hBERTv2_data_aug_qnli
This model is a fine-tuned version of gokuls/bert_12_layer_model_v2 on the GLUE QNLI dataset. It achieves the following results on the evaluation set:
- Loss: 0.6931
- Accuracy: 0.5054
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 |
|---|---|---|---|---|
| 0.6934 | 1.0 | 16604 | 0.6931 | 0.5054 |
| 0.6932 | 2.0 | 33208 | 0.6931 | 0.5054 |
| 0.6932 | 3.0 | 49812 | 0.6931 | 0.4946 |
| 0.6932 | 4.0 | 66416 | 0.6931 | 0.5054 |
| 0.6932 | 5.0 | 83020 | 0.6932 | 0.4946 |
| 0.6932 | 6.0 | 99624 | 0.6931 | 0.5054 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.14.0a0+410ce96
- Datasets 2.10.1
- Tokenizers 0.13.2
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