bert-base-uncased-finetuned-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: 0.4066
- Accuracy: 0.9118
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: 16
- 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 | Accuracy | Validation Loss |
|---|---|---|---|---|
| 0.3365 | 1.0 | 6547 | 0.9065 | 0.2398 |
| 0.1938 | 2.0 | 13094 | 0.9109 | 0.2898 |
| 0.1171 | 3.0 | 19641 | 0.9132 | 0.3919 |
Framework versions
- Transformers 4.45.0.dev0
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1
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Model tree for w05230505/bert-base-uncased-finetuned-qnli
Base model
google-bert/bert-base-uncased