---
license: mit
tags:
- generated_from_trainer
datasets:
- tapaco
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: punctuation-taboa-bert
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: tapaco
      type: tapaco
      config: all_languages
      split: train
      args: all_languages
    metrics:
    - name: Precision
      type: precision
      value: 0.9849559686888454
    - name: Recall
      type: recall
      value: 0.9836325882496642
    - name: F1
      type: f1
      value: 0.9842938336490864
    - name: Accuracy
      type: accuracy
      value: 0.9945622875893589
---

<!-- 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-taboa-bert

This model is a fine-tuned version of [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased) on the tapaco dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0181
- Precision: 0.9850
- Recall: 0.9836
- F1: 0.9843
- Accuracy: 0.9946

## 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: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0272        | 1.0   | 17438 | 0.0181          | 0.9850    | 0.9836 | 0.9843 | 0.9946   |
| 0.0234        | 2.0   | 34876 | 0.0196          | 0.9870    | 0.9853 | 0.9862 | 0.9948   |
| 0.0092        | 3.0   | 52314 | 0.0233          | 0.9874    | 0.9853 | 0.9864 | 0.9950   |


### Framework versions

- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.13.1