---
license: apache-2.0
base_model: google-bert/bert-base-multilingual-uncased
tags:
- generated_from_trainer
datasets:
- lener_br
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-multilingual-uncased-finetuned-ner-lenerBR
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: lener_br
      type: lener_br
      config: lener_br
      split: validation
      args: lener_br
    metrics:
    - name: Precision
      type: precision
      value: 0.8678256070640177
    - name: Recall
      type: recall
      value: 0.8758006126427179
    - name: F1
      type: f1
      value: 0.8717948717948718
    - name: Accuracy
      type: accuracy
      value: 0.9706569722150091
---

<!-- 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-multilingual-uncased-finetuned-ner-lenerBR

This model is a fine-tuned version of [google-bert/bert-base-multilingual-uncased](https://huggingface.co/google-bert/bert-base-multilingual-uncased) on the lener_br dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1568
- Precision: 0.8678
- Recall: 0.8758
- F1: 0.8718
- Accuracy: 0.9707

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 245  | 0.1819          | 0.7691    | 0.8118 | 0.7899 | 0.9585   |
| No log        | 2.0   | 490  | 0.1487          | 0.7383    | 0.8098 | 0.7724 | 0.9586   |
| 0.1325        | 3.0   | 735  | 0.1532          | 0.8662    | 0.8777 | 0.8719 | 0.9683   |
| 0.1325        | 4.0   | 980  | 0.1470          | 0.8770    | 0.8800 | 0.8785 | 0.9698   |
| 0.0233        | 5.0   | 1225 | 0.1155          | 0.8493    | 0.8839 | 0.8663 | 0.9750   |
| 0.0233        | 6.0   | 1470 | 0.1727          | 0.8874    | 0.8822 | 0.8848 | 0.9701   |
| 0.0126        | 7.0   | 1715 | 0.1698          | 0.8890    | 0.8853 | 0.8871 | 0.9710   |
| 0.0126        | 8.0   | 1960 | 0.1687          | 0.8651    | 0.8783 | 0.8716 | 0.9702   |
| 0.0076        | 9.0   | 2205 | 0.1593          | 0.8077    | 0.8797 | 0.8422 | 0.9668   |
| 0.0076        | 10.0  | 2450 | 0.1568          | 0.8678    | 0.8758 | 0.8718 | 0.9707   |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.1.2
- Datasets 2.19.1
- Tokenizers 0.19.1