---
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- lener_br
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-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: 0.7477750426055672
    - name: Recall
      type: recall
      value: 0.8118832236842105
    - name: F1
      type: f1
      value: 0.7785115820601283
    - name: Accuracy
      type: accuracy
      value: 0.9644699967525048
---

<!-- 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-uncased-finetuned-ner-lenerBr

This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the lener_br dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1546
- Precision: 0.7478
- Recall: 0.8119
- F1: 0.7785
- Accuracy: 0.9645

## 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  | 0.2131          | 0.6201    | 0.6604 | 0.6396 | 0.9359   |
| 0.264         | 2.0   | 980  | 0.1828          | 0.7004    | 0.7504 | 0.7246 | 0.9508   |
| 0.0776        | 3.0   | 1470 | 0.1564          | 0.6582    | 0.8137 | 0.7278 | 0.9537   |
| 0.0437        | 4.0   | 1960 | 0.1644          | 0.7485    | 0.7623 | 0.7553 | 0.9573   |
| 0.0288        | 5.0   | 2450 | 0.1555          | 0.7620    | 0.7662 | 0.7641 | 0.9614   |
| 0.0208        | 6.0   | 2940 | 0.1874          | 0.7530    | 0.7759 | 0.7643 | 0.9550   |
| 0.0143        | 7.0   | 3430 | 0.1546          | 0.7478    | 0.8119 | 0.7785 | 0.9645   |
| 0.0117        | 8.0   | 3920 | 0.1717          | 0.7014    | 0.7677 | 0.7330 | 0.9592   |
| 0.0102        | 9.0   | 4410 | 0.1884          | 0.7734    | 0.7714 | 0.7724 | 0.9613   |


### Framework versions

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