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--- |
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license: cc-by-nc-4.0 |
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base_model: microsoft/mdeberta-v3-base |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: deberta-base-azerbaijani-v2 |
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results: [] |
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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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# deberta-base-azerbaijani-v2 |
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This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on a various cleaned community corpus. It achieves the following results on the evaluation set: |
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- Loss: 0.9572 |
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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 4 X A100 80GB GPU for 23 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: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 4 |
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- total_train_batch_size: 128 |
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- total_eval_batch_size: 128 |
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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: 5 |
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### Training results |
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Perplexity at epoch 5: 2.6 |
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### Framework versions |
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- Transformers 4.40.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
