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bert_base_tcm_0.9_no_valor_objeto

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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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+ model-index:
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+ - name: bert_base_tcm_0.9_no_valor_objeto
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+ results: []
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+ ---
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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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+
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+ # bert_base_tcm_0.9_no_valor_objeto
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+
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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 None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0324
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+ - Criterio Julgamento Precision: 0.8141
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+ - Criterio Julgamento Recall: 0.8944
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+ - Criterio Julgamento F1: 0.8523
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+ - Criterio Julgamento Number: 142
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+ - Data Sessao Precision: 0.7524
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+ - Data Sessao Recall: 0.8778
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+ - Data Sessao F1: 0.8103
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+ - Data Sessao Number: 90
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+ - Modalidade Licitacao Precision: 0.9680
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+ - Modalidade Licitacao Recall: 0.9815
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+ - Modalidade Licitacao F1: 0.9747
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+ - Modalidade Licitacao Number: 648
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+ - Numero Exercicio Precision: 0.9494
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+ - Numero Exercicio Recall: 0.9667
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+ - Numero Exercicio F1: 0.9580
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+ - Numero Exercicio Number: 330
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+ - Objeto Licitacao Precision: 0.5878
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+ - Objeto Licitacao Recall: 0.7264
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+ - Objeto Licitacao F1: 0.6498
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+ - Objeto Licitacao Number: 106
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+ - Overall Precision: 0.8939
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+ - Overall Recall: 0.9407
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+ - Overall F1: 0.9167
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+ - Overall Accuracy: 0.9954
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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: 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: 10.0
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Criterio Julgamento Precision | Criterio Julgamento Recall | Criterio Julgamento F1 | Criterio Julgamento Number | Data Sessao Precision | Data Sessao Recall | Data Sessao F1 | Data Sessao Number | Modalidade Licitacao Precision | Modalidade Licitacao Recall | Modalidade Licitacao F1 | Modalidade Licitacao Number | Numero Exercicio Precision | Numero Exercicio Recall | Numero Exercicio F1 | Numero Exercicio Number | Objeto Licitacao Precision | Objeto Licitacao Recall | Objeto Licitacao F1 | Objeto Licitacao Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
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+ |:-------------:|:-----:|:-----:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:--------------------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:------------------------------:|:---------------------------:|:-----------------------:|:---------------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
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+ | 0.0159 | 1.0 | 3497 | 0.0176 | 0.8310 | 0.8310 | 0.8310 | 142 | 0.7909 | 0.9667 | 0.87 | 90 | 0.9564 | 0.9815 | 0.9688 | 648 | 0.9362 | 0.9788 | 0.9570 | 330 | 0.4460 | 0.5849 | 0.5061 | 106 | 0.8751 | 0.9316 | 0.9025 | 0.9953 |
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+ | 0.0161 | 2.0 | 6994 | 0.0191 | 0.8312 | 0.9014 | 0.8649 | 142 | 0.7890 | 0.9556 | 0.8643 | 90 | 0.9580 | 0.9846 | 0.9711 | 648 | 0.9475 | 0.9848 | 0.9658 | 330 | 0.5556 | 0.6604 | 0.6034 | 106 | 0.8920 | 0.9476 | 0.9189 | 0.9954 |
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+ | 0.0094 | 3.0 | 10491 | 0.0215 | 0.8125 | 0.9155 | 0.8609 | 142 | 0.7818 | 0.9556 | 0.86 | 90 | 0.9608 | 0.9846 | 0.9726 | 648 | 0.9503 | 0.9848 | 0.9673 | 330 | 0.5108 | 0.6698 | 0.5796 | 106 | 0.8834 | 0.9498 | 0.9154 | 0.9955 |
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+ | 0.0057 | 4.0 | 13988 | 0.0212 | 0.8269 | 0.9085 | 0.8658 | 142 | 0.8095 | 0.9444 | 0.8718 | 90 | 0.9697 | 0.9861 | 0.9778 | 648 | 0.9501 | 0.9818 | 0.9657 | 330 | 0.5290 | 0.6887 | 0.5984 | 106 | 0.8935 | 0.9498 | 0.9208 | 0.9960 |
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+ | 0.0049 | 5.0 | 17485 | 0.0214 | 0.8344 | 0.9225 | 0.8763 | 142 | 0.7905 | 0.9222 | 0.8513 | 90 | 0.9652 | 0.9830 | 0.9740 | 648 | 0.9474 | 0.9818 | 0.9643 | 330 | 0.5217 | 0.6792 | 0.5902 | 106 | 0.8894 | 0.9476 | 0.9176 | 0.9957 |
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+ | 0.0036 | 6.0 | 20982 | 0.0297 | 0.8397 | 0.9225 | 0.8792 | 142 | 0.7748 | 0.9556 | 0.8557 | 90 | 0.9636 | 0.9799 | 0.9717 | 648 | 0.9585 | 0.9788 | 0.9685 | 330 | 0.5435 | 0.7075 | 0.6148 | 106 | 0.8922 | 0.9498 | 0.9201 | 0.9953 |
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+ | 0.0016 | 7.0 | 24479 | 0.0297 | 0.8302 | 0.9296 | 0.8771 | 142 | 0.7925 | 0.9333 | 0.8571 | 90 | 0.9652 | 0.9830 | 0.9740 | 648 | 0.9467 | 0.9697 | 0.9581 | 330 | 0.5746 | 0.7264 | 0.6417 | 106 | 0.8948 | 0.9498 | 0.9215 | 0.9955 |
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+ | 0.0016 | 8.0 | 27976 | 0.0298 | 0.8212 | 0.8732 | 0.8464 | 142 | 0.8095 | 0.9444 | 0.8718 | 90 | 0.9666 | 0.9815 | 0.9740 | 648 | 0.9524 | 0.9697 | 0.9610 | 330 | 0.5746 | 0.7264 | 0.6417 | 106 | 0.8974 | 0.9438 | 0.9200 | 0.9955 |
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+ | 0.0011 | 9.0 | 31473 | 0.0319 | 0.7949 | 0.8732 | 0.8322 | 142 | 0.7788 | 0.9 | 0.8351 | 90 | 0.9650 | 0.9799 | 0.9724 | 648 | 0.9467 | 0.9697 | 0.9581 | 330 | 0.6016 | 0.7264 | 0.6581 | 106 | 0.8938 | 0.9400 | 0.9163 | 0.9954 |
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+ | 0.0011 | 10.0 | 34970 | 0.0324 | 0.8141 | 0.8944 | 0.8523 | 142 | 0.7524 | 0.8778 | 0.8103 | 90 | 0.9680 | 0.9815 | 0.9747 | 648 | 0.9494 | 0.9667 | 0.9580 | 330 | 0.5878 | 0.7264 | 0.6498 | 106 | 0.8939 | 0.9407 | 0.9167 | 0.9954 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.28.0.dev0
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+ - Pytorch 2.0.0+cu118
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+ - Datasets 2.11.0
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+ - Tokenizers 0.13.3