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--- |
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license: mit |
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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-large-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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args: lener_br |
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metric: |
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name: Accuracy |
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type: accuracy |
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value: 0.9744464930592591 |
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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-large-lener_br |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1193 |
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- Precision: 0.8287 |
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- Recall: 0.9144 |
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- F1: 0.8694 |
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- Accuracy: 0.9744 |
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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: 4 |
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- eval_batch_size: 4 |
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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: 3 |
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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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| 0.0586 | 1.0 | 1957 | 0.1409 | 0.8317 | 0.8725 | 0.8516 | 0.9667 | |
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| 0.0304 | 2.0 | 3914 | 0.1338 | 0.7137 | 0.9172 | 0.8027 | 0.9619 | |
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| 0.0119 | 3.0 | 5871 | 0.1193 | 0.8287 | 0.9144 | 0.8694 | 0.9744 | |
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### Framework versions |
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- Transformers 4.8.2 |
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- Pytorch 1.9.0+cu102 |
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- Datasets 1.9.0 |
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- Tokenizers 0.10.3 |
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