bert-base-cased-finetuned-mrpc
This model is a fine-tuned version of bert-base-cased on the glue dataset. It achieves the following results on the evaluation set:
- Loss: 0.6843
- Accuracy: 0.8358
- F1: 0.8839
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 | 0.4446 | 0.8186 | 0.8737 |
| 0.5484 | 2.0 | 918 | 0.6035 | 0.8333 | 0.8885 |
| 0.3276 | 3.0 | 1377 | 0.6843 | 0.8358 | 0.8839 |
Framework versions
- Transformers 4.33.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.14.3
- Tokenizers 0.13.3
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Model tree for yangdechuan/bert-base-cased-finetuned-mrpc
Base model
google-bert/bert-base-casedDataset used to train yangdechuan/bert-base-cased-finetuned-mrpc
Evaluation results
- Accuracy on gluevalidation set self-reported0.836
- F1 on gluevalidation set self-reported0.884