metadata
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: 0.8717564870259481
- name: Recall
type: recall
value: 0.8995880535530381
- name: F1
type: f1
value: 0.88545362392296
- name: Accuracy
type: accuracy
value: 0.9836487420412604
bert-base-portuguese-cased-finetuned-ner
This model is a fine-tuned version of neuralmind/bert-base-portuguese-cased on the lener_br dataset. It achieves the following results on the evaluation set:
- Loss: 0.0652
- Precision: 0.8718
- Recall: 0.8996
- F1: 0.8855
- Accuracy: 0.9836
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 | 0.0911 | 0.8063 | 0.7703 | 0.7879 | 0.9734 |
| 0.1901 | 2.0 | 980 | 0.0665 | 0.8525 | 0.8929 | 0.8722 | 0.9819 |
| 0.0419 | 3.0 | 1470 | 0.0652 | 0.8718 | 0.8996 | 0.8855 | 0.9836 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1