speaker-segmentation-sula-hf_luganda_mental_health_dataset-v1S
This model is a fine-tuned version of pyannote/speaker-diarization-3.0 on the Beijuka/hf_luganda_mental_health_dataset dataset. It achieves the following results on the evaluation set:
- Loss: 0.2251
- Model Preparation Time: 0.0171
- Der: 0.0933
- False Alarm: 0.0237
- Missed Detection: 0.0387
- Confusion: 0.0309
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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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: cosine
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Der | False Alarm | Missed Detection | Confusion |
|---|---|---|---|---|---|---|---|---|
| 0.3335 | 1.0 | 114 | 0.2926 | 0.0171 | 0.1212 | 0.0378 | 0.0501 | 0.0333 |
| 0.2908 | 2.0 | 228 | 0.2792 | 0.0171 | 0.1137 | 0.0434 | 0.0439 | 0.0264 |
| 0.2660 | 3.0 | 342 | 0.2788 | 0.0171 | 0.1117 | 0.0408 | 0.0447 | 0.0262 |
| 0.2431 | 4.0 | 456 | 0.2868 | 0.0171 | 0.1131 | 0.0361 | 0.0509 | 0.0261 |
| 0.2398 | 5.0 | 570 | 0.2723 | 0.0171 | 0.1093 | 0.0354 | 0.0495 | 0.0244 |
| 0.2457 | 6.0 | 684 | 0.2685 | 0.0171 | 0.1067 | 0.0411 | 0.0433 | 0.0223 |
| 0.2368 | 7.0 | 798 | 0.2697 | 0.0171 | 0.1100 | 0.0422 | 0.0440 | 0.0239 |
| 0.2211 | 8.0 | 912 | 0.2699 | 0.0171 | 0.1091 | 0.0416 | 0.0444 | 0.0230 |
| 0.2258 | 9.0 | 1026 | 0.2697 | 0.0171 | 0.1089 | 0.0410 | 0.0446 | 0.0233 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2