---
license: mit
tags:
- generated_from_trainer
datasets:
- harem
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-portuguese-cased_harem-selective-lowC-sm-first-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: harem
      type: harem
      args: selective
    metrics:
    - name: Precision
      type: precision
      value: 0.8
    - name: Recall
      type: recall
      value: 0.8764044943820225
    - name: F1
      type: f1
      value: 0.8364611260053619
    - name: Accuracy
      type: accuracy
      value: 0.9764089121887287
---

<!-- 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-portuguese-cased_harem-selective-lowC-sm-first-ner

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: 0.1160
- Precision: 0.8
- Recall: 0.8764
- F1: 0.8365
- Accuracy: 0.9764

## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.055         | 1.0   | 2517 | 0.0934          | 0.81      | 0.9101 | 0.8571 | 0.9699   |
| 0.0236        | 2.0   | 5034 | 0.0883          | 0.8307    | 0.8820 | 0.8556 | 0.9751   |
| 0.0129        | 3.0   | 7551 | 0.1160          | 0.8       | 0.8764 | 0.8365 | 0.9764   |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.10.2+cu102
- Datasets 2.2.2
- Tokenizers 0.12.1