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clasificador-ser-estar-window-5-bert-base

Text Classification
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bert
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  2. model.safetensors +1 -1
README.md CHANGED
@@ -19,11 +19,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [google-bert/bert-base-multilingual-uncased](https://huggingface.co/google-bert/bert-base-multilingual-uncased) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.4932
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- - F1 Score: 0.8401
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- - Recall: 0.9536
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- - Precision: 0.7508
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- - Roc Auc: 0.8601
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  ## Model description
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@@ -56,26 +56,26 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | F1 Score | Recall | Precision | Roc Auc |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:-------:|
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- | No log | 1.0 | 25 | 0.6813 | 0.7465 | 1.0 | 0.5955 | 0.5145 |
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- | No log | 2.0 | 50 | 0.6746 | 0.7465 | 1.0 | 0.5955 | 0.5832 |
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- | No log | 3.0 | 75 | 0.5414 | 0.8136 | 0.9578 | 0.7072 | 0.7754 |
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- | No log | 4.0 | 100 | 0.4586 | 0.8296 | 0.9451 | 0.7393 | 0.8525 |
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- | No log | 5.0 | 125 | 0.4932 | 0.8401 | 0.9536 | 0.7508 | 0.8601 |
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- | No log | 6.0 | 150 | 0.5393 | 0.8281 | 0.8945 | 0.7709 | 0.8585 |
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- | No log | 7.0 | 175 | 0.5481 | 0.8311 | 0.9241 | 0.7552 | 0.8636 |
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- | No log | 8.0 | 200 | 0.5758 | 0.8336 | 0.9409 | 0.7483 | 0.8506 |
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- | No log | 9.0 | 225 | 0.6115 | 0.8277 | 0.8819 | 0.7799 | 0.8622 |
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- | No log | 10.0 | 250 | 0.5712 | 0.8333 | 0.9072 | 0.7706 | 0.8641 |
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- | No log | 11.0 | 275 | 0.6646 | 0.8291 | 0.8903 | 0.7757 | 0.8605 |
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- | No log | 12.0 | 300 | 0.6946 | 0.8397 | 0.9283 | 0.7666 | 0.8583 |
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- | No log | 13.0 | 325 | 0.7200 | 0.8356 | 0.9114 | 0.7714 | 0.8584 |
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- | No log | 14.0 | 350 | 0.6916 | 0.8330 | 0.9156 | 0.7641 | 0.8556 |
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- | No log | 15.0 | 375 | 0.6988 | 0.8369 | 0.9198 | 0.7676 | 0.8612 |
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- | No log | 16.0 | 400 | 0.7424 | 0.8308 | 0.9114 | 0.7633 | 0.8587 |
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- | No log | 17.0 | 425 | 0.7404 | 0.8340 | 0.9114 | 0.7687 | 0.8678 |
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- | No log | 18.0 | 450 | 0.7558 | 0.8356 | 0.9114 | 0.7714 | 0.8689 |
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- | No log | 19.0 | 475 | 0.7841 | 0.8324 | 0.9114 | 0.7660 | 0.8634 |
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- | 0.3179 | 20.0 | 500 | 0.7865 | 0.8324 | 0.9114 | 0.7660 | 0.8636 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [google-bert/bert-base-multilingual-uncased](https://huggingface.co/google-bert/bert-base-multilingual-uncased) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.9606
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+ - F1 Score: 0.8491
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+ - Recall: 0.8903
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+ - Precision: 0.8115
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+ - Roc Auc: 0.9028
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | F1 Score | Recall | Precision | Roc Auc |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:-------:|
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+ | No log | 1.0 | 25 | 0.6774 | 0.7465 | 1.0 | 0.5955 | 0.5843 |
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+ | No log | 2.0 | 50 | 0.5260 | 0.7636 | 0.7089 | 0.8276 | 0.8132 |
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+ | No log | 3.0 | 75 | 0.4120 | 0.8393 | 0.9367 | 0.7603 | 0.8954 |
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+ | No log | 4.0 | 100 | 0.4838 | 0.8363 | 0.8945 | 0.7852 | 0.8914 |
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+ | No log | 5.0 | 125 | 0.4970 | 0.8267 | 0.8354 | 0.8182 | 0.8933 |
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+ | No log | 6.0 | 150 | 0.5585 | 0.8381 | 0.8734 | 0.8054 | 0.8932 |
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+ | No log | 7.0 | 175 | 0.5102 | 0.84 | 0.8861 | 0.7985 | 0.8960 |
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+ | No log | 8.0 | 200 | 0.7016 | 0.8316 | 0.8439 | 0.8197 | 0.8892 |
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+ | No log | 9.0 | 225 | 0.8863 | 0.8313 | 0.8523 | 0.8112 | 0.8793 |
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+ | No log | 10.0 | 250 | 0.8687 | 0.8273 | 0.7679 | 0.8966 | 0.8876 |
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+ | No log | 11.0 | 275 | 0.8506 | 0.8457 | 0.8903 | 0.8053 | 0.8926 |
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+ | No log | 12.0 | 300 | 0.9160 | 0.8393 | 0.7932 | 0.8910 | 0.8951 |
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+ | No log | 13.0 | 325 | 1.0401 | 0.8352 | 0.7806 | 0.8981 | 0.8918 |
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+ | No log | 14.0 | 350 | 1.0209 | 0.8361 | 0.8608 | 0.8127 | 0.8913 |
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+ | No log | 15.0 | 375 | 0.9580 | 0.8438 | 0.7975 | 0.8957 | 0.9036 |
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+ | No log | 16.0 | 400 | 0.9606 | 0.8491 | 0.8903 | 0.8115 | 0.9028 |
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+ | No log | 17.0 | 425 | 1.0079 | 0.8413 | 0.8945 | 0.7940 | 0.9000 |
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+ | No log | 18.0 | 450 | 1.0042 | 0.8463 | 0.8945 | 0.8030 | 0.9015 |
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+ | No log | 19.0 | 475 | 0.9886 | 0.8474 | 0.8903 | 0.8084 | 0.9006 |
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+ | 0.2037 | 20.0 | 500 | 1.0047 | 0.8457 | 0.8903 | 0.8053 | 0.9012 |
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  ### Framework versions
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