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Publication Year: 2026

61 to 70 of 1,392 Results

01a_navez_NER_spacy_off_the_shelf_no_dk_ner_pipe_sm.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 - 201.0 KB - MD5: fafce906de349b467636197fa64a4d95 Implements Workflow 1 described in the article by evaluating the off-the-shelf spaCy fr_core_news_sm model using only the isolated Named Entity Recognition (NER) component. The notebook evaluates the model on the held-out test set without additional domain-specific training.
01b_navez_NER_spacy_off_the_shelf_no_dk_ner_pipe_lg.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 - 248.3 KB - MD5: 4d33ecdf3831757bac0319676a57bb2a Implements Workflow 1 using the larger fr_core_news_lg model. The notebook evaluates the isolated NER component on the historical correspondence corpus and compares its performance with the smaller spaCy model.
02a_navez_NER_spacy_off_the_shelf_no_dk_nlp_pipe_sm.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 - 200.0 KB - MD5: 2998c19d3448da4d9ed7a7776de6e9dc Implements Workflow 2 by using the complete spaCy fr_core_news_sm pipeline, including all NLP components. The notebook assesses whether embedding the NER component within the full pipeline influences recognition performance.
02b_navez_NER_spacy_off_the_shelf_no_dk_nlp_pipe_lg.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 - 255.7 KB - MD5: 1e35b33c8202285ec93bb0d8508ec8ff Implements Workflow 2 using the complete fr_core_news_lg pipeline. Results are compared with Workflow 1 to evaluate the effect of the full NLP pipeline on NER performance.
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.

01a_navez_NER_spacy_off_the_shelf_no_dk_ner_pipe_sm.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 - 201.0 KB -

Implements Workflow 1 described in the article by evaluating the off-the-shelf spaCy fr_core_news_sm model using only the isolated Named Entity Recognition (NER) component. The notebook evaluates the model on the held-out test set without additional domain-specific training.

01b_navez_NER_spacy_off_the_shelf_no_dk_ner_pipe_lg.ipynb

Jupyter Notebook - 248.3 KB -

Implements Workflow 1 using the larger fr_core_news_lg model. The notebook evaluates the isolated NER component on the historical correspondence corpus and compares its performance with the smaller spaCy model.

02a_navez_NER_spacy_off_the_shelf_no_dk_nlp_pipe_sm.ipynb

Jupyter Notebook - 200.0 KB -

Implements Workflow 2 by using the complete spaCy fr_core_news_sm pipeline, including all NLP components. The notebook assesses whether embedding the NER component within the full pipeline influences recognition performance.

02b_navez_NER_spacy_off_the_shelf_no_dk_nlp_pipe_lg.ipynb

Jupyter Notebook - 255.7 KB -

Implements Workflow 2 using the complete fr_core_news_lg pipeline. Results are compared with Workflow 1 to evaluate the effect of the full NLP pipeline on NER performance.

03_navez_NER_spacy_custom-trained_dk.ipynb

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.

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