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---
language:
- pt
license: mit
tags:
- generated_from_trainer
datasets:
- lener_br
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: bertimbau-large-lener_br
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: lener_br
type: lener_br
args: lener_br
metric:
name: Accuracy
type: accuracy
value: 0.9801301293674859
model-index:
- name: Luciano/bertimbau-large-lener_br
results:
- task:
type: token-classification
name: Token Classification
dataset:
name: lener_br
type: lener_br
config: lener_br
split: test
metrics:
- name: Accuracy
type: accuracy
value: 0.9840898731012984
verified: true
- name: Precision
type: precision
value: 0.9895415357344292
verified: true
- name: Recall
type: recall
value: 0.9885856878370763
verified: true
- name: F1
type: f1
value: 0.9890633808488363
verified: true
- name: loss
type: loss
value: 0.10151929408311844
verified: true
- task:
type: token-classification
name: Token Classification
dataset:
name: lener_br
type: lener_br
config: lener_br
split: validation
metrics:
- name: Accuracy
type: accuracy
value: 0.9801301293674859
verified: true
- name: Precision
type: precision
value: 0.9864285473144053
verified: true
- name: Recall
type: recall
value: 0.9845505854603656
verified: true
- name: F1
type: f1
value: 0.9854886717201953
verified: true
- name: loss
type: loss
value: 0.11984097212553024
verified: true
---
<!-- 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. -->
# bertimbau-large-lener_br
This model is a fine-tuned version of [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased) on the lener_br dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1271
- Precision: 0.8965
- Recall: 0.9198
- F1: 0.9080
- Accuracy: 0.9801
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0674 | 1.0 | 1957 | 0.1349 | 0.7617 | 0.8710 | 0.8127 | 0.9594 |
| 0.0443 | 2.0 | 3914 | 0.1867 | 0.6862 | 0.9194 | 0.7858 | 0.9575 |
| 0.0283 | 3.0 | 5871 | 0.1185 | 0.8206 | 0.8766 | 0.8477 | 0.9678 |
| 0.0226 | 4.0 | 7828 | 0.1405 | 0.8072 | 0.8978 | 0.8501 | 0.9708 |
| 0.0141 | 5.0 | 9785 | 0.1898 | 0.7224 | 0.9194 | 0.8090 | 0.9629 |
| 0.01 | 6.0 | 11742 | 0.1655 | 0.9062 | 0.8856 | 0.8958 | 0.9741 |
| 0.012 | 7.0 | 13699 | 0.1271 | 0.8965 | 0.9198 | 0.9080 | 0.9801 |
| 0.0091 | 8.0 | 15656 | 0.1919 | 0.8890 | 0.8886 | 0.8888 | 0.9719 |
| 0.0042 | 9.0 | 17613 | 0.1725 | 0.8977 | 0.8985 | 0.8981 | 0.9744 |
| 0.0043 | 10.0 | 19570 | 0.1530 | 0.8878 | 0.9034 | 0.8955 | 0.9761 |
| 0.0042 | 11.0 | 21527 | 0.1635 | 0.8792 | 0.9108 | 0.8947 | 0.9774 |
| 0.0033 | 12.0 | 23484 | 0.2009 | 0.8155 | 0.9138 | 0.8619 | 0.9719 |
| 0.0008 | 13.0 | 25441 | 0.1766 | 0.8737 | 0.9135 | 0.8932 | 0.9755 |
| 0.0005 | 14.0 | 27398 | 0.1868 | 0.8616 | 0.9129 | 0.8865 | 0.9743 |
| 0.0014 | 15.0 | 29355 | 0.1910 | 0.8694 | 0.9101 | 0.8893 | 0.9746 |
### Framework versions
- Transformers 4.8.2
- Pytorch 1.9.0+cu102
- Datasets 1.9.0
- Tokenizers 0.10.3
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