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hBERTv2_new_pretrain_wnli
This model is a fine-tuned version of gokuls/bert_12_layer_model_v2_complete_training_new on the GLUE WNLI dataset. It achieves the following results on the evaluation set:
- Loss: 0.6857
- Accuracy: 0.5634
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 |
|---|---|---|---|---|
| 0.8646 | 1.0 | 5 | 0.7422 | 0.4366 |
| 0.7094 | 2.0 | 10 | 0.7290 | 0.4366 |
| 0.7047 | 3.0 | 15 | 0.7053 | 0.5634 |
| 0.7203 | 4.0 | 20 | 0.7022 | 0.4366 |
| 0.7 | 5.0 | 25 | 0.6977 | 0.4366 |
| 0.7098 | 6.0 | 30 | 0.6885 | 0.5634 |
| 0.695 | 7.0 | 35 | 0.7045 | 0.4366 |
| 0.7053 | 8.0 | 40 | 0.6858 | 0.5634 |
| 0.7095 | 9.0 | 45 | 0.7070 | 0.4366 |
| 0.7012 | 10.0 | 50 | 0.6857 | 0.5634 |
| 0.6995 | 11.0 | 55 | 0.6969 | 0.4507 |
| 0.6913 | 12.0 | 60 | 0.6875 | 0.5634 |
| 0.6963 | 13.0 | 65 | 0.6959 | 0.4789 |
| 0.6996 | 14.0 | 70 | 0.7190 | 0.4366 |
| 0.6957 | 15.0 | 75 | 0.6963 | 0.5634 |
Framework versions
- Transformers 4.29.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.12.0
- Tokenizers 0.13.3
