AnnoMI-full_speaker_role_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
• train_batch_size: 8
• eval_batch_size: 16
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 32
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 100
• num_epochs: 20
• mixed_precision_training: 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