bert-base-uncased-finetuned-qqp
This model is a fine-tuned version of bert-base-uncased on the GLUE QQP dataset. It achieves the following results on the evaluation set:
- Loss: 0.3674
- Accuracy: 0.9106
- F1: 0.8793
- Combined Score: 0.8950
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 | Combined Score | F1 | Validation Loss |
|---|---|---|---|---|---|---|
| 0.2942 | 1.0 | 22741 | 0.9009 | 0.8845 | 0.8681 | 0.2476 |
| 0.1919 | 2.0 | 45482 | 0.9080 | 0.8920 | 0.8761 | 0.2706 |
| 0.1342 | 3.0 | 68223 | 0.9109 | 0.8957 | 0.8805 | 0.3568 |
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-qqp
Base model
google-bert/bert-base-uncasedDataset used to train w05230505/bert-base-uncased-finetuned-qqp
Evaluation results
- Accuracy on GLUE QQPself-reported0.911
- F1 on GLUE QQPself-reported0.879