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rhaymison
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ner-portuguese-br-bert-cased

Token Classification
Transformers
Safetensors
Portuguese
bert
Generated from Trainer
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- library_name: transformers
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- tags: []
 
 
 
 
 
 
 
 
 
 
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+ license: apache-2.0
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+ base_model: google-bert/bert-base-cased
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+ tags:
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+ - generated_from_trainer
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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: ner-portuguese
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+ results: []
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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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+ # ner-portuguese-br-bert-cased
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+ This model aims to meet the needs of models in the Portuguese language. He has various named classes. Follow the list below:
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+ This model is a fine-tuned version of [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0618
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+ - Precision: 0.8965
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+ - Recall: 0.8815
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+ - F1: 0.8889
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+ - Accuracy: 0.9810
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 1e-05
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+ - train_batch_size: 4
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - gradient_accumulation_steps: 2
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+ - total_train_batch_size: 8
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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: 1
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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+ |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 0.3792 | 0.03 | 500 | 0.2062 | 0.6752 | 0.6537 | 0.6642 | 0.9522 |
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+ | 0.1822 | 0.06 | 1000 | 0.1587 | 0.7685 | 0.7267 | 0.7470 | 0.9618 |
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+ | 0.152 | 0.08 | 1500 | 0.1407 | 0.7932 | 0.7675 | 0.7802 | 0.9663 |
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+ | 0.1385 | 0.11 | 2000 | 0.1240 | 0.8218 | 0.7863 | 0.8037 | 0.9693 |
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+ | 0.1216 | 0.14 | 2500 | 0.1129 | 0.8529 | 0.7850 | 0.8175 | 0.9710 |
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+ | 0.1192 | 0.17 | 3000 | 0.1059 | 0.8520 | 0.7917 | 0.8208 | 0.9717 |
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+ | 0.1165 | 0.2 | 3500 | 0.1053 | 0.8373 | 0.8071 | 0.8220 | 0.9717 |
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+ | 0.0997 | 0.23 | 4000 | 0.0978 | 0.8434 | 0.8212 | 0.8322 | 0.9729 |
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+ | 0.0938 | 0.25 | 4500 | 0.0963 | 0.8393 | 0.8313 | 0.8353 | 0.9736 |
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+ | 0.0921 | 0.28 | 5000 | 0.0867 | 0.8593 | 0.8365 | 0.8478 | 0.9750 |
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+ | 0.0943 | 0.31 | 5500 | 0.0846 | 0.8704 | 0.8268 | 0.8480 | 0.9754 |
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+ | 0.0921 | 0.34 | 6000 | 0.0832 | 0.8556 | 0.8384 | 0.8469 | 0.9750 |
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+ | 0.0936 | 0.37 | 6500 | 0.0802 | 0.8726 | 0.8361 | 0.8540 | 0.9760 |
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+ | 0.0854 | 0.39 | 7000 | 0.0780 | 0.8749 | 0.8452 | 0.8598 | 0.9767 |
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+ | 0.082 | 0.42 | 7500 | 0.0751 | 0.8812 | 0.8472 | 0.8639 | 0.9773 |
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+ | 0.0761 | 0.45 | 8000 | 0.0745 | 0.8752 | 0.8571 | 0.8660 | 0.9772 |
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+ | 0.0799 | 0.48 | 8500 | 0.0752 | 0.8635 | 0.8530 | 0.8582 | 0.9767 |
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+ | 0.0728 | 0.51 | 9000 | 0.0746 | 0.8938 | 0.8398 | 0.8660 | 0.9780 |
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+ | 0.0787 | 0.54 | 9500 | 0.0715 | 0.8791 | 0.8552 | 0.8670 | 0.9780 |
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+ | 0.0721 | 0.56 | 10000 | 0.0707 | 0.8822 | 0.8598 | 0.8709 | 0.9785 |
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+ | 0.0729 | 0.59 | 10500 | 0.0682 | 0.8775 | 0.8743 | 0.8759 | 0.9790 |
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+ | 0.0707 | 0.62 | 11000 | 0.0686 | 0.8797 | 0.8696 | 0.8746 | 0.9789 |
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+ | 0.0726 | 0.65 | 11500 | 0.0683 | 0.8944 | 0.8497 | 0.8715 | 0.9788 |
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+ | 0.0689 | 0.68 | 12000 | 0.0667 | 0.8931 | 0.8609 | 0.8767 | 0.9795 |
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+ | 0.0735 | 0.7 | 12500 | 0.0673 | 0.8742 | 0.8815 | 0.8779 | 0.9791 |
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+ | 0.0725 | 0.73 | 13000 | 0.0666 | 0.8849 | 0.8713 | 0.8781 | 0.9796 |
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+ | 0.0684 | 0.76 | 13500 | 0.0656 | 0.8881 | 0.8728 | 0.8804 | 0.9799 |
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+ | 0.0736 | 0.79 | 14000 | 0.0644 | 0.8948 | 0.8677 | 0.8811 | 0.9800 |
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+ | 0.0663 | 0.82 | 14500 | 0.0644 | 0.8844 | 0.8764 | 0.8803 | 0.9798 |
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+ | 0.0652 | 0.85 | 15000 | 0.0645 | 0.8778 | 0.8845 | 0.8812 | 0.9797 |
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+ | 0.0672 | 0.87 | 15500 | 0.0644 | 0.8788 | 0.8807 | 0.8797 | 0.9796 |
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+ | 0.0625 | 0.9 | 16000 | 0.0630 | 0.8889 | 0.8819 | 0.8854 | 0.9804 |
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+ | 0.0712 | 0.93 | 16500 | 0.0621 | 0.8913 | 0.8818 | 0.8866 | 0.9806 |
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+ | 0.0629 | 0.96 | 17000 | 0.0618 | 0.8965 | 0.8815 | 0.8889 | 0.9810 |
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+ | 0.0649 | 0.99 | 17500 | 0.0618 | 0.8953 | 0.8806 | 0.8879 | 0.9809 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.38.2
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+ - Pytorch 2.2.1+cu121
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+ - Datasets 2.18.0
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+ - Tokenizers 0.15.2