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121 to 130 of 1,412 Results
Comma Separated Values - 156.8 KB - MD5: 0c0b15b22c6286d52351ce6df21146e7
Model predictions generated by the off-the-shelf fr_core_news_lg model using the complete spaCy NLP pipeline (Workflow 2). Contains predicted entities, spans, labels and corresponding gold-standard annotations for the held-out test set.
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.
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.
Comma Separated Values - 2.1 MB - MD5: 25c9405590014091e5d08bf4aebb8301
Token-level comparison between predicted and gold-standard IOB labels for the complete fr_core_news_sm NLP pipeline. Used for the qualitative error analysis and identification of recurring recognition errors.
Comma Separated Values - 157.0 KB - MD5: 1128164075ad2b33e1902c44b764d000
Model predictions generated by the off-the-shelf fr_core_news_sm model using the complete spaCy NLP pipeline (Workflow 2). Contains predicted entities, spans, labels and corresponding gold-standard annotations for the held-out test set.
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.
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.
JSON - 198.6 KB - MD5: 72da0345995240cc733071556d1a647e
Part 1 - JSON export from Label Studio containing manually annotated nineteenth-century French correspondence from the Navez Project. Includes entity annotations, transcriptions and project metadata. Used as source data for preprocessing and model training.
JSON - 575.1 KB - MD5: 526f06d4f9ce02a351f068df4162d62b
Part 2 - JSON export from Label Studio containing manually annotated nineteenth-century French correspondence from the Navez Project. Includes entity annotations, transcriptions and project metadata. Used as source data for preprocessing and model training.
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.
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