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---
language:
- pt
license: mit
tags:
- generated_from_trainer
datasets:
- lener_br
metrics:
- precision
- recall
- f1
- accuracy
base_model: xlm-roberta-large
model-index:
- name: xlm-roberta-large-finetuned-lener-br
results:
- task:
type: token-classification
name: Token Classification
dataset:
name: lener_br
type: lener_br
config: lener_br
split: train
args: lener_br
metrics:
- type: precision
value: 0.8762313715584744
name: Precision
- type: recall
value: 0.8966141121736882
name: Recall
- type: f1
value: 0.8863055697496168
name: F1
- type: accuracy
value: 0.979500052295785
name: Accuracy
---
<!-- 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
This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the lener_br dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Precision: 0.8762
- Recall: 0.8966
- F1: 0.8863
- Accuracy: 0.9795
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0785 | 1.0 | 3914 | nan | 0.7119 | 0.8410 | 0.7711 | 0.9658 |
| 0.076 | 2.0 | 7828 | nan | 0.8397 | 0.8679 | 0.8536 | 0.9740 |
| 0.0434 | 3.0 | 11742 | nan | 0.8545 | 0.8666 | 0.8605 | 0.9693 |
| 0.022 | 4.0 | 15656 | nan | 0.8293 | 0.8573 | 0.8431 | 0.9652 |
| 0.0284 | 5.0 | 19570 | nan | 0.8789 | 0.8571 | 0.8678 | 0.9776 |
| 0.029 | 6.0 | 23484 | nan | 0.8521 | 0.8788 | 0.8653 | 0.9771 |
| 0.0227 | 7.0 | 27398 | nan | 0.7648 | 0.8873 | 0.8215 | 0.9686 |
| 0.0219 | 8.0 | 31312 | nan | 0.8609 | 0.9026 | 0.8813 | 0.9780 |
| 0.0121 | 9.0 | 35226 | nan | 0.8746 | 0.8979 | 0.8861 | 0.9812 |
| 0.0087 | 10.0 | 39140 | nan | 0.8829 | 0.8827 | 0.8828 | 0.9808 |
| 0.0081 | 11.0 | 43054 | nan | 0.8740 | 0.8749 | 0.8745 | 0.9765 |
| 0.0058 | 12.0 | 46968 | nan | 0.8838 | 0.8842 | 0.8840 | 0.9788 |
| 0.0044 | 13.0 | 50882 | nan | 0.869 | 0.8984 | 0.8835 | 0.9788 |
| 0.002 | 14.0 | 54796 | nan | 0.8762 | 0.8966 | 0.8863 | 0.9795 |
| 0.0017 | 15.0 | 58710 | nan | 0.8729 | 0.8982 | 0.8854 | 0.9791 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|