<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->
add_BERT_24_qqp
This model is a fine-tuned version of gokuls/add_bert_12_layer_model_complete_training_new on the GLUE QQP dataset. It achieves the following results on the evaluation set:
- Loss: 0.4356
- Accuracy: 0.8049
- F1: 0.7302
- Combined Score: 0.7675
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: 128
- eval_batch_size: 128
- 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 |
|---|---|---|---|---|---|---|
| 0.5487 | 1.0 | 2843 | 0.5164 | 0.7477 | 0.6465 | 0.6971 |
| 0.4981 | 2.0 | 5686 | 0.4939 | 0.7635 | 0.6487 | 0.7061 |
| 0.4835 | 3.0 | 8529 | 0.4990 | 0.7568 | 0.6143 | 0.6856 |
| 0.4719 | 4.0 | 11372 | 0.4912 | 0.7637 | 0.6417 | 0.7027 |
| 0.4632 | 5.0 | 14215 | 0.4881 | 0.7680 | 0.6619 | 0.7150 |
| 0.4584 | 6.0 | 17058 | 0.4839 | 0.7679 | 0.6580 | 0.7129 |
| 0.4425 | 7.0 | 19901 | 0.4774 | 0.7723 | 0.6914 | 0.7319 |
| 0.4308 | 8.0 | 22744 | 0.4679 | 0.7738 | 0.6650 | 0.7194 |
| 0.4102 | 9.0 | 25587 | 0.4536 | 0.7873 | 0.6914 | 0.7393 |
| 0.3909 | 10.0 | 28430 | 0.4512 | 0.7895 | 0.7153 | 0.7524 |
| 0.3787 | 11.0 | 31273 | 0.4681 | 0.7959 | 0.7134 | 0.7547 |
| 0.3538 | 12.0 | 34116 | 0.4487 | 0.7981 | 0.7095 | 0.7538 |
| 0.3313 | 13.0 | 36959 | 0.4356 | 0.8049 | 0.7302 | 0.7675 |
| 0.3053 | 14.0 | 39802 | 0.4410 | 0.8081 | 0.7448 | 0.7764 |
| 0.2785 | 15.0 | 42645 | 0.4896 | 0.7942 | 0.7450 | 0.7696 |
| 0.2516 | 16.0 | 45488 | 0.4969 | 0.8055 | 0.7510 | 0.7782 |
| 0.2254 | 17.0 | 48331 | 0.5079 | 0.8129 | 0.7535 | 0.7832 |
| 0.2017 | 18.0 | 51174 | 0.5186 | 0.8113 | 0.7560 | 0.7836 |
Framework versions
- Transformers 4.30.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.13.0
- Tokenizers 0.13.3
