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distilbert_add_GLUE_Experiment_sst2_256
This model is a fine-tuned version of distilbert-base-uncased on the GLUE SST2 dataset. It achieves the following results on the evaluation set:
- Loss: 0.5464
- Accuracy: 0.7443
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: 256
- eval_batch_size: 256
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy |
---|---|---|---|---|
0.6877 | 1.0 | 264 | 0.6993 | 0.5092 |
0.5228 | 2.0 | 528 | 0.5464 | 0.7443 |
0.3483 | 3.0 | 792 | 0.5494 | 0.7775 |
0.2768 | 4.0 | 1056 | 0.7034 | 0.75 |
0.2496 | 5.0 | 1320 | 0.5917 | 0.7706 |
0.2287 | 6.0 | 1584 | 0.6191 | 0.7661 |
0.2145 | 7.0 | 1848 | 0.6363 | 0.7672 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.8.0
- Tokenizers 0.13.2