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distilbert_sa_GLUE_Experiment_logit_kd_stsb_384
This model is a fine-tuned version of distilbert-base-uncased on the GLUE STSB dataset. It achieves the following results on the evaluation set:
- Loss: 1.1777
- Pearson: 0.0643
- Spearmanr: 0.0642
- Combined Score: 0.0643
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 | Pearson | Spearmanr | Combined Score |
---|---|---|---|---|---|---|
1.9276 | 1.0 | 23 | 1.2132 | 0.0304 | 0.0277 | 0.0290 |
1.0768 | 2.0 | 46 | 1.1777 | 0.0643 | 0.0642 | 0.0643 |
1.022 | 3.0 | 69 | 1.2883 | 0.0649 | 0.0800 | 0.0724 |
0.9488 | 4.0 | 92 | 1.2808 | 0.1320 | 0.1373 | 0.1347 |
0.8423 | 5.0 | 115 | 1.2344 | 0.1839 | 0.2056 | 0.1947 |
0.7144 | 6.0 | 138 | 1.2392 | 0.2083 | 0.2102 | 0.2092 |
0.6064 | 7.0 | 161 | 1.2926 | 0.1790 | 0.1825 | 0.1807 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2