---
license: mit
tags:
- generated_from_trainer
datasets:
- lener_br
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: bertimbau-large-lener_br
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: lener_br
      type: lener_br
      args: lener_br
    metric:
      name: Accuracy
      type: accuracy
      value: 0.9744464930592591
---

<!-- 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. -->

# bertimbau-large-lener_br

This model is a fine-tuned version of [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased) on the lener_br dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1193
- Precision: 0.8287
- Recall: 0.9144
- F1: 0.8694
- Accuracy: 0.9744

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0586        | 1.0   | 1957 | 0.1409          | 0.8317    | 0.8725 | 0.8516 | 0.9667   |
| 0.0304        | 2.0   | 3914 | 0.1338          | 0.7137    | 0.9172 | 0.8027 | 0.9619   |
| 0.0119        | 3.0   | 5871 | 0.1193          | 0.8287    | 0.9144 | 0.8694 | 0.9744   |


### Framework versions

- Transformers 4.8.2
- Pytorch 1.9.0+cu102
- Datasets 1.9.0
- Tokenizers 0.10.3