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--- |
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language: |
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- pt |
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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: 0.9801301293674859 |
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model-index: |
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- name: Luciano/bertimbau-large-lener_br |
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results: |
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- task: |
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type: token-classification |
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name: 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: test |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9840898731012984 |
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verified: true |
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- name: Precision |
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type: precision |
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value: 0.9895415357344292 |
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verified: true |
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- name: Recall |
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type: recall |
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value: 0.9885856878370763 |
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verified: true |
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- name: F1 |
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type: f1 |
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value: 0.9890633808488363 |
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verified: true |
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- name: loss |
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type: loss |
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value: 0.10151929408311844 |
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verified: true |
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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: 0.1271 |
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- Precision: 0.8965 |
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- Recall: 0.9198 |
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- F1: 0.9080 |
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- Accuracy: 0.9801 |
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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: 15 |
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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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| 0.0674 | 1.0 | 1957 | 0.1349 | 0.7617 | 0.8710 | 0.8127 | 0.9594 | |
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| 0.0443 | 2.0 | 3914 | 0.1867 | 0.6862 | 0.9194 | 0.7858 | 0.9575 | |
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| 0.0283 | 3.0 | 5871 | 0.1185 | 0.8206 | 0.8766 | 0.8477 | 0.9678 | |
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| 0.0226 | 4.0 | 7828 | 0.1405 | 0.8072 | 0.8978 | 0.8501 | 0.9708 | |
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| 0.0141 | 5.0 | 9785 | 0.1898 | 0.7224 | 0.9194 | 0.8090 | 0.9629 | |
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| 0.01 | 6.0 | 11742 | 0.1655 | 0.9062 | 0.8856 | 0.8958 | 0.9741 | |
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| 0.012 | 7.0 | 13699 | 0.1271 | 0.8965 | 0.9198 | 0.9080 | 0.9801 | |
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| 0.0091 | 8.0 | 15656 | 0.1919 | 0.8890 | 0.8886 | 0.8888 | 0.9719 | |
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| 0.0042 | 9.0 | 17613 | 0.1725 | 0.8977 | 0.8985 | 0.8981 | 0.9744 | |
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| 0.0043 | 10.0 | 19570 | 0.1530 | 0.8878 | 0.9034 | 0.8955 | 0.9761 | |
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| 0.0042 | 11.0 | 21527 | 0.1635 | 0.8792 | 0.9108 | 0.8947 | 0.9774 | |
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| 0.0033 | 12.0 | 23484 | 0.2009 | 0.8155 | 0.9138 | 0.8619 | 0.9719 | |
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| 0.0008 | 13.0 | 25441 | 0.1766 | 0.8737 | 0.9135 | 0.8932 | 0.9755 | |
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| 0.0005 | 14.0 | 27398 | 0.1868 | 0.8616 | 0.9129 | 0.8865 | 0.9743 | |
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| 0.0014 | 15.0 | 29355 | 0.1910 | 0.8694 | 0.9101 | 0.8893 | 0.9746 | |
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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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