| | --- |
| | base_model: HariprasathSB/indic-whisper-vulnerable |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | model-index: |
| | - name: audio-abuse-feature |
| | 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. --> |
| |
|
| | # audio-abuse-feature |
| |
|
| | This model is a fine-tuned version of [HariprasathSB/indic-whisper-vulnerable](https://huggingface.co/HariprasathSB/indic-whisper-vulnerable) on the None dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.4489 |
| | - Accuracy: 0.8814 |
| | - Macro Precision: 0.8557 |
| | - Macro Recall: 0.8472 |
| | - Macro F1-score: 0.8513 |
| |
|
| | ## 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: 1e-05 |
| | - train_batch_size: 16 |
| | - eval_batch_size: 16 |
| | - seed: 42 |
| | - gradient_accumulation_steps: 4 |
| | - total_train_batch_size: 64 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - lr_scheduler_warmup_ratio: 0.01 |
| | - num_epochs: 5 |
| | - mixed_precision_training: Native AMP |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro Precision | Macro Recall | Macro F1-score | |
| | |:-------------:|:------:|:----:|:---------------:|:--------:|:---------------:|:------------:|:--------------:| |
| | | 0.4633 | 0.4367 | 50 | 0.3753 | 0.8327 | 0.8321 | 0.8314 | 0.8317 | |
| | | 0.345 | 0.8734 | 100 | 0.4170 | 0.8241 | 0.8612 | 0.8126 | 0.8150 | |
| | | 0.2592 | 1.3100 | 150 | 0.3357 | 0.8512 | 0.8506 | 0.8502 | 0.8504 | |
| | | 0.2097 | 1.7467 | 200 | 0.3142 | 0.8758 | 0.8757 | 0.8744 | 0.8749 | |
| | | 0.1545 | 2.1834 | 250 | 0.3551 | 0.8721 | 0.8713 | 0.8718 | 0.8715 | |
| | | 0.0829 | 2.6201 | 300 | 0.3916 | 0.8795 | 0.8797 | 0.8778 | 0.8786 | |
| | | 0.0944 | 3.0568 | 350 | 0.4137 | 0.8721 | 0.8714 | 0.8730 | 0.8718 | |
| | | 0.0416 | 3.4934 | 400 | 0.5350 | 0.8659 | 0.8677 | 0.8631 | 0.8646 | |
| | | 0.0469 | 3.9301 | 450 | 0.5129 | 0.8733 | 0.8727 | 0.8726 | 0.8727 | |
| | | 0.0247 | 4.3668 | 500 | 0.5543 | 0.8708 | 0.8713 | 0.8689 | 0.8698 | |
| | | 0.0208 | 4.8035 | 550 | 0.5611 | 0.8696 | 0.8691 | 0.8688 | 0.8689 | |
| |
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| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.41.2 |
| | - Pytorch 2.1.2 |
| | - Datasets 2.19.2 |
| | - Tokenizers 0.19.1 |
| |
|
