---
library_name: transformers
license: mit
base_model: neuralmind/bert-base-portuguese-cased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: TOTVS_Churn_Risk_V2
  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. -->

# TOTVS_Churn_Risk_V2

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: 0.2512
- Accuracy: 0.9375
- F1: 0.9378
- Precision: 0.9608
- Recall: 0.9159

## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.2545        | 1.0   | 52   | 0.9010          | 0.8317   | 0.8523 | 0.7769    | 0.9439 |
| 0.5754        | 2.0   | 104  | 0.4523          | 0.9087   | 0.9073 | 0.9490    | 0.8692 |
| 0.4382        | 3.0   | 156  | 0.2997          | 0.9327   | 0.9346 | 0.9346    | 0.9346 |
| 0.2150        | 4.0   | 208  | 0.2800          | 0.9327   | 0.9320 | 0.9697    | 0.8972 |
| 0.1737        | 5.0   | 260  | 0.2512          | 0.9375   | 0.9378 | 0.9608    | 0.9159 |


### Framework versions

- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.8.3
- Tokenizers 0.22.2