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
ddi_42
This model is a fine-tuned version of microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3300
- Accuracy: 0.9547
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: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 791 | 0.1947 | 0.9471 |
| 0.1709 | 2.0 | 1582 | 0.2474 | 0.9527 |
| 0.0734 | 3.0 | 2373 | 0.2485 | 0.9475 |
| 0.0475 | 4.0 | 3164 | 0.2686 | 0.9499 |
| 0.0475 | 5.0 | 3955 | 0.3196 | 0.9475 |
| 0.0284 | 6.0 | 4746 | 0.3014 | 0.9527 |
| 0.0194 | 7.0 | 5537 | 0.3125 | 0.9523 |
| 0.0133 | 8.0 | 6328 | 0.3641 | 0.9491 |
| 0.0065 | 9.0 | 7119 | 0.3300 | 0.9547 |
| 0.0065 | 10.0 | 7910 | 0.3502 | 0.9543 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2+cu118
- Datasets 2.18.0
- Tokenizers 0.15.2
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