address-normalization-model
This model is a fine-tuned version of bert-base-multilingual-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0950
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.0877 | 1.0 | 666 | 0.1114 |
| 0.0738 | 2.0 | 1332 | 0.1145 |
| 0.0641 | 3.0 | 1998 | 0.1029 |
| 0.0573 | 4.0 | 2664 | 0.1178 |
| 0.0131 | 5.0 | 3330 | 0.1452 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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Model tree for RaihanShakeel/address-normalization-model
Base model
google-bert/bert-base-multilingual-cased