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---
license: mit
base_model: neuralmind/bert-large-portuguese-cased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: LVI_bert-large-portuguese-cased
results: []
---
<!-- 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. -->
# LVI_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 None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0755
- Accuracy: 0.9775
- F1: 0.9775
- Precision: 0.9758
- Recall: 0.9793
## Run it
```sh
import transformers
model_name = "liaad/LVI_bert-large-portuguese-cased"
pipe = transformers.pipeline(model=model_name)
text = "Olá, como você está?"
print(pipe(text))
```
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.1071 | 1.0 | 3217 | 0.0755 | 0.9775 | 0.9775 | 0.9758 | 0.9793 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|