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---
language:
- pt
license: mit
tags:
- generated_from_trainer
datasets:
- lener_br
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-large-finetuned-lener_br-finetuned-lener-br
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: lener_br
type: lener_br
config: lener_br
split: train
args: lener_br
metrics:
- name: Precision
type: precision
value: 0.9122490993309316
- name: Recall
type: recall
value: 0.9162574308606876
- name: F1
type: f1
value: 0.9142488716956804
- name: Accuracy
type: accuracy
value: 0.982592974434832
---
<!-- 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. -->
# xlm-roberta-large-finetuned-lener_br-finetuned-lener-br
This model is a fine-tuned version of [Luciano/xlm-roberta-large-finetuned-lener_br](https://huggingface.co/Luciano/xlm-roberta-large-finetuned-lener_br) on the lener_br dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Precision: 0.9122
- Recall: 0.9163
- F1: 0.9142
- Accuracy: 0.9826
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.068 | 1.0 | 3914 | nan | 0.6196 | 0.8604 | 0.7204 | 0.9568 |
| 0.0767 | 2.0 | 7828 | nan | 0.8270 | 0.8710 | 0.8484 | 0.9693 |
| 0.0257 | 3.0 | 11742 | nan | 0.7243 | 0.9005 | 0.8029 | 0.9639 |
| 0.0193 | 4.0 | 15656 | nan | 0.9010 | 0.8984 | 0.8997 | 0.9821 |
| 0.0156 | 5.0 | 19570 | nan | 0.7150 | 0.9121 | 0.8016 | 0.9641 |
| 0.0165 | 6.0 | 23484 | nan | 0.7640 | 0.8796 | 0.8177 | 0.9691 |
| 0.0225 | 7.0 | 27398 | nan | 0.8851 | 0.9098 | 0.8973 | 0.9803 |
| 0.016 | 8.0 | 31312 | nan | 0.9081 | 0.9015 | 0.9048 | 0.9792 |
| 0.0078 | 9.0 | 35226 | nan | 0.8941 | 0.8863 | 0.8902 | 0.9788 |
| 0.0061 | 10.0 | 39140 | nan | 0.9026 | 0.9002 | 0.9014 | 0.9804 |
| 0.0057 | 11.0 | 43054 | nan | 0.8793 | 0.9018 | 0.8904 | 0.9769 |
| 0.0044 | 12.0 | 46968 | nan | 0.8790 | 0.9033 | 0.8910 | 0.9785 |
| 0.0043 | 13.0 | 50882 | nan | 0.9122 | 0.9163 | 0.9142 | 0.9826 |
| 0.0003 | 14.0 | 54796 | nan | 0.9032 | 0.9070 | 0.9051 | 0.9807 |
| 0.0025 | 15.0 | 58710 | nan | 0.8903 | 0.9085 | 0.8993 | 0.9798 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
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