---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
datasets:
- lener_br
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-large-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: 0.9166029074215761
    - name: Recall
      type: recall
      value: 0.9289222021194107
    - name: F1
      type: f1
      value: 0.9227214377406933
    - name: Accuracy
      type: accuracy
      value: 0.9853721218641206
---

<!-- 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-ner-lenerBr

This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the lener_br dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Precision: 0.9166
- Recall: 0.9289
- F1: 0.9227
- Accuracy: 0.9854

## 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
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.9995 | 489  | nan             | 0.8191    | 0.8167 | 0.8179 | 0.9751   |
| 0.163         | 1.9990 | 978  | nan             | 0.8600    | 0.9080 | 0.8833 | 0.9790   |
| 0.0427        | 2.9985 | 1467 | nan             | 0.8736    | 0.9163 | 0.8944 | 0.9814   |
| 0.0279        | 4.0    | 1957 | nan             | 0.8688    | 0.9191 | 0.8932 | 0.9801   |
| 0.019         | 4.9995 | 2446 | nan             | 0.9123    | 0.9196 | 0.9159 | 0.9840   |
| 0.0143        | 5.9990 | 2935 | nan             | 0.9008    | 0.9346 | 0.9174 | 0.9842   |
| 0.0112        | 6.9985 | 3424 | nan             | 0.9063    | 0.9250 | 0.9156 | 0.9843   |
| 0.0072        | 8.0    | 3914 | nan             | 0.8954    | 0.9315 | 0.9131 | 0.9841   |
| 0.0065        | 8.9995 | 4403 | nan             | 0.9226    | 0.9245 | 0.9236 | 0.9857   |
| 0.0048        | 9.9949 | 4890 | nan             | 0.9166    | 0.9289 | 0.9227 | 0.9854   |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3