BRlkl/BingoGuard-bert-large-base-benchmarks

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BingoGuard-bert-large-base-benchmarks

This model is a fine-tuned version of neuralmind/bert-base-portuguese-cased on the None dataset. It achieves the following results on the evaluation set:

• —Loss: 0.9187
• —Accuracy: 0.8026
• —F1: 0.7892

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: 5e-05
• —trainbatchsize: 32
• —evalbatchsize: 32
• —seed: 42
• —gradientaccumulationsteps: 2
• —totaltrainbatch_size: 64
• —optimizer: Use adamwtorchfused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• —lrschedulertype: linear
• —num_epochs: 8

Training results

Framework versions

• —Transformers 4.55.4
• —Pytorch 2.8.0+cu128
• —Datasets 3.6.0
• —Tokenizers 0.21.4