Multilingual NLP
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ug_pos_tagger
This model is a fine-tuned version of bert-base-multilingual-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.8548
- Accuracy: 0.7457
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: 16
- seed: 42
- optimizer: Use 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 |
|---|---|---|---|---|
| 1.0582 | 1.0 | 83 | 0.9918 | 0.6856 |
| 0.9264 | 2.0 | 166 | 0.8798 | 0.7285 |
| 0.853 | 3.0 | 249 | 0.8548 | 0.7457 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
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Model tree for ankei/ug_pos_tagger
Base model
google-bert/bert-base-multilingual-cased