---
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
  - task:
      type: token-classification
      name: Token Classification
    dataset:
      name: lener_br
      type: lener_br
      config: lener_br
      split: validation
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.979126510974644
      verified: true
    - name: Precision
      type: precision
      value: 0.9846948786709399
      verified: true
    - name: Recall
      type: recall
      value: 0.9839386958155646
      verified: true
    - name: F1
      type: f1
      value: 0.9843166420124387
      verified: true
    - name: loss
      type: loss
      value: 0.17586557567119598
      verified: true
  - task:
      type: token-classification
      name: Token Classification
    dataset:
      name: lener_br
      type: lener_br
      config: lener_br
      split: train
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9986508230532317
      verified: true
    - name: Precision
      type: precision
      value: 0.9980332928982356
      verified: true
    - name: Recall
      type: recall
      value: 0.998726011303645
      verified: true
    - name: F1
      type: f1
      value: 0.998379531941543
      verified: true
    - name: loss
      type: loss
      value: 0.002737082075327635
      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