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---
license: mit
base_model: neuralmind/bert-base-portuguese-cased
tags:
- generated_from_trainer
datasets:
- glue-ptpt
metrics:
- accuracy
- f1
model-index:
- name: paraphrase-bert-portuguese
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue-ptpt
type: glue-ptpt
config: mrpc
split: validation
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8676470588235294
- name: F1
type: f1
value: 0.9028776978417268
---
<!-- 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. -->
# paraphrase-bert-portuguese
This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the glue-ptpt dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2267
- Accuracy: 0.8676
- F1: 0.9029
## 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: 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 459 | 0.7241 | 0.8603 | 0.9012 |
| 0.0658 | 2.0 | 918 | 0.7902 | 0.8725 | 0.9071 |
| 0.1499 | 3.0 | 1377 | 0.7895 | 0.8676 | 0.9022 |
| 0.0654 | 4.0 | 1836 | 0.9841 | 0.8676 | 0.9036 |
| 0.018 | 5.0 | 2295 | 1.0520 | 0.8627 | 0.8989 |
| 0.0144 | 6.0 | 2754 | 1.1002 | 0.8725 | 0.9081 |
| 0.007 | 7.0 | 3213 | 1.1303 | 0.8652 | 0.9005 |
| 0.0056 | 8.0 | 3672 | 1.2298 | 0.8725 | 0.9081 |
| 0.0019 | 9.0 | 4131 | 1.2353 | 0.8701 | 0.9038 |
| 0.0001 | 10.0 | 4590 | 1.2267 | 0.8676 | 0.9029 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
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