bert-finetuned-mrpc
This model is a fine-tuned version of bert-base-uncased on the glue dataset. It achieves the following results on the evaluation set:
- Loss: 0.5401
- Accuracy: 0.8088
- F1: 0.8682
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: 8
- 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 | F1 |
|---|---|---|---|---|---|
| No log | 1.0 | 459 | 0.4804 | 0.7770 | 0.8384 |
| 0.5792 | 2.0 | 918 | 0.5523 | 0.7966 | 0.8668 |
| 0.4419 | 3.0 | 1377 | 0.5401 | 0.8088 | 0.8682 |
Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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Model tree for chineidu/bert-finetuned-mrpc
Base model
google-bert/bert-base-uncasedDataset used to train chineidu/bert-finetuned-mrpc
Evaluation results
- Accuracy on gluevalidation set self-reported0.809
- F1 on gluevalidation set self-reported0.868