hBERTv2_new_pretrain_qnli
This model is a fine-tuned version of gokuls/bert_12_layer_model_v2_complete_training_new on the GLUE QNLI dataset. It achieves the following results on the evaluation set:
- Loss: 0.6870
- Accuracy: 0.5502
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 |
|---|---|---|---|---|
| 0.6921 | 1.0 | 819 | 0.6903 | 0.5050 |
| 0.6906 | 2.0 | 1638 | 0.6870 | 0.5502 |
| 0.6889 | 3.0 | 2457 | 0.6924 | 0.5232 |
| 0.6932 | 4.0 | 3276 | 0.6916 | 0.5261 |
| 0.6927 | 5.0 | 4095 | 0.6920 | 0.5314 |
| 0.6918 | 6.0 | 4914 | 0.6897 | 0.5297 |
| 0.6927 | 7.0 | 5733 | 0.6930 | 0.5087 |
Framework versions
- Transformers 4.29.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.12.0
- Tokenizers 0.13.3
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Dataset used to train gokuls/hBERTv2_new_pretrain_qnli
Evaluation results
- Accuracy on GLUE QNLIvalidation set self-reported0.550