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---
license: mit
tags:
- generated_from_trainer
datasets:
- lener_br
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-large-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: 0.8545767716535433
- name: Recall
type: recall
value: 0.8976479710519514
- name: F1
type: f1
value: 0.8755830076893987
- name: Accuracy
type: accuracy
value: 0.979126510974644
- task:
type: token-classification
name: Token Classification
dataset:
name: lener_br
type: lener_br
config: lener_br
split: test
metrics:
- name: Accuracy
type: accuracy
value: 0.9842606502473917
verified: true
- name: Precision
type: precision
value: 0.9880888491353608
verified: true
- name: Recall
type: recall
value: 0.9863977974551678
verified: true
- name: F1
type: f1
value: 0.9872425991435487
verified: true
- name: loss
type: loss
value: 0.12697908282279968
verified: true
---
<!-- 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: 0.8546
- Recall: 0.8976
- F1: 0.8756
- Accuracy: 0.9791
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0836 | 1.0 | 3914 | nan | 0.5735 | 0.8348 | 0.6799 | 0.9526 |
| 0.0664 | 2.0 | 7828 | nan | 0.8153 | 0.8315 | 0.8233 | 0.9658 |
| 0.0505 | 3.0 | 11742 | nan | 0.6885 | 0.9147 | 0.7857 | 0.9644 |
| 0.1165 | 4.0 | 15656 | nan | 0.7572 | 0.8067 | 0.7811 | 0.9641 |
| 0.0206 | 5.0 | 19570 | nan | 0.8678 | 0.8770 | 0.8723 | 0.9774 |
| 0.02 | 6.0 | 23484 | nan | 0.7285 | 0.8907 | 0.8015 | 0.9669 |
| 0.0248 | 7.0 | 27398 | nan | 0.8717 | 0.9095 | 0.8902 | 0.9793 |
| 0.0223 | 8.0 | 31312 | nan | 0.8407 | 0.8801 | 0.8600 | 0.9766 |
| 0.0084 | 9.0 | 35226 | nan | 0.8354 | 0.8684 | 0.8516 | 0.9705 |
| 0.0067 | 10.0 | 39140 | nan | 0.8312 | 0.9062 | 0.8671 | 0.9753 |
| 0.006 | 11.0 | 43054 | nan | 0.8866 | 0.8953 | 0.8909 | 0.9784 |
| 0.0058 | 12.0 | 46968 | nan | 0.8961 | 0.8987 | 0.8974 | 0.9807 |
| 0.0062 | 13.0 | 50882 | nan | 0.8360 | 0.8785 | 0.8567 | 0.9783 |
| 0.0053 | 14.0 | 54796 | nan | 0.8327 | 0.8749 | 0.8533 | 0.9782 |
| 0.003 | 15.0 | 58710 | nan | 0.8546 | 0.8976 | 0.8756 | 0.9791 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
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