---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: punctuation-nilc-bert-large
  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. -->

# punctuation-nilc-bert-large

This model is a fine-tuned version of [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1585
- Precision: 0.9053
- Recall: 0.8923
- F1: 0.8988
- Accuracy: 0.9755

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0742        | 1.0   | 1172 | 0.0653          | 0.9194    | 0.8702 | 0.8941 | 0.9742   |
| 0.0396        | 2.0   | 2344 | 0.0773          | 0.9088    | 0.8834 | 0.8959 | 0.9748   |
| 0.0153        | 3.0   | 3516 | 0.1171          | 0.8996    | 0.8817 | 0.8906 | 0.9739   |
| 0.0059        | 4.0   | 4688 | 0.1390          | 0.9174    | 0.8719 | 0.8941 | 0.9747   |
| 0.0024        | 5.0   | 5860 | 0.1585          | 0.9053    | 0.8923 | 0.8988 | 0.9755   |


### Framework versions

- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2