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# finetuned-bert-categories-estimation

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.
It achieves the following results on the evaluation set:
- Loss: 0.4080
- F1: 0.9054
- Accuracy: 0.9277

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
| 4.3517        | 0.13  | 100  | 3.6120          | 0.0934 | 0.3599   |
| 3.2593        | 0.25  | 200  | 2.7209          | 0.1848 | 0.5085   |
| 2.584         | 0.38  | 300  | 2.1874          | 0.2784 | 0.5952   |
| 2.1208        | 0.51  | 400  | 1.8393          | 0.3656 | 0.6504   |
| 1.7726        | 0.63  | 500  | 1.5560          | 0.4633 | 0.7121   |
| 1.5799        | 0.76  | 600  | 1.3686          | 0.5185 | 0.7442   |
| 1.3384        | 0.89  | 700  | 1.2079          | 0.5896 | 0.7759   |
| 1.2751        | 1.01  | 800  | 1.0736          | 0.6044 | 0.7941   |
| 1.0223        | 1.14  | 900  | 0.9892          | 0.6353 | 0.8089   |
| 0.9095        | 1.27  | 1000 | 0.9277          | 0.6699 | 0.8157   |
| 0.8496        | 1.39  | 1100 | 0.8370          | 0.6973 | 0.8311   |
| 0.7735        | 1.52  | 1200 | 0.7878          | 0.7093 | 0.8349   |
| 0.7058        | 1.65  | 1300 | 0.7299          | 0.7239 | 0.8481   |
| 0.6545        | 1.77  | 1400 | 0.6823          | 0.7444 | 0.8563   |
| 0.6652        | 1.9   | 1500 | 0.6623          | 0.7547 | 0.8609   |
| 0.5905        | 2.03  | 1600 | 0.6079          | 0.7660 | 0.8663   |
| 0.4679        | 2.15  | 1700 | 0.5910          | 0.7867 | 0.8696   |
| 0.4415        | 2.28  | 1800 | 0.5668          | 0.8034 | 0.8785   |
| 0.4377        | 2.41  | 1900 | 0.5580          | 0.8068 | 0.8796   |
| 0.4262        | 2.53  | 2000 | 0.5366          | 0.8054 | 0.8815   |
| 0.4272        | 2.66  | 2100 | 0.5094          | 0.8189 | 0.8880   |
| 0.3979        | 2.79  | 2200 | 0.4966          | 0.8229 | 0.8898   |
| 0.3763        | 2.92  | 2300 | 0.4838          | 0.8349 | 0.8950   |
| 0.366         | 3.04  | 2400 | 0.4742          | 0.8340 | 0.8950   |
| 0.2686        | 3.17  | 2500 | 0.4591          | 0.8365 | 0.8966   |
| 0.2735        | 3.3   | 2600 | 0.4676          | 0.8393 | 0.8958   |
| 0.2582        | 3.42  | 2700 | 0.4263          | 0.8580 | 0.9025   |
| 0.2451        | 3.55  | 2800 | 0.4383          | 0.8526 | 0.8988   |
| 0.2626        | 3.68  | 2900 | 0.4420          | 0.8554 | 0.9018   |
| 0.248         | 3.8   | 3000 | 0.4153          | 0.8658 | 0.9080   |
| 0.2634        | 3.93  | 3100 | 0.4082          | 0.8666 | 0.9088   |
| 0.2           | 4.06  | 3200 | 0.4162          | 0.8716 | 0.9090   |
| 0.1717        | 4.18  | 3300 | 0.4032          | 0.8748 | 0.9117   |
| 0.19          | 4.31  | 3400 | 0.4019          | 0.8747 | 0.9117   |
| 0.1507        | 4.44  | 3500 | 0.4118          | 0.8789 | 0.9139   |
| 0.16          | 4.56  | 3600 | 0.4107          | 0.8815 | 0.9139   |
| 0.1716        | 4.69  | 3700 | 0.4105          | 0.8826 | 0.9132   |
| 0.1545        | 4.82  | 3800 | 0.3945          | 0.8850 | 0.9180   |
| 0.1628        | 4.94  | 3900 | 0.3974          | 0.8907 | 0.9194   |
| 0.1123        | 5.07  | 4000 | 0.4060          | 0.8828 | 0.9166   |
| 0.0988        | 5.2   | 4100 | 0.4037          | 0.8847 | 0.9167   |
| 0.1065        | 5.32  | 4200 | 0.3959          | 0.8895 | 0.9201   |
| 0.1018        | 5.45  | 4300 | 0.4040          | 0.8875 | 0.9183   |
| 0.1091        | 5.58  | 4400 | 0.4044          | 0.8908 | 0.9199   |
| 0.1041        | 5.7   | 4500 | 0.3937          | 0.8943 | 0.9218   |
| 0.1154        | 5.83  | 4600 | 0.3981          | 0.8956 | 0.9205   |
| 0.0932        | 5.96  | 4700 | 0.3940          | 0.8967 | 0.9223   |
| 0.0835        | 6.08  | 4800 | 0.3914          | 0.8967 | 0.9224   |
| 0.065         | 6.21  | 4900 | 0.3905          | 0.8922 | 0.9215   |
| 0.0634        | 6.34  | 5000 | 0.3999          | 0.8924 | 0.9215   |
| 0.0618        | 6.46  | 5100 | 0.4013          | 0.8966 | 0.9226   |
| 0.0678        | 6.59  | 5200 | 0.3985          | 0.9004 | 0.9242   |
| 0.0666        | 6.72  | 5300 | 0.3892          | 0.8993 | 0.9239   |
| 0.0564        | 6.84  | 5400 | 0.4026          | 0.8986 | 0.9228   |
| 0.0704        | 6.97  | 5500 | 0.4011          | 0.9004 | 0.9236   |
| 0.0508        | 7.1   | 5600 | 0.4035          | 0.8987 | 0.9234   |
| 0.0395        | 7.22  | 5700 | 0.4131          | 0.8979 | 0.9216   |
| 0.0363        | 7.35  | 5800 | 0.4112          | 0.9022 | 0.9243   |
| 0.0443        | 7.48  | 5900 | 0.4079          | 0.9039 | 0.9251   |
| 0.0383        | 7.6   | 6000 | 0.4152          | 0.9031 | 0.9248   |
| 0.0384        | 7.73  | 6100 | 0.4075          | 0.9037 | 0.9258   |
| 0.0414        | 7.86  | 6200 | 0.4087          | 0.9035 | 0.9256   |
| 0.0449        | 7.98  | 6300 | 0.4066          | 0.9060 | 0.9262   |
| 0.0246        | 8.11  | 6400 | 0.4091          | 0.9041 | 0.9258   |
| 0.0275        | 8.24  | 6500 | 0.4085          | 0.9035 | 0.9262   |
| 0.0256        | 8.37  | 6600 | 0.4077          | 0.9048 | 0.9269   |
| 0.0307        | 8.49  | 6700 | 0.4040          | 0.9082 | 0.9285   |
| 0.0294        | 8.62  | 6800 | 0.4057          | 0.9067 | 0.9283   |
| 0.0226        | 8.75  | 6900 | 0.4069          | 0.9054 | 0.9274   |
| 0.0218        | 8.87  | 7000 | 0.4090          | 0.9053 | 0.9278   |
| 0.0333        | 9.0   | 7100 | 0.4053          | 0.9075 | 0.9286   |
| 0.0182        | 9.13  | 7200 | 0.4071          | 0.9063 | 0.9277   |
| 0.0176        | 9.25  | 7300 | 0.4058          | 0.9053 | 0.9278   |
| 0.0187        | 9.38  | 7400 | 0.4074          | 0.9060 | 0.9280   |
| 0.0185        | 9.51  | 7500 | 0.4069          | 0.9059 | 0.9278   |
| 0.0135        | 9.63  | 7600 | 0.4067          | 0.9049 | 0.9275   |
| 0.0118        | 9.76  | 7700 | 0.4076          | 0.9039 | 0.9267   |
| 0.0163        | 9.89  | 7800 | 0.4081          | 0.9050 | 0.9275   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1", "accuracy"], "base_model": "neuralmind/bert-base-portuguese-cased", "model-index": [{"name": "finetuned-bert-categories-estimation", "results": []}]}
transformers tensorboard safetensors bert text-classification generated_from_trainer base_model:neuralmind/bert-base-portuguese-cased license:mit autotrain_compatible endpoints_compatible region:us