---
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: 0.761528608027327
    - name: Recall
      type: recall
      value: 0.7616912235746316
    - name: F1
      type: f1
      value: 0.7616099071207431
    - name: Accuracy
      type: accuracy
      value: 0.9554657562878841
---

<!-- 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: 0.1792
- Precision: 0.7615
- Recall: 0.7617
- F1: 0.7616
- Accuracy: 0.9555

## 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: 100

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 490  | 0.2100          | 0.7139    | 0.6624 | 0.6872 | 0.9394   |
| 0.2608        | 2.0   | 980  | 0.1962          | 0.7059    | 0.7508 | 0.7276 | 0.9443   |
| 0.0681        | 3.0   | 1470 | 0.1858          | 0.7225    | 0.7649 | 0.7431 | 0.9486   |
| 0.0382        | 4.0   | 1960 | 0.1792          | 0.7615    | 0.7617 | 0.7616 | 0.9555   |
| 0.0248        | 5.0   | 2450 | 0.2068          | 0.7715    | 0.8149 | 0.7926 | 0.9560   |
| 0.0173        | 6.0   | 2940 | 0.2029          | 0.7112    | 0.8031 | 0.7544 | 0.9529   |


### Framework versions

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