---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- lener_br
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-cased-finetuned-ner
  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: 0.7640519805855644
    - name: Recall
      type: recall
      value: 0.818242790073776
    - name: F1
      type: f1
      value: 0.7902194154319487
    - name: Accuracy
      type: accuracy
      value: 0.9615441099339138
---

<!-- 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. -->

# bert-base-cased-finetuned-ner

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the lener_br dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Precision: 0.7641
- Recall: 0.8182
- F1: 0.7902
- Accuracy: 0.9615

## 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: 8
- eval_batch_size: 8
- 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   | 432  | nan             | 0.6807    | 0.7773 | 0.7258 | 0.9450   |
| 0.3019        | 2.0   | 864  | nan             | 0.7244    | 0.7725 | 0.7476 | 0.9531   |
| 0.0871        | 3.0   | 1296 | nan             | 0.7352    | 0.8192 | 0.7749 | 0.9571   |
| 0.0527        | 4.0   | 1728 | nan             | 0.7455    | 0.7864 | 0.7654 | 0.9557   |
| 0.031         | 5.0   | 2160 | nan             | 0.7334    | 0.7976 | 0.7642 | 0.9544   |
| 0.0223        | 6.0   | 2592 | nan             | 0.7703    | 0.8343 | 0.8010 | 0.9624   |
| 0.0171        | 7.0   | 3024 | nan             | 0.7279    | 0.8119 | 0.7676 | 0.9569   |
| 0.0171        | 8.0   | 3456 | nan             | 0.7609    | 0.8067 | 0.7831 | 0.9613   |
| 0.012         | 9.0   | 3888 | nan             | 0.7585    | 0.8152 | 0.7858 | 0.9608   |
| 0.0097        | 10.0  | 4320 | nan             | 0.7641    | 0.8182 | 0.7902 | 0.9615   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0