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ner-bert-large-cased-pt-contratos_tceal

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contratos_tceal

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  1. README.md +30 -30
  2. model.safetensors +1 -1
  3. runs/Dec22_19-06-27_f6155cf7bac4/events.out.tfevents.1703273497.f6155cf7bac4.6191.2 +3 -0
  4. runs/Dec22_19-06-27_f6155cf7bac4/events.out.tfevents.1703275699.f6155cf7bac4.6191.3 +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: 0.8960907944514502
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  - name: Recall
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  type: recall
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- value: 0.9091606960081884
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  - name: F1
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  type: f1
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- value: 0.9025784326178077
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  - name: Accuracy
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  type: accuracy
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- value: 0.9402469135802469
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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: 0.3934
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- - Precision: 0.8961
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- - Recall: 0.9092
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- - F1: 0.9026
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- - Accuracy: 0.9402
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  ## Model description
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@@ -77,31 +77,31 @@ 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 | 223 | 0.2742 | 0.8893 | 0.8961 | 0.8927 | 0.9409 |
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- | No log | 2.0 | 446 | 0.2695 | 0.8916 | 0.9010 | 0.8963 | 0.9409 |
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- | 0.2524 | 3.0 | 669 | 0.2733 | 0.8996 | 0.8984 | 0.8990 | 0.9428 |
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- | 0.2524 | 4.0 | 892 | 0.2730 | 0.9014 | 0.9007 | 0.9011 | 0.9441 |
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- | 0.1608 | 5.0 | 1115 | 0.2728 | 0.8983 | 0.9043 | 0.9013 | 0.9459 |
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- | 0.1608 | 6.0 | 1338 | 0.2916 | 0.8845 | 0.9030 | 0.8936 | 0.9404 |
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- | 0.116 | 7.0 | 1561 | 0.2977 | 0.8895 | 0.9040 | 0.8967 | 0.9414 |
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- | 0.116 | 8.0 | 1784 | 0.3041 | 0.8981 | 0.9025 | 0.9003 | 0.9405 |
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- | 0.0826 | 9.0 | 2007 | 0.3257 | 0.8937 | 0.9033 | 0.8984 | 0.9423 |
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- | 0.0826 | 10.0 | 2230 | 0.3487 | 0.8875 | 0.9040 | 0.8957 | 0.9380 |
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- | 0.0826 | 11.0 | 2453 | 0.3561 | 0.8937 | 0.9058 | 0.8997 | 0.9396 |
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- | 0.0549 | 12.0 | 2676 | 0.3627 | 0.8996 | 0.9012 | 0.9004 | 0.9417 |
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- | 0.0549 | 13.0 | 2899 | 0.3655 | 0.9036 | 0.9094 | 0.9065 | 0.9411 |
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- | 0.0422 | 14.0 | 3122 | 0.3745 | 0.8940 | 0.9046 | 0.8993 | 0.9416 |
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- | 0.0422 | 15.0 | 3345 | 0.3941 | 0.9005 | 0.9058 | 0.9032 | 0.9384 |
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- | 0.031 | 16.0 | 3568 | 0.3886 | 0.9061 | 0.9040 | 0.9051 | 0.9408 |
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- | 0.031 | 17.0 | 3791 | 0.3912 | 0.9029 | 0.9061 | 0.9045 | 0.9425 |
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- | 0.0261 | 18.0 | 4014 | 0.3889 | 0.8939 | 0.9097 | 0.9017 | 0.9408 |
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- | 0.0261 | 19.0 | 4237 | 0.3917 | 0.8973 | 0.9097 | 0.9034 | 0.9404 |
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- | 0.0261 | 20.0 | 4460 | 0.3934 | 0.8961 | 0.9092 | 0.9026 | 0.9402 |
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  ### Framework versions
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  - Transformers 4.36.2
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  - Pytorch 2.1.0+cu121
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- - Datasets 2.15.0
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  - Tokenizers 0.15.0
 
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  metrics:
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  - name: Precision
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  type: precision
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+ value: 0.9134177215189874
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  - name: Recall
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  type: recall
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+ value: 0.9168996188055909
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  - name: F1
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  type: f1
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+ value: 0.9151553582752061
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9556322655972385
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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: 0.3141
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+ - Precision: 0.9134
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+ - Recall: 0.9169
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+ - F1: 0.9152
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+ - Accuracy: 0.9556
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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 | 252 | 0.2193 | 0.9026 | 0.8948 | 0.8987 | 0.9488 |
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+ | 0.2496 | 2.0 | 504 | 0.2110 | 0.8957 | 0.9098 | 0.9027 | 0.9494 |
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+ | 0.2496 | 3.0 | 756 | 0.2098 | 0.9166 | 0.9105 | 0.9136 | 0.9531 |
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+ | 0.1666 | 4.0 | 1008 | 0.2063 | 0.9221 | 0.9146 | 0.9183 | 0.9559 |
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+ | 0.1666 | 5.0 | 1260 | 0.2165 | 0.9219 | 0.9146 | 0.9182 | 0.9562 |
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+ | 0.1255 | 6.0 | 1512 | 0.2143 | 0.9175 | 0.9133 | 0.9154 | 0.9555 |
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+ | 0.1255 | 7.0 | 1764 | 0.2278 | 0.9181 | 0.9146 | 0.9164 | 0.9559 |
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+ | 0.092 | 8.0 | 2016 | 0.2404 | 0.9188 | 0.9174 | 0.9181 | 0.9561 |
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+ | 0.092 | 9.0 | 2268 | 0.2538 | 0.9133 | 0.9100 | 0.9117 | 0.9533 |
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+ | 0.069 | 10.0 | 2520 | 0.2654 | 0.9132 | 0.9118 | 0.9125 | 0.9543 |
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+ | 0.069 | 11.0 | 2772 | 0.2796 | 0.9085 | 0.9133 | 0.9109 | 0.9527 |
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+ | 0.0498 | 12.0 | 3024 | 0.2827 | 0.9130 | 0.9149 | 0.9139 | 0.9552 |
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+ | 0.0498 | 13.0 | 3276 | 0.2869 | 0.9127 | 0.9144 | 0.9135 | 0.9557 |
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+ | 0.0397 | 14.0 | 3528 | 0.2993 | 0.9123 | 0.9093 | 0.9108 | 0.9546 |
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+ | 0.0397 | 15.0 | 3780 | 0.2951 | 0.9056 | 0.9144 | 0.9100 | 0.9547 |
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+ | 0.0312 | 16.0 | 4032 | 0.2989 | 0.9092 | 0.9136 | 0.9114 | 0.9566 |
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+ | 0.0312 | 17.0 | 4284 | 0.3104 | 0.9115 | 0.9113 | 0.9114 | 0.9554 |
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+ | 0.0257 | 18.0 | 4536 | 0.3098 | 0.9143 | 0.9161 | 0.9152 | 0.9564 |
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+ | 0.0257 | 19.0 | 4788 | 0.3129 | 0.9141 | 0.9166 | 0.9154 | 0.9556 |
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+ | 0.0207 | 20.0 | 5040 | 0.3141 | 0.9134 | 0.9169 | 0.9152 | 0.9556 |
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101
 
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  ### Framework versions
103
 
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  - Transformers 4.36.2
105
  - Pytorch 2.1.0+cu121
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+ - Datasets 2.16.0
107
  - Tokenizers 0.15.0
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