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distilbert_add_GLUE_Experiment_qqp
This model is a fine-tuned version of distilbert-base-uncased on the GLUE QQP dataset. It achieves the following results on the evaluation set:
- Loss: 0.4050
- Accuracy: 0.8320
- F1: 0.7639
- Combined Score: 0.7979
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 | F1 | Combined Score |
---|---|---|---|---|---|---|
0.5406 | 1.0 | 1422 | 0.4844 | 0.7648 | 0.6276 | 0.6962 |
0.4161 | 2.0 | 2844 | 0.4451 | 0.8044 | 0.6939 | 0.7491 |
0.3079 | 3.0 | 4266 | 0.4050 | 0.8320 | 0.7639 | 0.7979 |
0.2338 | 4.0 | 5688 | 0.4633 | 0.8388 | 0.7715 | 0.8052 |
0.1801 | 5.0 | 7110 | 0.5597 | 0.8346 | 0.7489 | 0.7918 |
0.1433 | 6.0 | 8532 | 0.5641 | 0.8460 | 0.7774 | 0.8117 |
0.1155 | 7.0 | 9954 | 0.5940 | 0.8481 | 0.7889 | 0.8185 |
0.0963 | 8.0 | 11376 | 0.6896 | 0.8438 | 0.7670 | 0.8054 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.8.0
- Tokenizers 0.13.2