sa_BERT_48_qnli
This model is a fine-tuned version of gokuls/bert_base_48 on the GLUE QNLI dataset. It achieves the following results on the evaluation set:
- Loss: 0.6317
- Accuracy: 0.6983
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: 96
- eval_batch_size: 96
- 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 |
|---|---|---|---|---|
| 0.674 | 1.0 | 1092 | 0.6500 | 0.6253 |
| 0.6353 | 2.0 | 2184 | 0.6513 | 0.6244 |
| 0.5987 | 3.0 | 3276 | 0.6552 | 0.6357 |
| 0.5429 | 4.0 | 4368 | 0.6414 | 0.6760 |
| 0.465 | 5.0 | 5460 | 0.6317 | 0.6983 |
| 0.3904 | 6.0 | 6552 | 0.6376 | 0.7146 |
| 0.3215 | 7.0 | 7644 | 0.7152 | 0.7137 |
| 0.2584 | 8.0 | 8736 | 0.7690 | 0.7278 |
| 0.2096 | 9.0 | 9828 | 0.8507 | 0.7128 |
| 0.1685 | 10.0 | 10920 | 0.9555 | 0.7201 |
Framework versions
- Transformers 4.30.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.13.0
- Tokenizers 0.13.3
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Dataset used to train gokuls/sa_BERT_48_qnli
Evaluation results
- Accuracy on GLUE QNLIvalidation set self-reported0.698