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mnavas
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roberta-finetuned-CPV_Spanish

Text Classification
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roberta
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María Navas Loro commited on
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update model card README.md

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@@ -17,12 +17,12 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0463
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- - F1: 0.7931
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- - Roc Auc: 0.8858
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- - Accuracy: 0.7376
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- - Coverage Error: 10.3626
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- - Label Ranking Average Precision Score: 0.7968
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  ## Model description
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@@ -53,16 +53,16 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | Coverage Error | Label Ranking Average Precision Score |
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  |:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|:--------:|:--------------:|:-------------------------------------:|
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- | 0.0355 | 1.0 | 9054 | 0.0366 | 0.7550 | 0.8373 | 0.6950 | 14.1539 | 0.7347 |
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- | 0.0309 | 2.0 | 18108 | 0.0330 | 0.7773 | 0.8553 | 0.7204 | 12.6503 | 0.7647 |
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- | 0.0234 | 3.0 | 27162 | 0.0330 | 0.7836 | 0.8693 | 0.7293 | 11.6192 | 0.7799 |
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- | 0.0159 | 4.0 | 36216 | 0.0348 | 0.7830 | 0.8709 | 0.7291 | 11.5355 | 0.7810 |
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- | 0.0109 | 5.0 | 45270 | 0.0376 | 0.7789 | 0.8786 | 0.7201 | 10.9898 | 0.7812 |
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- | 0.0075 | 6.0 | 54324 | 0.0397 | 0.7838 | 0.8813 | 0.7241 | 10.7035 | 0.7861 |
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- | 0.0039 | 7.0 | 63378 | 0.0415 | 0.7888 | 0.8818 | 0.7309 | 10.6559 | 0.7898 |
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- | 0.0028 | 8.0 | 72432 | 0.0437 | 0.7906 | 0.8838 | 0.7326 | 10.5117 | 0.7924 |
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- | 0.0016 | 9.0 | 81486 | 0.0453 | 0.7908 | 0.8890 | 0.7308 | 10.0988 | 0.7957 |
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- | 0.001 | 10.0 | 90540 | 0.0463 | 0.7931 | 0.8858 | 0.7376 | 10.3626 | 0.7968 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0460
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+ - F1: 0.7937
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+ - Roc Auc: 0.8857
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+ - Accuracy: 0.7398
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+ - Coverage Error: 10.3171
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+ - Label Ranking Average Precision Score: 0.7977
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | Coverage Error | Label Ranking Average Precision Score |
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  |:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|:--------:|:--------------:|:-------------------------------------:|
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+ | 0.0359 | 1.0 | 9054 | 0.0368 | 0.7527 | 0.8361 | 0.6920 | 14.2585 | 0.7318 |
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+ | 0.0314 | 2.0 | 18108 | 0.0332 | 0.7753 | 0.8518 | 0.7198 | 12.9053 | 0.7612 |
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+ | 0.0235 | 3.0 | 27162 | 0.0332 | 0.7824 | 0.8656 | 0.7284 | 11.8961 | 0.7767 |
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+ | 0.0166 | 4.0 | 36216 | 0.0348 | 0.7824 | 0.8725 | 0.7289 | 11.3928 | 0.7821 |
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+ | 0.0114 | 5.0 | 45270 | 0.0371 | 0.7825 | 0.8799 | 0.7271 | 10.8051 | 0.7871 |
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+ | 0.0079 | 6.0 | 54324 | 0.0398 | 0.7829 | 0.8765 | 0.7260 | 11.0922 | 0.7831 |
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+ | 0.0042 | 7.0 | 63378 | 0.0414 | 0.7889 | 0.8798 | 0.7317 | 10.7793 | 0.7891 |
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+ | 0.0025 | 8.0 | 72432 | 0.0434 | 0.7895 | 0.8847 | 0.7317 | 10.3856 | 0.7924 |
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+ | 0.0014 | 9.0 | 81486 | 0.0451 | 0.7928 | 0.8860 | 0.7356 | 10.3086 | 0.7960 |
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+ | 0.001 | 10.0 | 90540 | 0.0460 | 0.7937 | 0.8857 | 0.7398 | 10.3171 | 0.7977 |
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  ### Framework versions