File size: 9,337 Bytes
86bb4f6 1a37cac 86bb4f6 1a37cac 86bb4f6 1a37cac 86bb4f6 1a37cac 86bb4f6 1a37cac 86bb4f6 1a37cac 86bb4f6 1a37cac 3592313 86bb4f6 3592313 86bb4f6 3592313 86bb4f6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: bert_base_tcm_teste
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert_base_tcm_teste
This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0192
- Criterio Julgamento Precision: 0.7209
- Criterio Julgamento Recall: 0.8942
- Criterio Julgamento F1: 0.7983
- Criterio Julgamento Number: 104
- Data Sessao Precision: 0.6351
- Data Sessao Recall: 0.8545
- Data Sessao F1: 0.7287
- Data Sessao Number: 55
- Modalidade Licitacao Precision: 0.9224
- Modalidade Licitacao Recall: 0.9596
- Modalidade Licitacao F1: 0.9406
- Modalidade Licitacao Number: 421
- Numero Exercicio Precision: 0.8872
- Numero Exercicio Recall: 0.9351
- Numero Exercicio F1: 0.9105
- Numero Exercicio Number: 185
- Objeto Licitacao Precision: 0.2348
- Objeto Licitacao Recall: 0.4576
- Objeto Licitacao F1: 0.3103
- Objeto Licitacao Number: 59
- Valor Objeto Precision: 0.5424
- Valor Objeto Recall: 0.7805
- Valor Objeto F1: 0.64
- Valor Objeto Number: 41
- Overall Precision: 0.7683
- Overall Recall: 0.8971
- Overall F1: 0.8277
- Overall Accuracy: 0.9948
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50.0
### Training results
| 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 | Valor Objeto Precision | Valor Objeto Recall | Valor Objeto F1 | Valor Objeto Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:--------------------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:------------------------------:|:---------------------------:|:-----------------------:|:---------------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.0346 | 0.96 | 2750 | 0.0329 | 0.6154 | 0.8462 | 0.7126 | 104 | 0.5495 | 0.9091 | 0.6849 | 55 | 0.8482 | 0.9287 | 0.8866 | 421 | 0.7438 | 0.9730 | 0.8431 | 185 | 0.0525 | 0.3220 | 0.0903 | 59 | 0.4762 | 0.7317 | 0.5769 | 41 | 0.5565 | 0.8763 | 0.6807 | 0.9880 |
| 0.0309 | 1.92 | 5500 | 0.0322 | 0.6694 | 0.7788 | 0.72 | 104 | 0.5976 | 0.8909 | 0.7153 | 55 | 0.9178 | 0.9549 | 0.9360 | 421 | 0.8211 | 0.8432 | 0.8320 | 185 | 0.15 | 0.2034 | 0.1727 | 59 | 0.2203 | 0.3171 | 0.26 | 41 | 0.7351 | 0.8243 | 0.7771 | 0.9934 |
| 0.0179 | 2.88 | 8250 | 0.0192 | 0.7209 | 0.8942 | 0.7983 | 104 | 0.6351 | 0.8545 | 0.7287 | 55 | 0.9224 | 0.9596 | 0.9406 | 421 | 0.8872 | 0.9351 | 0.9105 | 185 | 0.2348 | 0.4576 | 0.3103 | 59 | 0.5424 | 0.7805 | 0.64 | 41 | 0.7683 | 0.8971 | 0.8277 | 0.9948 |
| 0.0174 | 3.84 | 11000 | 0.0320 | 0.7522 | 0.8173 | 0.7834 | 104 | 0.5741 | 0.5636 | 0.5688 | 55 | 0.8881 | 0.9430 | 0.9147 | 421 | 0.8490 | 0.8811 | 0.8647 | 185 | 0.2436 | 0.3220 | 0.2774 | 59 | 0.5370 | 0.7073 | 0.6105 | 41 | 0.7719 | 0.8370 | 0.8031 | 0.9946 |
| 0.0192 | 4.8 | 13750 | 0.0261 | 0.6744 | 0.8365 | 0.7468 | 104 | 0.6190 | 0.7091 | 0.6610 | 55 | 0.9169 | 0.9430 | 0.9297 | 421 | 0.8404 | 0.8541 | 0.8472 | 185 | 0.2059 | 0.3559 | 0.2609 | 59 | 0.5088 | 0.7073 | 0.5918 | 41 | 0.7521 | 0.8451 | 0.7959 | 0.9949 |
| 0.0158 | 5.76 | 16500 | 0.0250 | 0.6641 | 0.8173 | 0.7328 | 104 | 0.5610 | 0.8364 | 0.6715 | 55 | 0.9199 | 0.9549 | 0.9371 | 421 | 0.9167 | 0.9514 | 0.9337 | 185 | 0.1912 | 0.4407 | 0.2667 | 59 | 0.4828 | 0.6829 | 0.5657 | 41 | 0.7386 | 0.8821 | 0.8040 | 0.9948 |
| 0.0126 | 6.72 | 19250 | 0.0267 | 0.6694 | 0.7981 | 0.7281 | 104 | 0.6386 | 0.9636 | 0.7681 | 55 | 0.8723 | 0.9572 | 0.9128 | 421 | 0.8812 | 0.9622 | 0.9199 | 185 | 0.2180 | 0.4915 | 0.3021 | 59 | 0.5323 | 0.8049 | 0.6408 | 41 | 0.7308 | 0.9006 | 0.8068 | 0.9945 |
| 0.0162 | 7.68 | 22000 | 0.0328 | 0.675 | 0.7788 | 0.7232 | 104 | 0.6604 | 0.6364 | 0.6481 | 55 | 0.9263 | 0.9549 | 0.9404 | 421 | 0.8535 | 0.9135 | 0.8825 | 185 | 0.2471 | 0.3559 | 0.2917 | 59 | 0.5091 | 0.6829 | 0.5833 | 41 | 0.7788 | 0.8509 | 0.8133 | 0.9948 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|