---
license: mit
base_model: PORTULAN/albertina-100m-portuguese-ptpt-encoder
tags:
- generated_from_trainer
datasets:
- harem
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: NER_harem_albertina-100m-portuguese-ptpt-encoder
  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: 0.67216673903604
    - name: Recall
      type: recall
      value: 0.725398313027179
    - name: F1
      type: f1
      value: 0.6977687626774848
    - name: Accuracy
      type: accuracy
      value: 0.9532056132627089
---

<!-- 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_albertina-100m-portuguese-ptpt-encoder

This model is a fine-tuned version of [PORTULAN/albertina-100m-portuguese-ptpt-encoder](https://huggingface.co/PORTULAN/albertina-100m-portuguese-ptpt-encoder) on the harem dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2583
- Precision: 0.6722
- Recall: 0.7254
- F1: 0.6978
- Accuracy: 0.9532

## 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   | 0.5322          | 0.0212    | 0.0117 | 0.0151 | 0.8615   |
| No log        | 2.0   | 32   | 0.3238          | 0.4230    | 0.4981 | 0.4575 | 0.9110   |
| No log        | 3.0   | 48   | 0.2460          | 0.5006    | 0.6007 | 0.5461 | 0.9369   |
| No log        | 4.0   | 64   | 0.2240          | 0.5526    | 0.6396 | 0.5930 | 0.9414   |
| No log        | 5.0   | 80   | 0.2088          | 0.5498    | 0.6340 | 0.5889 | 0.9492   |
| No log        | 6.0   | 96   | 0.2068          | 0.5884    | 0.6645 | 0.6241 | 0.9496   |
| No log        | 7.0   | 112  | 0.2253          | 0.5906    | 0.6720 | 0.6287 | 0.9481   |
| No log        | 8.0   | 128  | 0.2115          | 0.6245    | 0.6874 | 0.6545 | 0.9516   |
| No log        | 9.0   | 144  | 0.2187          | 0.6546    | 0.7062 | 0.6794 | 0.9533   |
| No log        | 10.0  | 160  | 0.2398          | 0.6432    | 0.7020 | 0.6713 | 0.9495   |
| No log        | 11.0  | 176  | 0.2554          | 0.6653    | 0.7043 | 0.6843 | 0.9526   |
| No log        | 12.0  | 192  | 0.2397          | 0.6777    | 0.7212 | 0.6988 | 0.9529   |
| No log        | 13.0  | 208  | 0.2565          | 0.6778    | 0.7207 | 0.6986 | 0.9531   |
| No log        | 14.0  | 224  | 0.2700          | 0.6586    | 0.7142 | 0.6853 | 0.9506   |
| No log        | 15.0  | 240  | 0.2700          | 0.7009    | 0.7259 | 0.7132 | 0.9544   |
| No log        | 16.0  | 256  | 0.2688          | 0.6761    | 0.7240 | 0.6993 | 0.9532   |
| No log        | 17.0  | 272  | 0.2741          | 0.7132    | 0.7343 | 0.7236 | 0.9558   |
| No log        | 18.0  | 288  | 0.2732          | 0.6740    | 0.7132 | 0.6931 | 0.9530   |
| No log        | 19.0  | 304  | 0.2745          | 0.7094    | 0.7310 | 0.7201 | 0.9550   |
| No log        | 20.0  | 320  | 0.2583          | 0.6722    | 0.7254 | 0.6978 | 0.9532   |


### Framework versions

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