hBERTv2_data_aug_mnli
This model is a fine-tuned version of gokuls/bert_12_layer_model_v2 on the GLUE MNLI dataset. It achieves the following results on the evaluation set:
- Loss: 1.0988
- Accuracy: 0.3182
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 |
|---|---|---|---|---|
| 1.0988 | 1.0 | 31440 | 1.0988 | 0.3182 |
| 1.0985 | 2.0 | 62880 | 1.0992 | 0.3182 |
| 1.0985 | 3.0 | 94320 | 1.0991 | 0.3182 |
| 1.0985 | 4.0 | 125760 | 1.0991 | 0.3182 |
| 1.0985 | 5.0 | 157200 | 1.0988 | 0.3182 |
| 1.0985 | 6.0 | 188640 | 1.0988 | 0.3182 |
| 1.0985 | 7.0 | 220080 | 1.0988 | 0.3182 |
| 1.0985 | 8.0 | 251520 | 1.0988 | 0.3182 |
| 1.0985 | 9.0 | 282960 | 1.0988 | 0.3182 |
| 1.0985 | 10.0 | 314400 | 1.0988 | 0.3182 |
| 1.0985 | 11.0 | 345840 | 1.0988 | 0.3182 |
| 1.0985 | 12.0 | 377280 | 1.0988 | 0.3182 |
| 1.0985 | 13.0 | 408720 | 1.0988 | 0.3182 |
| 1.0985 | 14.0 | 440160 | 1.0988 | 0.3182 |
| 1.0985 | 15.0 | 471600 | 1.0988 | 0.3182 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.14.0a0+410ce96
- Datasets 2.10.1
- Tokenizers 0.13.2
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