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---
library_name: transformers
license: mit
base_model: neuralmind/bert-base-portuguese-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: e3_lr2e-05
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# e3_lr2e-05
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: 0.0753
- Precision: 0.9611
- Recall: 0.9778
- F1: 0.9694
- Accuracy: 0.9817
## 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: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4195 | 0.2564 | 50 | 0.2315 | 0.8642 | 0.8460 | 0.8550 | 0.9499 |
| 0.2396 | 0.5128 | 100 | 0.1778 | 0.8971 | 0.8970 | 0.8970 | 0.9517 |
| 0.1717 | 0.7692 | 150 | 0.1330 | 0.9033 | 0.9323 | 0.9176 | 0.9639 |
| 0.1249 | 1.0256 | 200 | 0.1090 | 0.9369 | 0.9554 | 0.9460 | 0.9728 |
| 0.0929 | 1.2821 | 250 | 0.1066 | 0.9397 | 0.9630 | 0.9512 | 0.9739 |
| 0.0954 | 1.5385 | 300 | 0.0831 | 0.9498 | 0.9670 | 0.9583 | 0.9788 |
| 0.0858 | 1.7949 | 350 | 0.0844 | 0.9459 | 0.9727 | 0.9591 | 0.9776 |
| 0.0715 | 2.0513 | 400 | 0.0868 | 0.9512 | 0.9766 | 0.9637 | 0.9796 |
| 0.056 | 2.3077 | 450 | 0.0789 | 0.9616 | 0.9774 | 0.9695 | 0.9818 |
| 0.0592 | 2.5641 | 500 | 0.0768 | 0.9614 | 0.9783 | 0.9698 | 0.9817 |
| 0.0607 | 2.8205 | 550 | 0.0753 | 0.9611 | 0.9778 | 0.9694 | 0.9817 |
### Framework versions
- Transformers 4.45.0
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.20.0
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