---
license: apache-2.0
base_model: google-bert/bert-base-multilingual-cased
tags:
- generated_from_trainer
datasets:
- lener_br
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-multilingual-cased-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: 0.8457276795226933
    - name: Recall
      type: recall
      value: 0.8475336322869955
    - name: F1
      type: f1
      value: 0.8466296928327645
    - name: Accuracy
      type: accuracy
      value: 0.9641886713579043
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# bert-base-multilingual-cased-finetuned-ner-lenerBR

This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) on the lener_br dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1941
- Precision: 0.8457
- Recall: 0.8475
- F1: 0.8466
- Accuracy: 0.9642

## 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  | 0.2100          | 0.7326    | 0.7596 | 0.7459 | 0.9478   |
| No log        | 2.0   | 490  | 0.1885          | 0.7737    | 0.8119 | 0.7923 | 0.9548   |
| 0.1595        | 3.0   | 735  | 0.1491          | 0.8056    | 0.8388 | 0.8218 | 0.9616   |
| 0.1595        | 4.0   | 980  | 0.1787          | 0.8369    | 0.8251 | 0.8310 | 0.9612   |
| 0.0311        | 5.0   | 1225 | 0.1788          | 0.8303    | 0.8601 | 0.8450 | 0.9646   |
| 0.0311        | 6.0   | 1470 | 0.2131          | 0.7985    | 0.8463 | 0.8217 | 0.9595   |
| 0.0156        | 7.0   | 1715 | 0.1879          | 0.8161    | 0.8635 | 0.8392 | 0.9630   |
| 0.0156        | 8.0   | 1960 | 0.1975          | 0.8445    | 0.8469 | 0.8457 | 0.9636   |
| 0.0091        | 9.0   | 2205 | 0.1979          | 0.8460    | 0.8422 | 0.8441 | 0.9635   |
| 0.0091        | 10.0  | 2450 | 0.1941          | 0.8457    | 0.8475 | 0.8466 | 0.9642   |


### Framework versions

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