bert-mean-pooling-model
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.4229
- Accuracy: 0.6715
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: 3.694799201458697e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.6791 | 1.0 | 312 | 0.6666 | 0.5957 |
| 0.3841 | 2.0 | 624 | 0.7171 | 0.6390 |
| 0.2341 | 3.0 | 936 | 1.4229 | 0.6715 |
Framework versions
- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Tokenizers 0.21.1
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Model tree for Zonuralp/bert-mean-pooling-model
Base model
google-bert/bert-base-uncased