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---
license: mit
base_model: neuralmind/bert-base-portuguese-cased
tags:
- generated_from_trainer
datasets:
- lener_br
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-portuguese-cased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: lener_br
type: lener_br
config: lener_br
split: test
args: lener_br
metrics:
- name: Precision
type: precision
value: 0.8649122807017544
- name: Recall
type: recall
value: 0.8885169927909372
- name: F1
type: f1
value: 0.8765557531115061
- name: Accuracy
type: accuracy
value: 0.9821930095431353
---
<!-- 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. -->
# bert-base-portuguese-cased-finetuned-ner
This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the lener_br dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0679
- Precision: 0.8649
- Recall: 0.8885
- F1: 0.8766
- Accuracy: 0.9822
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 490 | 0.0795 | 0.8185 | 0.7907 | 0.8043 | 0.9753 |
| 0.1925 | 2.0 | 980 | 0.0683 | 0.8475 | 0.8602 | 0.8538 | 0.9803 |
| 0.0422 | 3.0 | 1470 | 0.0679 | 0.8649 | 0.8885 | 0.8766 | 0.9822 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|