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---
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
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