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bert-base-multilingual-cased
zues0102/bert-base-multilingual-cased
bert-base-multilingual-cased is a text classification model from zues0102. Use it when you need a label for a piece of text. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- bert
- Access
- Public
- Created
- May 29, 2025
- Updated
- May 29, 2025
- SHA
- 9041a345
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Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
bert-base-multilingual-cased
This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0125
- Accuracy: 0.9985
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: 64
- eval_batch_size: 128
- 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: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.0527 | 1.0 | 500 | 0.0274 | 0.9958 |
| 0.0139 | 2.0 | 1000 | 0.0212 | 0.997 |
| 0.0078 | 3.0 | 1500 | 0.0203 | 0.9962 |
| 0.0047 | 4.0 | 2000 | 0.0198 | 0.9972 |
| 0.0026 | 5.0 | 2500 | 0.0156 | 0.9975 |
| 0.0018 | 6.0 | 3000 | 0.0166 | 0.9978 |
| 0.001 | 7.0 | 3500 | 0.0188 | 0.9972 |
| 0.0011 | 8.0 | 4000 | 0.0169 | 0.9975 |
Framework versions
- Transformers 4.52.2
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- Created
- May 29, 2025
- Updated
- May 29, 2025