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ner-bert-large-cased-pt-contratos_tceal

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contratos_tceal

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  1. README.md +29 -29
  2. model.safetensors +1 -1
  3. runs/Jan10_14-17-41_aa538244e52d/events.out.tfevents.1704897581.aa538244e52d.937.2 +3 -0
  4. runs/Jan10_14-17-41_aa538244e52d/events.out.tfevents.1704901207.aa538244e52d.937.3 +3 -0
  5. training_args.bin +1 -1
README.md CHANGED
@@ -23,16 +23,16 @@ model-index:
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  metrics:
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  - name: Precision
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  type: precision
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- value: 0.8953924914675768
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  - name: Recall
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  type: recall
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- value: 0.9060611293386289
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  - name: F1
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  type: f1
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- value: 0.9006952192944813
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  - name: Accuracy
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  type: accuracy
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- value: 0.9424897938441211
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -42,11 +42,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model was trained from scratch on the contratos_tceal dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.4054
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- - Precision: 0.8954
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- - Recall: 0.9061
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- - F1: 0.9007
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- - Accuracy: 0.9425
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  ## Model description
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@@ -77,26 +77,26 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | No log | 1.0 | 331 | 0.4092 | 0.7739 | 0.8094 | 0.7913 | 0.9062 |
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- | 0.4849 | 2.0 | 662 | 0.3127 | 0.8671 | 0.8841 | 0.8755 | 0.9356 |
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- | 0.4849 | 3.0 | 993 | 0.2946 | 0.8726 | 0.8821 | 0.8773 | 0.9366 |
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- | 0.2571 | 4.0 | 1324 | 0.3077 | 0.8770 | 0.8850 | 0.8810 | 0.9322 |
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- | 0.1827 | 5.0 | 1655 | 0.2851 | 0.8821 | 0.8936 | 0.8878 | 0.9401 |
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- | 0.1827 | 6.0 | 1986 | 0.3059 | 0.8872 | 0.8974 | 0.8923 | 0.9395 |
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- | 0.1503 | 7.0 | 2317 | 0.3000 | 0.8882 | 0.8998 | 0.8940 | 0.9389 |
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- | 0.1157 | 8.0 | 2648 | 0.3008 | 0.8892 | 0.9038 | 0.8965 | 0.9420 |
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- | 0.1157 | 9.0 | 2979 | 0.3450 | 0.8758 | 0.9024 | 0.8889 | 0.9381 |
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- | 0.0973 | 10.0 | 3310 | 0.3314 | 0.8911 | 0.9005 | 0.8958 | 0.9416 |
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- | 0.0799 | 11.0 | 3641 | 0.3468 | 0.8889 | 0.9004 | 0.8946 | 0.9406 |
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- | 0.0799 | 12.0 | 3972 | 0.3567 | 0.8891 | 0.9011 | 0.8950 | 0.9398 |
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- | 0.0661 | 13.0 | 4303 | 0.3637 | 0.8893 | 0.8973 | 0.8932 | 0.9404 |
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- | 0.0532 | 14.0 | 4634 | 0.3778 | 0.8892 | 0.9005 | 0.8948 | 0.9398 |
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- | 0.0532 | 15.0 | 4965 | 0.3875 | 0.8836 | 0.9030 | 0.8932 | 0.9403 |
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- | 0.0456 | 16.0 | 5296 | 0.3955 | 0.8896 | 0.9021 | 0.8958 | 0.9403 |
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- | 0.0371 | 17.0 | 5627 | 0.4034 | 0.8885 | 0.9050 | 0.8967 | 0.9405 |
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- | 0.0371 | 18.0 | 5958 | 0.4019 | 0.8909 | 0.9043 | 0.8976 | 0.9408 |
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- | 0.0347 | 19.0 | 6289 | 0.4038 | 0.8966 | 0.9059 | 0.9012 | 0.9425 |
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- | 0.0297 | 20.0 | 6620 | 0.4054 | 0.8954 | 0.9061 | 0.9007 | 0.9425 |
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  ### Framework versions
 
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  metrics:
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  - name: Precision
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  type: precision
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+ value: 0.918525703200776
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  - name: Recall
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  type: recall
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+ value: 0.9458964541368403
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  - name: F1
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  type: f1
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+ value: 0.9320101697695399
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9639352869753552
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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  This model was trained from scratch on the contratos_tceal dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.2229
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+ - Precision: 0.9185
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+ - Recall: 0.9459
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+ - F1: 0.9320
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+ - Accuracy: 0.9639
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | No log | 1.0 | 363 | 0.1242 | 0.9164 | 0.9469 | 0.9314 | 0.9696 |
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+ | 0.1307 | 2.0 | 726 | 0.1413 | 0.9197 | 0.9424 | 0.9309 | 0.9664 |
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+ | 0.0831 | 3.0 | 1089 | 0.1366 | 0.9237 | 0.9477 | 0.9356 | 0.9682 |
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+ | 0.0831 | 4.0 | 1452 | 0.1360 | 0.9283 | 0.9489 | 0.9385 | 0.9696 |
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+ | 0.0646 | 5.0 | 1815 | 0.1572 | 0.9171 | 0.9427 | 0.9297 | 0.9646 |
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+ | 0.0473 | 6.0 | 2178 | 0.1674 | 0.9069 | 0.9454 | 0.9257 | 0.9646 |
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+ | 0.0367 | 7.0 | 2541 | 0.1783 | 0.9155 | 0.9414 | 0.9283 | 0.9644 |
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+ | 0.0367 | 8.0 | 2904 | 0.1823 | 0.9244 | 0.9442 | 0.9342 | 0.9656 |
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+ | 0.029 | 9.0 | 3267 | 0.1815 | 0.9190 | 0.9444 | 0.9315 | 0.9655 |
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+ | 0.0227 | 10.0 | 3630 | 0.1945 | 0.9084 | 0.9457 | 0.9267 | 0.9617 |
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+ | 0.0227 | 11.0 | 3993 | 0.1962 | 0.9134 | 0.9442 | 0.9285 | 0.9635 |
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+ | 0.0188 | 12.0 | 4356 | 0.1893 | 0.9203 | 0.9442 | 0.9321 | 0.9651 |
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+ | 0.0134 | 13.0 | 4719 | 0.1982 | 0.9181 | 0.9441 | 0.9309 | 0.9650 |
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+ | 0.0126 | 14.0 | 5082 | 0.1962 | 0.9162 | 0.9447 | 0.9303 | 0.9667 |
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+ | 0.0126 | 15.0 | 5445 | 0.2112 | 0.9196 | 0.9446 | 0.9319 | 0.9642 |
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+ | 0.0099 | 16.0 | 5808 | 0.2138 | 0.9165 | 0.9449 | 0.9305 | 0.9630 |
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+ | 0.007 | 17.0 | 6171 | 0.2110 | 0.9208 | 0.9447 | 0.9326 | 0.9652 |
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+ | 0.0075 | 18.0 | 6534 | 0.2216 | 0.9210 | 0.9452 | 0.9330 | 0.9641 |
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+ | 0.0075 | 19.0 | 6897 | 0.2232 | 0.9191 | 0.9461 | 0.9324 | 0.9640 |
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+ | 0.0062 | 20.0 | 7260 | 0.2229 | 0.9185 | 0.9459 | 0.9320 | 0.9639 |
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  ### Framework versions
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