---
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: 0.7845931433292028
    - name: Recall
      type: recall
      value: 0.7810444078947368
    - name: F1
      type: f1
      value: 0.7828147537605605
    - name: Accuracy
      type: accuracy
      value: 0.9671762427683093
---

<!-- 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: 0.1555
- Precision: 0.7846
- Recall: 0.7810
- F1: 0.7828
- Accuracy: 0.9672

## 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  | 0.1861          | 0.6380    | 0.6661 | 0.6518 | 0.9446   |
| 0.2629        | 2.0   | 980  | 0.1618          | 0.7063    | 0.7303 | 0.7181 | 0.9537   |
| 0.0756        | 3.0   | 1470 | 0.1299          | 0.7299    | 0.8010 | 0.7638 | 0.9645   |
| 0.0443        | 4.0   | 1960 | 0.1422          | 0.7634    | 0.7708 | 0.7671 | 0.9643   |
| 0.0279        | 5.0   | 2450 | 0.1508          | 0.7870    | 0.7679 | 0.7773 | 0.9648   |
| 0.0203        | 6.0   | 2940 | 0.1457          | 0.7693    | 0.7815 | 0.7753 | 0.9681   |
| 0.0143        | 7.0   | 3430 | 0.1508          | 0.7767    | 0.7714 | 0.7740 | 0.9663   |
| 0.0105        | 8.0   | 3920 | 0.1537          | 0.7812    | 0.7669 | 0.7739 | 0.9671   |
| 0.0085        | 9.0   | 4410 | 0.1564          | 0.7809    | 0.7681 | 0.7745 | 0.9669   |
| 0.0064        | 10.0  | 4900 | 0.1555          | 0.7846    | 0.7810 | 0.7828 | 0.9672   |


### Framework versions

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