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finetuned-bert-categories-estimation

Text Classification
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bert
Generated from Trainer
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  1. README.md +92 -1
  2. model.safetensors +1 -1
  3. runs/Dec16_00-28-07_13df8a6ea8ec/events.out.tfevents.1702686502.13df8a6ea8ec.296.0 +2 -2
  4. runs/Dec16_00-28-07_13df8a6ea8ec/events.out.tfevents.1702691624.13df8a6ea8ec.296.1 +3 -0
README.md CHANGED
@@ -3,6 +3,9 @@ license: mit
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  base_model: neuralmind/bert-base-portuguese-cased
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  tags:
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  - generated_from_trainer
 
 
 
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  model-index:
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  - name: finetuned-bert-categories-estimation
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  results: []
@@ -14,6 +17,10 @@ should probably proofread and complete it, then remove this comment. -->
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  # finetuned-bert-categories-estimation
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  This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the None dataset.
 
 
 
 
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  ## Model description
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@@ -38,7 +45,91 @@ The following hyperparameters were used during training:
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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- - num_epochs: 4
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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  base_model: neuralmind/bert-base-portuguese-cased
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  tags:
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  - generated_from_trainer
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+ metrics:
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+ - f1
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+ - accuracy
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  model-index:
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  - name: finetuned-bert-categories-estimation
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  results: []
 
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  # finetuned-bert-categories-estimation
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  This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.4080
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+ - F1: 0.9054
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+ - Accuracy: 0.9277
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  ## Model description
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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+ - num_epochs: 10
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
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+ | 4.3517 | 0.13 | 100 | 3.6120 | 0.0934 | 0.3599 |
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+ | 3.2593 | 0.25 | 200 | 2.7209 | 0.1848 | 0.5085 |
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+ | 2.584 | 0.38 | 300 | 2.1874 | 0.2784 | 0.5952 |
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+ | 2.1208 | 0.51 | 400 | 1.8393 | 0.3656 | 0.6504 |
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+ | 1.7726 | 0.63 | 500 | 1.5560 | 0.4633 | 0.7121 |
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+ | 1.5799 | 0.76 | 600 | 1.3686 | 0.5185 | 0.7442 |
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+ | 1.3384 | 0.89 | 700 | 1.2079 | 0.5896 | 0.7759 |
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+ | 1.2751 | 1.01 | 800 | 1.0736 | 0.6044 | 0.7941 |
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+ | 1.0223 | 1.14 | 900 | 0.9892 | 0.6353 | 0.8089 |
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+ | 0.9095 | 1.27 | 1000 | 0.9277 | 0.6699 | 0.8157 |
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+ | 0.8496 | 1.39 | 1100 | 0.8370 | 0.6973 | 0.8311 |
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+ | 0.7735 | 1.52 | 1200 | 0.7878 | 0.7093 | 0.8349 |
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+ | 0.7058 | 1.65 | 1300 | 0.7299 | 0.7239 | 0.8481 |
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+ | 0.6545 | 1.77 | 1400 | 0.6823 | 0.7444 | 0.8563 |
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+ | 0.6652 | 1.9 | 1500 | 0.6623 | 0.7547 | 0.8609 |
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+ | 0.5905 | 2.03 | 1600 | 0.6079 | 0.7660 | 0.8663 |
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+ | 0.4679 | 2.15 | 1700 | 0.5910 | 0.7867 | 0.8696 |
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+ | 0.4415 | 2.28 | 1800 | 0.5668 | 0.8034 | 0.8785 |
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+ | 0.4377 | 2.41 | 1900 | 0.5580 | 0.8068 | 0.8796 |
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+ | 0.4262 | 2.53 | 2000 | 0.5366 | 0.8054 | 0.8815 |
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+ | 0.4272 | 2.66 | 2100 | 0.5094 | 0.8189 | 0.8880 |
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+ | 0.3979 | 2.79 | 2200 | 0.4966 | 0.8229 | 0.8898 |
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+ | 0.3763 | 2.92 | 2300 | 0.4838 | 0.8349 | 0.8950 |
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+ | 0.366 | 3.04 | 2400 | 0.4742 | 0.8340 | 0.8950 |
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+ | 0.2686 | 3.17 | 2500 | 0.4591 | 0.8365 | 0.8966 |
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+ | 0.2735 | 3.3 | 2600 | 0.4676 | 0.8393 | 0.8958 |
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+ | 0.2582 | 3.42 | 2700 | 0.4263 | 0.8580 | 0.9025 |
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+ | 0.2451 | 3.55 | 2800 | 0.4383 | 0.8526 | 0.8988 |
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+ | 0.2626 | 3.68 | 2900 | 0.4420 | 0.8554 | 0.9018 |
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+ | 0.248 | 3.8 | 3000 | 0.4153 | 0.8658 | 0.9080 |
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+ | 0.2634 | 3.93 | 3100 | 0.4082 | 0.8666 | 0.9088 |
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+ | 0.2 | 4.06 | 3200 | 0.4162 | 0.8716 | 0.9090 |
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+ | 0.1717 | 4.18 | 3300 | 0.4032 | 0.8748 | 0.9117 |
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+ | 0.19 | 4.31 | 3400 | 0.4019 | 0.8747 | 0.9117 |
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+ | 0.1507 | 4.44 | 3500 | 0.4118 | 0.8789 | 0.9139 |
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+ | 0.16 | 4.56 | 3600 | 0.4107 | 0.8815 | 0.9139 |
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+ | 0.1716 | 4.69 | 3700 | 0.4105 | 0.8826 | 0.9132 |
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+ | 0.1545 | 4.82 | 3800 | 0.3945 | 0.8850 | 0.9180 |
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+ | 0.1628 | 4.94 | 3900 | 0.3974 | 0.8907 | 0.9194 |
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+ | 0.1123 | 5.07 | 4000 | 0.4060 | 0.8828 | 0.9166 |
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+ | 0.0988 | 5.2 | 4100 | 0.4037 | 0.8847 | 0.9167 |
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+ | 0.1065 | 5.32 | 4200 | 0.3959 | 0.8895 | 0.9201 |
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+ | 0.1018 | 5.45 | 4300 | 0.4040 | 0.8875 | 0.9183 |
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+ | 0.1091 | 5.58 | 4400 | 0.4044 | 0.8908 | 0.9199 |
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+ | 0.1041 | 5.7 | 4500 | 0.3937 | 0.8943 | 0.9218 |
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+ | 0.1154 | 5.83 | 4600 | 0.3981 | 0.8956 | 0.9205 |
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+ | 0.0932 | 5.96 | 4700 | 0.3940 | 0.8967 | 0.9223 |
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+ | 0.0835 | 6.08 | 4800 | 0.3914 | 0.8967 | 0.9224 |
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+ | 0.065 | 6.21 | 4900 | 0.3905 | 0.8922 | 0.9215 |
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+ | 0.0634 | 6.34 | 5000 | 0.3999 | 0.8924 | 0.9215 |
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+ | 0.0618 | 6.46 | 5100 | 0.4013 | 0.8966 | 0.9226 |
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+ | 0.0678 | 6.59 | 5200 | 0.3985 | 0.9004 | 0.9242 |
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+ | 0.0666 | 6.72 | 5300 | 0.3892 | 0.8993 | 0.9239 |
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+ | 0.0564 | 6.84 | 5400 | 0.4026 | 0.8986 | 0.9228 |
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+ | 0.0704 | 6.97 | 5500 | 0.4011 | 0.9004 | 0.9236 |
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+ | 0.0508 | 7.1 | 5600 | 0.4035 | 0.8987 | 0.9234 |
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+ | 0.0395 | 7.22 | 5700 | 0.4131 | 0.8979 | 0.9216 |
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+ | 0.0363 | 7.35 | 5800 | 0.4112 | 0.9022 | 0.9243 |
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+ | 0.0443 | 7.48 | 5900 | 0.4079 | 0.9039 | 0.9251 |
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+ | 0.0383 | 7.6 | 6000 | 0.4152 | 0.9031 | 0.9248 |
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+ | 0.0384 | 7.73 | 6100 | 0.4075 | 0.9037 | 0.9258 |
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+ | 0.0414 | 7.86 | 6200 | 0.4087 | 0.9035 | 0.9256 |
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+ | 0.0449 | 7.98 | 6300 | 0.4066 | 0.9060 | 0.9262 |
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+ | 0.0246 | 8.11 | 6400 | 0.4091 | 0.9041 | 0.9258 |
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+ | 0.0275 | 8.24 | 6500 | 0.4085 | 0.9035 | 0.9262 |
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+ | 0.0256 | 8.37 | 6600 | 0.4077 | 0.9048 | 0.9269 |
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+ | 0.0307 | 8.49 | 6700 | 0.4040 | 0.9082 | 0.9285 |
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+ | 0.0294 | 8.62 | 6800 | 0.4057 | 0.9067 | 0.9283 |
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+ | 0.0226 | 8.75 | 6900 | 0.4069 | 0.9054 | 0.9274 |
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+ | 0.0218 | 8.87 | 7000 | 0.4090 | 0.9053 | 0.9278 |
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+ | 0.0333 | 9.0 | 7100 | 0.4053 | 0.9075 | 0.9286 |
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+ | 0.0182 | 9.13 | 7200 | 0.4071 | 0.9063 | 0.9277 |
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+ | 0.0176 | 9.25 | 7300 | 0.4058 | 0.9053 | 0.9278 |
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+ | 0.0187 | 9.38 | 7400 | 0.4074 | 0.9060 | 0.9280 |
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+ | 0.0185 | 9.51 | 7500 | 0.4069 | 0.9059 | 0.9278 |
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+ | 0.0135 | 9.63 | 7600 | 0.4067 | 0.9049 | 0.9275 |
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+ | 0.0118 | 9.76 | 7700 | 0.4076 | 0.9039 | 0.9267 |
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+ | 0.0163 | 9.89 | 7800 | 0.4081 | 0.9050 | 0.9275 |
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+
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  ### Framework versions
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