---
license: mit
base_model: neuralmind/bert-base-portuguese-cased
tags:
- generated_from_trainer
datasets:
- __main__
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ner_model
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: __main__
      type: __main__
      config: local
      split: test
      args: local
    metrics:
    - name: Precision
      type: precision
      value: 0.5783305117853887
    - name: Recall
      type: recall
      value: 0.6134825252106645
    - name: F1
      type: f1
      value: 0.5953881217321357
    - name: Accuracy
      type: accuracy
      value: 0.7670984455958549
---

<!-- 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_model

This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co./neuralmind/bert-base-portuguese-cased) on the __main__ dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5136
- Precision: 0.5783
- Recall: 0.6135
- F1: 0.5954
- Accuracy: 0.7671

## 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: 4
- eval_batch_size: 4
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.7447        | 1.0   | 5905  | 0.7678          | 0.4966    | 0.5209 | 0.5085 | 0.7409   |
| 0.6153        | 2.0   | 11810 | 0.7378          | 0.5628    | 0.5600 | 0.5614 | 0.7624   |
| 0.4623        | 3.0   | 17715 | 0.7959          | 0.5449    | 0.5836 | 0.5636 | 0.7573   |
| 0.3629        | 4.0   | 23620 | 0.8921          | 0.5679    | 0.6017 | 0.5843 | 0.7631   |
| 0.246         | 5.0   | 29525 | 1.0286          | 0.5878    | 0.5955 | 0.5916 | 0.7685   |
| 0.1923        | 6.0   | 35430 | 1.2142          | 0.5926    | 0.5957 | 0.5941 | 0.7689   |
| 0.1477        | 7.0   | 41335 | 1.3019          | 0.5681    | 0.6091 | 0.5879 | 0.7591   |
| 0.1214        | 8.0   | 47240 | 1.4101          | 0.5834    | 0.6110 | 0.5969 | 0.7659   |
| 0.0793        | 9.0   | 53145 | 1.4745          | 0.5848    | 0.6136 | 0.5989 | 0.7688   |
| 0.0733        | 10.0  | 59050 | 1.5136          | 0.5783    | 0.6135 | 0.5954 | 0.7671   |


### Framework versions

- Transformers 4.36.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.15.0