hBERTv2_new_pretrain_48_ver2_wnli
This model is a fine-tuned version of gokuls/bert_12_layer_model_v2_complete_training_new_48 on the GLUE WNLI dataset. It achieves the following results on the evaluation set:
- Loss: 0.6858
- 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: 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 |
|---|---|---|---|---|
| 0.8132 | 1.0 | 10 | 0.7425 | 0.4366 |
| 0.7131 | 2.0 | 20 | 0.6970 | 0.4366 |
| 0.7083 | 3.0 | 30 | 0.6858 | 0.5634 |
| 0.6956 | 4.0 | 40 | 0.6939 | 0.5352 |
| 0.7103 | 5.0 | 50 | 0.7313 | 0.4366 |
| 0.7169 | 6.0 | 60 | 0.7041 | 0.4366 |
| 0.7039 | 7.0 | 70 | 0.6862 | 0.5634 |
| 0.7041 | 8.0 | 80 | 0.6919 | 0.5352 |
Framework versions
- Transformers 4.34.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.14.5
- Tokenizers 0.14.1
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Dataset used to train gokuls/hBERTv2_new_pretrain_48_ver2_wnli
Evaluation results
- Accuracy on GLUE WNLIvalidation set self-reported0.563