---
license: apache-2.0
base_model: distilbert/distilbert-base-multilingual-cased
tags:
- generated_from_trainer
datasets:
- lener_br
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-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: 0.7959714100064977
    - name: Recall
      type: recall
      value: 0.7847533632286996
    - name: F1
      type: f1
      value: 0.7903225806451614
    - name: Accuracy
      type: accuracy
      value: 0.959060823521215
---

<!-- 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. -->

# distilbert-base-multilingual-cased-finetuned-ner-lenerBr

This model is a fine-tuned version of [distilbert/distilbert-base-multilingual-cased](https://huggingface.co/distilbert/distilbert-base-multilingual-cased) on the lener_br dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1904
- Precision: 0.7960
- Recall: 0.7848
- F1: 0.7903
- Accuracy: 0.9591

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 490  | 0.2124          | 0.6794    | 0.6842 | 0.6818 | 0.9363   |
| 0.2601        | 2.0   | 980  | 0.1744          | 0.701     | 0.7485 | 0.7239 | 0.9486   |
| 0.0688        | 3.0   | 1470 | 0.1653          | 0.7344    | 0.7598 | 0.7469 | 0.9522   |
| 0.0375        | 4.0   | 1960 | 0.1868          | 0.7764    | 0.7429 | 0.7593 | 0.9546   |
| 0.0229        | 5.0   | 2450 | 0.1844          | 0.7748    | 0.7854 | 0.7801 | 0.9560   |
| 0.0162        | 6.0   | 2940 | 0.2072          | 0.6896    | 0.7929 | 0.7377 | 0.9462   |
| 0.0123        | 7.0   | 3430 | 0.1941          | 0.7612    | 0.7704 | 0.7658 | 0.9548   |
| 0.0078        | 8.0   | 3920 | 0.1900          | 0.7701    | 0.7909 | 0.7804 | 0.9581   |
| 0.0068        | 9.0   | 4410 | 0.1884          | 0.8000    | 0.7822 | 0.7910 | 0.9593   |
| 0.0045        | 10.0  | 4900 | 0.1904          | 0.7960    | 0.7848 | 0.7903 | 0.9591   |


### Framework versions

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