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---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: pmfsl/bertimbau-base-finetuned-rte
results:
- task:
type: text-classification
name: Natural Lenguage Inference
dataset:
type: assin2
name: ASSIN2
metrics:
- type: accuracy
value: 0.877859477124183
- type: f1
value: 0.8860083873427372
datasets:
- assin2
metrics:
- accuracy
- f1
language:
- pt
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# pmfsl/bertimbau-base-finetuned-rte
This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0326
- Validation Loss: 0.1834
- Test Loss: 0.5695
- Train Accuracy: 0.9531
- Train F1: 0.9534
- Test Accuracy: 0.8778
- Test F1: 0.8860
- Epoch: 4
## 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:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 505, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Train F1 | Epoch |
|:----------:|:---------------:|:--------------:|:--------:|:-----:|
| 0.3846 | 0.2204 | 0.9152 | 0.9191 | 0 |
| 0.1981 | 0.1577 | 0.9442 | 0.9455 | 1 |
| 0.1026 | 0.1348 | 0.9509 | 0.9511 | 2 |
| 0.0593 | 0.1492 | 0.9531 | 0.9542 | 3 |
| 0.0326 | 0.1834 | 0.9531 | 0.9534 | 4 |
### Framework versions
- Transformers 4.27.4
- TensorFlow 2.12.0
- Datasets 2.11.0
- Tokenizers 0.13.2 |