---
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: 0.8649122807017544
    - name: Recall
      type: recall
      value: 0.8885169927909372
    - name: F1
      type: f1
      value: 0.8765557531115061
    - name: Accuracy
      type: accuracy
      value: 0.9821930095431353
---

<!-- 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: 0.0679
- Precision: 0.8649
- Recall: 0.8885
- F1: 0.8766
- Accuracy: 0.9822

## 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  | 0.0795          | 0.8185    | 0.7907 | 0.8043 | 0.9753   |
| 0.1925        | 2.0   | 980  | 0.0683          | 0.8475    | 0.8602 | 0.8538 | 0.9803   |
| 0.0422        | 3.0   | 1470 | 0.0679          | 0.8649    | 0.8885 | 0.8766 | 0.9822   |


### Framework versions

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