---
license: mit
base_model: PORTULAN/albertina-900m-portuguese-ptpt-encoder
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: LVI_albertina-900m-portuguese-ptpt-encoder
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# LVI_albertina-900m-portuguese-ptpt-encoder

This model is a fine-tuned version of [PORTULAN/albertina-900m-portuguese-ptpt-encoder](https://huggingface.co/PORTULAN/albertina-900m-portuguese-ptpt-encoder) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1651
- Accuracy: 0.9832
- F1: 0.9833
- Precision: 0.9779
- Recall: 0.9889

## 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: 5e-06
- train_batch_size: 16
- eval_batch_size: 16
- 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 | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.1273        | 1.0   | 12866 | 0.1718          | 0.9676   | 0.9668 | 0.9914    | 0.9434 |
| 0.1034        | 2.0   | 25732 | 0.1283          | 0.9814   | 0.9813 | 0.9844    | 0.9782 |
| 0.0292        | 3.0   | 38598 | 0.1219          | 0.9850   | 0.9850 | 0.9828    | 0.9872 |
| 0.0251        | 4.0   | 51464 | 0.1203          | 0.9857   | 0.9856 | 0.9901    | 0.9812 |
| 0.013         | 5.0   | 64330 | 0.1240          | 0.9837   | 0.9836 | 0.9896    | 0.9777 |
| 0.0237        | 6.0   | 77196 | 0.1294          | 0.9848   | 0.9849 | 0.9809    | 0.9889 |
| 0.0153        | 7.0   | 90062 | 0.1651          | 0.9832   | 0.9833 | 0.9779    | 0.9889 |


### Framework versions

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