71 to 80 of 3,928 Results
Comma Separated Values - 988 B -
MD5: 95c489a1ea58b92b8d94ef795bc284ce
Evaluation metrics for each entity type recognised by the off-the-shelf fr_core_news_lg model (PER, LOC and ORG). Enables comparison of model performance across entity categories. |
Comma Separated Values - 439 B -
MD5: b1e463b5396ee3ecd1eb9256b33d3ccf
Overall evaluation results for the off-the-shelf fr_core_news_sm model. Reports precision, recall and F1 scores under the Strict, Exact, Partial and Type evaluation scenarios using the Nervaluate framework. |
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. |
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. |
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. |
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. |
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. |
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. |
