---
license: mit
base_model: neuralmind/bert-large-portuguese-cased
tags:
- generated_from_trainer
datasets:
- harem
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: NER_harem_bert-large-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: 0.7077353867693384
    - name: Recall
      type: recall
      value: 0.7553231228987672
    - name: F1
      type: f1
      value: 0.7307553306830503
    - name: Accuracy
      type: accuracy
      value: 0.9551379448220711
---

<!-- 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-large-portuguese-cased

This model is a fine-tuned version of [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased) on the harem dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2487
- Precision: 0.7077
- Recall: 0.7553
- F1: 0.7308
- Accuracy: 0.9551

## 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   | 0.6334          | 0.0163    | 0.0078 | 0.0106 | 0.8468   |
| No log        | 2.0   | 32   | 0.4537          | 0.2614    | 0.3112 | 0.2841 | 0.8826   |
| No log        | 3.0   | 48   | 0.3117          | 0.5262    | 0.5671 | 0.5458 | 0.9231   |
| No log        | 4.0   | 64   | 0.2421          | 0.5852    | 0.6631 | 0.6217 | 0.9385   |
| No log        | 5.0   | 80   | 0.2099          | 0.5950    | 0.6855 | 0.6370 | 0.9479   |
| No log        | 6.0   | 96   | 0.2153          | 0.6810    | 0.7464 | 0.7122 | 0.9551   |
| No log        | 7.0   | 112  | 0.2270          | 0.6894    | 0.7198 | 0.7043 | 0.9546   |
| No log        | 8.0   | 128  | 0.2213          | 0.6918    | 0.7437 | 0.7168 | 0.9554   |
| No log        | 9.0   | 144  | 0.2299          | 0.7021    | 0.7564 | 0.7283 | 0.9545   |
| No log        | 10.0  | 160  | 0.2256          | 0.7002    | 0.7591 | 0.7284 | 0.9562   |
| No log        | 11.0  | 176  | 0.2169          | 0.7100    | 0.7736 | 0.7404 | 0.9568   |
| No log        | 12.0  | 192  | 0.2266          | 0.6981    | 0.7740 | 0.7341 | 0.9571   |
| No log        | 13.0  | 208  | 0.2322          | 0.7093    | 0.7620 | 0.7347 | 0.9570   |
| No log        | 14.0  | 224  | 0.2487          | 0.7077    | 0.7553 | 0.7308 | 0.9551   |


### Framework versions

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