add_BERT_no_pretrain_wnli
This model is a fine-tuned version of on the GLUE WNLI dataset. It achieves the following results on the evaluation set:
- Loss: 0.6852
- 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: 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 |
|---|---|---|---|---|
| 0.9529 | 1.0 | 5 | 0.6860 | 0.5634 |
| 0.762 | 2.0 | 10 | 0.8068 | 0.4366 |
| 0.7199 | 3.0 | 15 | 0.6987 | 0.4366 |
| 0.7092 | 4.0 | 20 | 0.6958 | 0.5634 |
| 0.7149 | 5.0 | 25 | 0.6854 | 0.5634 |
| 0.7069 | 6.0 | 30 | 0.6956 | 0.4366 |
| 0.7008 | 7.0 | 35 | 0.6986 | 0.4366 |
| 0.7079 | 8.0 | 40 | 0.6852 | 0.5634 |
| 0.7444 | 9.0 | 45 | 0.7382 | 0.4366 |
| 0.7147 | 10.0 | 50 | 0.7009 | 0.5634 |
| 0.7318 | 11.0 | 55 | 0.7316 | 0.4366 |
| 0.7212 | 12.0 | 60 | 0.6858 | 0.5634 |
| 0.7043 | 13.0 | 65 | 0.7075 | 0.4366 |
Framework versions
- Transformers 4.30.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.12.0
- Tokenizers 0.13.3
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Dataset used to train gokuls/add_BERT_no_pretrain_wnli
Evaluation results
- Accuracy on GLUE WNLIvalidation set self-reported0.563