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--- |
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license: mit |
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base_model: neuralmind/bert-base-portuguese-cased |
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tags: |
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- generated_from_trainer |
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datasets: |
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- lener_br |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: BERTimbau-base_LeNER-Br |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: lener_br |
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type: lener_br |
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config: lener_br |
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split: validation |
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args: lener_br |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.8317805383022774 |
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- name: Recall |
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type: recall |
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value: 0.8839383938393839 |
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- name: F1 |
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type: f1 |
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value: 0.8570666666666666 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9754369390647142 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# BERTimbau-base_LeNER-Br |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: nan |
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- Precision: 0.8318 |
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- Recall: 0.8839 |
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- F1: 0.8571 |
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- Accuracy: 0.9754 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.2037 | 1.0 | 979 | nan | 0.7910 | 0.8762 | 0.8314 | 0.9721 | |
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| 0.0308 | 2.0 | 1958 | nan | 0.7747 | 0.8663 | 0.8180 | 0.9698 | |
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| 0.02 | 3.0 | 2937 | nan | 0.8316 | 0.8911 | 0.8603 | 0.9801 | |
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| 0.0133 | 4.0 | 3916 | nan | 0.8038 | 0.8812 | 0.8407 | 0.9728 | |
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| 0.0111 | 5.0 | 4895 | nan | 0.8253 | 0.8707 | 0.8474 | 0.9753 | |
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| 0.0078 | 6.0 | 5874 | nan | 0.8235 | 0.8779 | 0.8498 | 0.9711 | |
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| 0.0057 | 7.0 | 6853 | nan | 0.8174 | 0.8768 | 0.8461 | 0.9760 | |
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| 0.0032 | 8.0 | 7832 | nan | 0.8113 | 0.8845 | 0.8463 | 0.9769 | |
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| 0.0027 | 9.0 | 8811 | nan | 0.8344 | 0.8867 | 0.8597 | 0.9767 | |
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| 0.0023 | 10.0 | 9790 | nan | 0.8318 | 0.8839 | 0.8571 | 0.9754 | |
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### Testing results |
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metrics={'test_loss': 0.0710107609629631, 'test_precision': 0.8785578747628083, 'test_recall': 0.9138157894736842, 'test_f1': 0.8958400515962593, 'test_accuracy': 0.9884423662270061, 'test_runtime': 12.4395, 'test_samples_per_second': 111.741, 'test_steps_per_second': 13.988}) |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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