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bert-base-portuguese-cased_harem-selective-sm-first-ner

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bert-base-portuguese-cased_harem-selective-sm-first-ner
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---
license: mit
tags:
- generated_from_trainer
datasets:
- harem
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-portuguese-cased_harem-sm-first-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: harem
      type: harem
      args: selective
    metrics:
    - name: Precision
      type: precision
      value: 0.7455830388692579
    - name: Recall
      type: recall
      value: 0.8053435114503816
    - name: F1
      type: f1
      value: 0.7743119266055045
    - name: Accuracy
      type: accuracy
      value: 0.964875491480996
---

<!-- 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-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: 0.1952
- Precision: 0.7456
- Recall: 0.8053
- F1: 0.7743
- Accuracy: 0.9649

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1049        | 1.0   | 2517 | 0.1955          | 0.6601    | 0.7710 | 0.7113 | 0.9499   |
| 0.0622        | 2.0   | 5034 | 0.2097          | 0.7314    | 0.7901 | 0.7596 | 0.9554   |
| 0.0318        | 3.0   | 7551 | 0.1952          | 0.7456    | 0.8053 | 0.7743 | 0.9649   |


### Framework versions

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