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--- |
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license: cc-by-nc-4.0 |
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base_model: xlm-roberta-base |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: roberta-base-azerbaijani |
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results: [] |
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datasets: |
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- hajili/azerbaijani-various-corpus |
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language: |
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- az |
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metrics: |
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- perplexity |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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This model is a continued pre-trained version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on a various cleaned community corpus. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.1697 |
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We thank Microsoft Accelerating Foundation Models Research Program for supporting our research. |
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Authors: Mammad Hajili, Duygu Ataman |
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## Model description |
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The model was trained on masked language model task on a single V100 GPU for 68 hours. For downstream tasks, it requires to be fine-tuned based on objective of the task. |
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## Training and evaluation data |
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The training data is clean mix of various Azerbaijani corpus shared by the community. |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 3.0 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:-------:|:---------------:| |
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| 1.6126 | 0.2500 | 100910 | 1.4818 | |
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| 1.4961 | 0.5000 | 201820 | 1.4163 | |
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| 1.4324 | 0.7500 | 302730 | 1.3371 | |
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| 1.387 | 1.0000 | 403640 | 1.3070 | |
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| 1.3488 | 1.2500 | 504550 | 1.2649 | |
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| 1.323 | 1.5000 | 605460 | 1.2581 | |
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| 1.3006 | 1.7500 | 706370 | 1.2066 | |
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| 1.2866 | 2.0000 | 807280 | 1.2095 | |
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| 1.2646 | 2.2500 | 908190 | 1.2019 | |
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| 1.2492 | 2.5000 | 1009100 | 1.1779 | |
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| 1.2425 | 2.7500 | 1110010 | 1.1742 | |
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- Validation loss at epoch 3: 1.1697 |
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- Perplexity at epoch 3: 3.22 |
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### Framework versions |
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- Transformers 4.40.1 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.0 |
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- Tokenizers 0.19.1 |
