---
license: mit
base_model: neuralmind/bert-base-portuguese-cased
tags:
- generated_from_trainer
datasets:
- lener_br
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-portuguese-cased-finetuned-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: lener_br
      type: lener_br
      config: lener_br
      split: test
      args: lener_br
    metrics:
    - name: Precision
      type: precision
      value: 0.8717564870259481
    - name: Recall
      type: recall
      value: 0.8995880535530381
    - name: F1
      type: f1
      value: 0.88545362392296
    - name: Accuracy
      type: accuracy
      value: 0.9836487420412604
---

<!-- 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. -->

# bert-base-portuguese-cased-finetuned-ner

This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the lener_br dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0652
- Precision: 0.8718
- Recall: 0.8996
- F1: 0.8855
- Accuracy: 0.9836

## 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: 16
- eval_batch_size: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 490  | 0.0911          | 0.8063    | 0.7703 | 0.7879 | 0.9734   |
| 0.1901        | 2.0   | 980  | 0.0665          | 0.8525    | 0.8929 | 0.8722 | 0.9819   |
| 0.0419        | 3.0   | 1470 | 0.0652          | 0.8718    | 0.8996 | 0.8855 | 0.9836   |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1