MU-Bench: Benchmarking Machine Unlearning
Collection
Benchmark machine unlearning (MU) in a wide range of tasks, domains, modalities.
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18 items
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Updated
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1
superb_ks_42
This model is a fine-tuned version of openai/whisper-tiny on the superb dataset. It achieves the following results on the evaluation set:
- Loss: 0.1434
- Accuracy: 0.9801
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: 32
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.8486 | 1.0 | 1597 | 0.2380 | 0.9385 |
| 0.0986 | 2.0 | 3194 | 0.1777 | 0.9598 |
| 0.0773 | 3.0 | 4791 | 0.1249 | 0.9738 |
| 0.0532 | 4.0 | 6388 | 0.1078 | 0.9782 |
| 0.0472 | 5.0 | 7985 | 0.1258 | 0.9766 |
| 0.0322 | 6.0 | 9582 | 0.1365 | 0.9772 |
| 0.0258 | 7.0 | 11179 | 0.1338 | 0.9798 |
| 0.0231 | 8.0 | 12776 | 0.1447 | 0.9796 |
| 0.0171 | 9.0 | 14373 | 0.1435 | 0.9797 |
| 0.0142 | 10.0 | 15970 | 0.1434 | 0.9801 |
Framework versions
- Transformers 4.40.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
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Model tree for jialicheng/whisper-tiny-speech_commands
Base model
openai/whisper-tiny