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sa_BERT_no_pretrain_qqp
This model is a fine-tuned version of bert-base-uncased on the GLUE QQP dataset. It achieves the following results on the evaluation set:
- Loss: 0.4355
- Accuracy: 0.7934
- F1: 0.6836
- Combined Score: 0.7385
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: 4e-05
- train_batch_size: 96
- eval_batch_size: 96
- 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
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score |
|---|---|---|---|---|---|---|
| 0.5241 | 1.0 | 3791 | 0.4947 | 0.7638 | 0.6550 | 0.7094 |
| 0.4527 | 2.0 | 7582 | 0.4524 | 0.7853 | 0.7027 | 0.7440 |
| 0.404 | 3.0 | 11373 | 0.4355 | 0.7934 | 0.6836 | 0.7385 |
| 0.3675 | 4.0 | 15164 | 0.4407 | 0.8038 | 0.7438 | 0.7738 |
| 0.3315 | 5.0 | 18955 | 0.4426 | 0.8060 | 0.7368 | 0.7714 |
| 0.3031 | 6.0 | 22746 | 0.4437 | 0.8067 | 0.7444 | 0.7755 |
| 0.2747 | 7.0 | 26537 | 0.4359 | 0.8046 | 0.7523 | 0.7785 |
| 0.2441 | 8.0 | 30328 | 0.4718 | 0.8074 | 0.7547 | 0.7811 |
Framework versions
- Transformers 4.30.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.12.0
- Tokenizers 0.13.3
