---
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- lener_br
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-base-finetuned-ner-lenerBr
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: lener_br
      type: lener_br
      config: lener_br
      split: validation
      args: lener_br
    metrics:
    - name: Precision
      type: precision
      value: 0.7397260273972602
    - name: Recall
      type: recall
      value: 0.9211682605324373
    - name: F1
      type: f1
      value: 0.8205364337515828
    - name: Accuracy
      type: accuracy
      value: 0.970340819101409
---

<!-- 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-base-finetuned-ner-lenerBr

This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the lener_br dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1294
- Precision: 0.7397
- Recall: 0.9212
- F1: 0.8205
- Accuracy: 0.9703

## 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: 32
- eval_batch_size: 32
- 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 | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 245  | 0.1569          | 0.7358    | 0.7788 | 0.7567 | 0.9534   |
| No log        | 2.0   | 490  | 0.1310          | 0.6909    | 0.8927 | 0.7790 | 0.9632   |
| 0.1674        | 3.0   | 735  | 0.1148          | 0.7174    | 0.9119 | 0.8030 | 0.9677   |
| 0.1674        | 4.0   | 980  | 0.1550          | 0.7209    | 0.8979 | 0.7997 | 0.9658   |
| 0.0276        | 5.0   | 1225 | 0.1441          | 0.7183    | 0.9173 | 0.8057 | 0.9682   |
| 0.0276        | 6.0   | 1470 | 0.1482          | 0.7326    | 0.8752 | 0.7976 | 0.9665   |
| 0.0154        | 7.0   | 1715 | 0.1209          | 0.7418    | 0.9284 | 0.8247 | 0.9710   |
| 0.0154        | 8.0   | 1960 | 0.1266          | 0.7375    | 0.9243 | 0.8204 | 0.9708   |
| 0.0096        | 9.0   | 2205 | 0.1394          | 0.7356    | 0.9147 | 0.8154 | 0.9690   |
| 0.0096        | 10.0  | 2450 | 0.1294          | 0.7397    | 0.9212 | 0.8205 | 0.9703   |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.1.2
- Datasets 2.19.1
- Tokenizers 0.19.1