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hBERTv2_new_pretrain_w_init_48_ver2_qqp
This model is a fine-tuned version of gokuls/bert_12_layer_model_v2_complete_training_new_wt_init_48 on the GLUE QQP dataset. It achieves the following results on the evaluation set:
- Loss: 0.5073
- Accuracy: 0.7574
- F1: 0.6487
- Combined Score: 0.7030
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: 64
- eval_batch_size: 64
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score |
|---|---|---|---|---|---|---|
| 0.5438 | 1.0 | 5686 | 0.5073 | 0.7574 | 0.6487 | 0.7030 |
| 0.5215 | 2.0 | 11372 | 0.5411 | 0.7379 | 0.6475 | 0.6927 |
| 0.5467 | 3.0 | 17058 | 0.6578 | 0.6323 | 0.0047 | 0.3185 |
| 0.5441 | 4.0 | 22744 | 0.5636 | 0.7429 | 0.5943 | 0.6686 |
| 0.5524 | 5.0 | 28430 | 0.5958 | 0.7216 | 0.5353 | 0.6284 |
| 0.5635 | 6.0 | 34116 | 0.5578 | 0.7358 | 0.5946 | 0.6652 |
Framework versions
- Transformers 4.34.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.14.5
- Tokenizers 0.14.1
