This model is a continued pre-trained version of xlm-roberta-base on an various cleaned community corpus. It achieves the following results on the evaluation set:
- Loss: 2.8039
Model description
The model was trained on whole word masked language model task on a single V100 GPU for 55 hours. For downstream tasks, it requires to be fine-tuned based on objective of the task.
Intended uses & limitations
Since some of dependent datasets have non-commercial use licences, the model is under cc-by-nc-4.0 licence.
Training and evaluation data
The training data is clean mix of various Azerbaijani corpus shared by the community.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- 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
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
3.4315 | 0.2500 | 100910 | 3.3178 |
3.2537 | 0.5000 | 201820 | 3.1369 |
3.1598 | 0.7500 | 302730 | 3.0042 |
3.0927 | 1.0000 | 403640 | 2.9691 |
3.0353 | 1.2500 | 504550 | 2.9385 |
2.9947 | 1.5000 | 605460 | 2.9062 |
2.9586 | 1.7500 | 706370 | 2.8547 |
2.9389 | 2.0000 | 807280 | 2.7979 |
2.9071 | 2.2500 | 908190 | 2.8124 |
2.8871 | 2.5000 | 1009100 | 2.7924 |
2.8792 | 2.7500 | 1110010 | 2.7697 |
Framework versions
- Transformers 4.40.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
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