Beijuka/AnnoMI-full_speaker_role_id-bert-base-uncased-v1

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AnnoMI-fullspeakerrole_id-bert-base-uncased-v1

This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

• —Loss: 0.3878
• —Accuracy: 0.8819
• —Precision Macro: 0.8819
• —Recall Macro: 0.8819
• —F1 Macro: 0.8819
• —Precision Weighted: 0.8820
• —Recall Weighted: 0.8819
• —F1 Weighted: 0.8819
• —Report: precision recall f1-score support

0 0.89 0.88 0.88 683 1 0.88 0.89 0.88 672

accuracy 0.88 1355 macro avg 0.88 0.88 0.88 1355 weighted avg 0.88 0.88 0.88 1355

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: 8
• —evalbatchsize: 16
• —seed: 42
• —gradientaccumulationsteps: 4
• —totaltrainbatch_size: 32
• —optimizer: Use OptimizerNames.ADAMWTORCHFUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• —lrschedulertype: linear
• —lrschedulerwarmup_steps: 100
• —num_epochs: 20
• —mixedprecisiontraining: Native AMP

Training results

0 0.90 0.87 0.89 683 1 0.87 0.90 0.89 672

accuracy 0.89 1355 macro avg 0.89 0.89 0.89 1355 weighted avg 0.89 0.89 0.89 1355 | | 0.217 | 2.9506 | 1000 | 0.2941 | 0.8819 | 0.8824 | 0.8821 | 0.8819 | 0.8825 | 0.8819 | 0.8819 | precision recall f1-score support

0 0.90 0.86 0.88 683 1 0.87 0.90 0.88 672

accuracy 0.88 1355 macro avg 0.88 0.88 0.88 1355 weighted avg 0.88 0.88 0.88 1355 | | 0.189 | 4.4251 | 1500 | 0.3902 | 0.8797 | 0.8797 | 0.8798 | 0.8797 | 0.8798 | 0.8797 | 0.8797 | precision recall f1-score support

0 0.89 0.87 0.88 683 1 0.87 0.89 0.88 672

accuracy 0.88 1355 macro avg 0.88 0.88 0.88 1355 weighted avg 0.88 0.88 0.88 1355 | | 0.167 | 5.9004 | 2000 | 0.3878 | 0.8819 | 0.8819 | 0.8819 | 0.8819 | 0.8820 | 0.8819 | 0.8819 | precision recall f1-score support

0 0.89 0.88 0.88 683 1 0.88 0.89 0.88 672

accuracy 0.88 1355 macro avg 0.88 0.88 0.88 1355 weighted avg 0.88 0.88 0.88 1355 |

Framework versions

• —Transformers 4.57.6
• —Pytorch 2.9.0+cu126
• —Datasets 4.0.0
• —Tokenizers 0.22.2