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---
license: mit
base_model: FacebookAI/roberta-base
tags:
- generated_from_trainer
datasets:
- lener_br
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-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: 0.765
- name: Recall
type: recall
value: 0.8415841584158416
- name: F1
type: f1
value: 0.8014667365112624
- name: Accuracy
type: accuracy
value: 0.9711736213348917
---
<!-- 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. -->
# roberta-base_LeNER-Br
This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on the lener_br dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Precision: 0.765
- Recall: 0.8416
- F1: 0.8015
- Accuracy: 0.9712
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.293 | 1.0 | 979 | nan | 0.5758 | 0.7525 | 0.6524 | 0.9542 |
| 0.0596 | 2.0 | 1958 | nan | 0.6546 | 0.7987 | 0.7195 | 0.9534 |
| 0.0376 | 3.0 | 2937 | nan | 0.7366 | 0.8339 | 0.7822 | 0.9672 |
| 0.0256 | 4.0 | 3916 | nan | 0.6975 | 0.8042 | 0.7471 | 0.9627 |
| 0.0192 | 5.0 | 4895 | nan | 0.7173 | 0.8317 | 0.7702 | 0.9646 |
| 0.013 | 6.0 | 5874 | nan | 0.7271 | 0.8498 | 0.7837 | 0.9605 |
| 0.013 | 7.0 | 6853 | nan | 0.7426 | 0.8537 | 0.7943 | 0.9680 |
| 0.0064 | 8.0 | 7832 | nan | 0.7493 | 0.8399 | 0.7920 | 0.9702 |
| 0.0052 | 9.0 | 8811 | nan | 0.7611 | 0.8273 | 0.7928 | 0.9725 |
| 0.0044 | 10.0 | 9790 | nan | 0.765 | 0.8416 | 0.8015 | 0.9712 |
### Testing results
metrics={'test_loss': 0.08161260932683945, 'test_precision': 0.8342714196372732, 'test_recall': 0.8840291583830351, 'test_f1': 0.8584298584298585, 'test_accuracy': 0.9863512377202157, 'test_runtime': 20.4317, 'test_samples_per_second': 68.032, 'test_steps_per_second': 8.516})
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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