hajili/roberta-base-azerbaijani-whole-word-masking

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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

We thank Microsoft Accelerating Foundation Models Research Program for supporting our research. Authors: Mammad Hajili, Duygu Ataman

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.

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
• —trainbatchsize: 8
• —evalbatchsize: 8
• —seed: 42
• —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• —lrschedulertype: linear
• —num_epochs: 3.0
• —mixedprecisiontraining: Native AMP

Training results

Framework versions

• —Transformers 4.40.1
• —Pytorch 2.3.0+cu121
• —Datasets 2.19.0
• —Tokenizers 0.19.1