---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- lener_br
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ner_bert_model
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: lener_br
      type: lener_br
      config: lener_br
      split: test
      args: lener_br
    metrics:
    - name: Precision
      type: precision
      value: 0.828094932649134
    - name: Recall
      type: recall
      value: 0.8532716457369465
    - name: F1
      type: f1
      value: 0.8404947916666666
    - name: Accuracy
      type: accuracy
      value: 0.9840912469998513
---

<!-- 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. -->

# ner_bert_model

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the lener_br dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0922
- Precision: 0.8281
- Recall: 0.8533
- F1: 0.8405
- Accuracy: 0.9841

## 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 490  | 0.0971          | 0.6373    | 0.7607 | 0.6936 | 0.9706   |
| 0.2449        | 2.0   | 980  | 0.0820          | 0.6916    | 0.8063 | 0.7446 | 0.9760   |
| 0.0634        | 3.0   | 1470 | 0.0750          | 0.7106    | 0.8473 | 0.7730 | 0.9778   |
| 0.0352        | 4.0   | 1960 | 0.0707          | 0.7690    | 0.8361 | 0.8011 | 0.9799   |
| 0.0226        | 5.0   | 2450 | 0.0812          | 0.8063    | 0.8394 | 0.8225 | 0.9821   |
| 0.0157        | 6.0   | 2940 | 0.0779          | 0.7931    | 0.8486 | 0.8199 | 0.9826   |
| 0.0105        | 7.0   | 3430 | 0.0958          | 0.7314    | 0.8586 | 0.7899 | 0.9779   |
| 0.0082        | 8.0   | 3920 | 0.0810          | 0.8158    | 0.8460 | 0.8306 | 0.9829   |
| 0.0067        | 9.0   | 4410 | 0.0830          | 0.8190    | 0.8526 | 0.8355 | 0.9832   |
| 0.0054        | 10.0  | 4900 | 0.0810          | 0.8165    | 0.8500 | 0.8329 | 0.9833   |
| 0.0051        | 11.0  | 5390 | 0.0855          | 0.8180    | 0.8493 | 0.8333 | 0.9832   |
| 0.0037        | 12.0  | 5880 | 0.0862          | 0.8195    | 0.8519 | 0.8354 | 0.9841   |
| 0.0034        | 13.0  | 6370 | 0.0867          | 0.8165    | 0.8586 | 0.8370 | 0.9833   |
| 0.0027        | 14.0  | 6860 | 0.0922          | 0.8214    | 0.8420 | 0.8316 | 0.9832   |
| 0.0024        | 15.0  | 7350 | 0.0910          | 0.8147    | 0.8486 | 0.8313 | 0.9836   |
| 0.002         | 16.0  | 7840 | 0.0928          | 0.8191    | 0.8559 | 0.8371 | 0.9840   |
| 0.0018        | 17.0  | 8330 | 0.0928          | 0.8119    | 0.8559 | 0.8333 | 0.9834   |
| 0.0017        | 18.0  | 8820 | 0.0920          | 0.8228    | 0.8592 | 0.8406 | 0.9838   |
| 0.0015        | 19.0  | 9310 | 0.0919          | 0.8242    | 0.8553 | 0.8394 | 0.9837   |
| 0.0011        | 20.0  | 9800 | 0.0922          | 0.8281    | 0.8533 | 0.8405 | 0.9841   |


### Framework versions

- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1