| --- |
| library_name: transformers |
| tags: |
| - generated_from_trainer |
| model-index: |
| - name: BengaliDiarization |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # BengaliDiarization |
|
|
| This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.2690 |
| - Der: 0.0945 |
| - False Alarm: 0.0140 |
| - Missed Detection: 0.0450 |
| - Confusion: 0.0355 |
|
|
| ## Model description |
|
|
| More information needed |
|
|
| ## Intended uses & limitations |
|
|
| More information needed |
|
|
| ## Training and evaluation data |
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|
| More information needed |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
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|
| The following hyperparameters were used during training: |
| - learning_rate: 8e-05 |
| - train_batch_size: 128 |
| - eval_batch_size: 128 |
| - 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: linear |
| - lr_scheduler_warmup_steps: 100 |
| - num_epochs: 20 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Der | False Alarm | Missed Detection | Confusion | |
| |:-------------:|:-------:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:| |
| | 0.5565 | 1.7241 | 100 | 0.3220 | 0.1152 | 0.0158 | 0.0628 | 0.0366 | |
| | 0.498 | 3.4483 | 200 | 0.3009 | 0.1055 | 0.0164 | 0.0474 | 0.0418 | |
| | 0.4955 | 5.1724 | 300 | 0.2863 | 0.0979 | 0.0161 | 0.0431 | 0.0387 | |
| | 0.4766 | 6.8966 | 400 | 0.2784 | 0.0981 | 0.0162 | 0.0429 | 0.0391 | |
| | 0.4726 | 8.6207 | 500 | 0.2755 | 0.0970 | 0.0151 | 0.0435 | 0.0385 | |
| | 0.4574 | 10.3448 | 600 | 0.2740 | 0.0966 | 0.0145 | 0.0450 | 0.0371 | |
| | 0.4441 | 12.0690 | 700 | 0.2719 | 0.0956 | 0.0143 | 0.0445 | 0.0369 | |
| | 0.4157 | 13.7931 | 800 | 0.2736 | 0.0958 | 0.0141 | 0.0449 | 0.0368 | |
| | 0.4916 | 15.5172 | 900 | 0.2722 | 0.0954 | 0.0141 | 0.0453 | 0.0361 | |
| | 0.4358 | 17.2414 | 1000 | 0.2682 | 0.0941 | 0.0141 | 0.0448 | 0.0352 | |
| | 0.4287 | 18.9655 | 1100 | 0.2690 | 0.0945 | 0.0140 | 0.0450 | 0.0355 | |
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|
| ### Framework versions |
|
|
| - Transformers 4.57.1 |
| - Pytorch 2.9.1+cu128 |
| - Datasets 4.0.0 |
| - Tokenizers 0.22.1 |
|
|
