---
license: mit
base_model: neuralmind/bert-base-portuguese-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: nees-bert-base-portuguese-cased-finetuned-ner
  results: []
---

<!-- 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. -->

# nees-bert-base-portuguese-cased-finetuned-ner

This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0360
- Precision: 0.5404
- Recall: 0.7230
- F1: 0.6185
- Accuracy: 0.9949

## 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: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0352        | 1.0   | 599  | 0.0212          | 0.0154    | 0.0034 | 0.0055 | 0.9956   |
| 0.0125        | 2.0   | 1198 | 0.0184          | 0.4039    | 0.2770 | 0.3287 | 0.9958   |
| 0.0099        | 3.0   | 1797 | 0.0141          | 0.4528    | 0.4865 | 0.4691 | 0.9958   |
| 0.0082        | 4.0   | 2396 | 0.0279          | 0.4558    | 0.5743 | 0.5082 | 0.9958   |
| 0.0074        | 5.0   | 2995 | 0.0138          | 0.5153    | 0.6824 | 0.5872 | 0.9960   |
| 0.0034        | 6.0   | 3594 | 0.0193          | 0.4934    | 0.6284 | 0.5527 | 0.9953   |
| 0.0046        | 7.0   | 4193 | 0.0190          | 0.5305    | 0.7635 | 0.6260 | 0.9960   |
| 0.0024        | 8.0   | 4792 | 0.0286          | 0.5670    | 0.6858 | 0.6208 | 0.9955   |
| 0.0024        | 9.0   | 5391 | 0.0270          | 0.5889    | 0.5709 | 0.5798 | 0.9953   |
| 0.0034        | 10.0  | 5990 | 0.0339          | 0.5623    | 0.6858 | 0.6180 | 0.9953   |
| 0.0015        | 11.0  | 6589 | 0.0373          | 0.6122    | 0.7095 | 0.6573 | 0.9947   |
| 0.001         | 12.0  | 7188 | 0.0361          | 0.5519    | 0.6284 | 0.5877 | 0.9950   |
| 0.0005        | 13.0  | 7787 | 0.0353          | 0.5658    | 0.6824 | 0.6187 | 0.9950   |
| 0.0007        | 14.0  | 8386 | 0.0355          | 0.5556    | 0.7264 | 0.6296 | 0.9948   |
| 0.0003        | 15.0  | 8985 | 0.0360          | 0.5404    | 0.7230 | 0.6185 | 0.9949   |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2