---
license: mit
base_model: xlnet/xlnet-base-cased
tags:
- generated_from_trainer
datasets:
- lener_br
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: XLNet-base_LeNER-Br
  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: 0.8062054933875891
    - name: Recall
      type: recall
      value: 0.872317006053935
    - name: F1
      type: f1
      value: 0.8379592915675389
    - name: Accuracy
      type: accuracy
      value: 0.9783680282796544
---

<!-- 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. -->

# XLNet-base_LeNER-Br

This model is a fine-tuned version of [xlnet/xlnet-base-cased](https://huggingface.co/xlnet/xlnet-base-cased) on the lener_br dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Precision: 0.8062
- Recall: 0.8723
- F1: 0.8380
- Accuracy: 0.9784

## 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: 8
- eval_batch_size: 8
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2531        | 1.0   | 979  | nan             | 0.6037    | 0.7788 | 0.6801 | 0.9602   |
| 0.0531        | 2.0   | 1958 | nan             | 0.6865    | 0.8184 | 0.7467 | 0.9657   |
| 0.0344        | 3.0   | 2937 | nan             | 0.7079    | 0.8321 | 0.7650 | 0.9697   |
| 0.0214        | 4.0   | 3916 | nan             | 0.7739    | 0.8514 | 0.8108 | 0.9765   |
| 0.0176        | 5.0   | 4895 | nan             | 0.7407    | 0.8520 | 0.7924 | 0.9712   |
| 0.0109        | 6.0   | 5874 | nan             | 0.7984    | 0.8696 | 0.8325 | 0.9773   |
| 0.0093        | 7.0   | 6853 | nan             | 0.7944    | 0.8657 | 0.8285 | 0.9778   |
| 0.0056        | 8.0   | 7832 | nan             | 0.8130    | 0.8756 | 0.8431 | 0.9779   |
| 0.0041        | 9.0   | 8811 | nan             | 0.8171    | 0.8751 | 0.8451 | 0.9781   |
| 0.0034        | 10.0  | 9790 | nan             | 0.8062    | 0.8723 | 0.8380 | 0.9784   |

#### Testing results
 metrics={'test_loss': 0.10678809881210327, 'test_precision': 0.8132832080200502, 'test_recall': 0.8670674682698731, 'test_f1': 0.8393145813126414, 'test_accuracy': 0.9862667593953853, 'test_runtime': 42.9969, 'test_samples_per_second': 32.328, 'test_steps_per_second': 4.047})
 
### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1