bert-base-uncased-qnli
This model is a fine-tuned version of bert-base-uncased on the GLUE QNLI dataset. It achieves the following results on the evaluation set:
- Loss: 0.3208
- Accuracy: 0.9125
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: 2e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.289 | 1.0 | 3274 | 0.2289 | 0.9094 |
| 0.1801 | 2.0 | 6548 | 0.2493 | 0.9118 |
| 0.1074 | 3.0 | 9822 | 0.3208 | 0.9125 |
Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
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Model tree for JeremiahZ/bert-base-uncased-qnli
Base model
google-bert/bert-base-uncasedDataset used to train JeremiahZ/bert-base-uncased-qnli
Evaluation results
- Accuracy on GLUE QNLIself-reported0.913