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--- |
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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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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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--- |
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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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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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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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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 8 |
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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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### Training results |
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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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### Framework versions |
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- Transformers 4.35.2 |
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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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