER models. Reports evaluation metrics across five training runs with the best hyperparameters per model to assess model robustness.

development_set-2.csv

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.

errors_camembertav2.tab

Tabular Data - 13.8 KB - 10 Variables, 159 Observations -

Prediction errors produced by the fine-tuned CamemBERTav2 model. Used for qualitative comparison with the other transformer-based NER models.

errors_camembert.tab

Tabular Data - 43.3 KB - 10 Variables, 529 Observations -

Prediction errors produced by the fine-tuned CamemBERT model. Documents incorrectly recognised, partially recognised, missed and spurious entities for qualitative error analysis.

errors_dalembert.tab

Tabular Data - 16.0 KB - 10 Variables, 185 Observations -

Prediction errors produced by the fine-tuned D'AlemBERT model. Documents representative recognition errors analysed in the accompanying study.

errors_europeana.tab

Tabular Data - 14.0 KB - 10 Variables, 161 Observations -

Prediction errors produced by the fine-tuned Europeana BERT model. Supports the qualitative evaluation and comparison of transformer-based Named Entity Recognition models.

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