MU-Bench: Benchmarking Machine Unlearning
Collection
Benchmark machine unlearning (MU) in a wide range of tasks, domains, modalities. • 18 items • Updated • 1
imdb_42
This model is a fine-tuned version of distilbert/distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.0838
- eval_accuracy: 0.9781
- eval_runtime: 20.6358
- eval_samples_per_second: 1211.49
- eval_steps_per_second: 4.749
- step: 0
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
Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2+cu118
- Datasets 2.18.0
- Tokenizers 0.15.2
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Model tree for jialicheng/imdb_distilbert-base
Base model
distilbert/distilbert-base-uncased