---
license: mit
base_model: neuralmind/bert-base-portuguese-cased
tags:
- generated_from_trainer
datasets:
- harem
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: NER_harem_bert-base-portuguese-cased
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: harem
      type: harem
      config: default
      split: test
      args: default
    metrics:
    - name: Precision
      type: precision
      value: 0.6852879944482998
    - name: Recall
      type: recall
      value: 0.7377661561449383
    - name: F1
      type: f1
      value: 0.7105594531390537
    - name: Accuracy
      type: accuracy
      value: 0.952219112355058
---

<!-- 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_harem_bert-base-portuguese-cased

This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the harem dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2351
- Precision: 0.6853
- Recall: 0.7378
- F1: 0.7106
- Accuracy: 0.9522

## 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: 3e-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: 300

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 16   | 0.7692          | 0.0       | 0.0    | 0.0    | 0.8358   |
| No log        | 2.0   | 32   | 0.4831          | 0.3140    | 0.2731 | 0.2921 | 0.8790   |
| No log        | 3.0   | 48   | 0.3405          | 0.4692    | 0.4897 | 0.4793 | 0.9119   |
| No log        | 4.0   | 64   | 0.2747          | 0.5481    | 0.6156 | 0.5799 | 0.9340   |
| No log        | 5.0   | 80   | 0.2282          | 0.6077    | 0.6758 | 0.6399 | 0.9443   |
| No log        | 6.0   | 96   | 0.2145          | 0.6267    | 0.6892 | 0.6565 | 0.9479   |
| No log        | 7.0   | 112  | 0.2223          | 0.6395    | 0.6926 | 0.6650 | 0.9493   |
| No log        | 8.0   | 128  | 0.2100          | 0.6822    | 0.7378 | 0.7089 | 0.9530   |
| No log        | 9.0   | 144  | 0.2077          | 0.6810    | 0.7497 | 0.7137 | 0.9537   |
| No log        | 10.0  | 160  | 0.2173          | 0.6846    | 0.7460 | 0.7140 | 0.9523   |
| No log        | 11.0  | 176  | 0.2226          | 0.7001    | 0.7594 | 0.7285 | 0.9542   |
| No log        | 12.0  | 192  | 0.2204          | 0.7015    | 0.7568 | 0.7281 | 0.9538   |
| No log        | 13.0  | 208  | 0.2278          | 0.6746    | 0.7411 | 0.7063 | 0.9533   |
| No log        | 14.0  | 224  | 0.2351          | 0.6853    | 0.7378 | 0.7106 | 0.9522   |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2