ibama_29102024_20241029175942
Model description
Dataset com 1750 registros.
Média do tamanho dos contextos: 2467.439831104856
["train"] : 1421 registros
["test"] : 329 registros
{'exact_match': 6.990881458966565, 'f1': 41.36428322707063}
Resultados:
:: Filtrando registros de ['test'] onde o contexto possuia até 6697 caracteres.
Modelo: ibama_29102024_20241029175942 :
'exact_match': 3.9755351681957185, 'f1': 38.429269059347Modelo: pierreguillou/bert-base-cased-squad-v1.1-portuguese :
'exact_match': 6.422018348623853, 'f1': 37.47550481021018Modelo: neuralmind/bert-base-portuguese-cased :
'exact_match': 0.0, 'f1': 21.520346204352514:: Filtrando registros de ['test'] onde o contexto possuia até 512 caracteres.
Modelo: ibama_29102024_20241029175942 :
'exact_match': 12.67605633802817, 'f1': 70.76635146201694 Modelo: pierreguillou/bert-base-cased-squad-v1.1-portuguese :
'exact_match': 1.408450704225352, 'f1': 38.42469128241023Modelo: neuralmind/bert-base-portuguese-cased :
'exact_match': 0.0, 'f1': 15.264048430063177Training results
It achieves the following results on the evaluation set:
Epoch Training Loss Validation Loss
1 No log 4.598662
2 No log 4.266841
3 No log 4.225364
4 No log 4.181730
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.0.2
- Tokenizers 0.19.1
Notebook
