--- language: - en license: mit base_model: microsoft/mdeberta-v3-base tags: - generated_from_trainer datasets: - tmnam20/VieGLUE metrics: - accuracy model-index: - name: mdeberta-v3-base-vnrte-100 results: - task: name: Text Classification type: text-classification dataset: name: tmnam20/VieGLUE/VNRTE type: tmnam20/VieGLUE config: vnrte split: validation args: vnrte metrics: - name: Accuracy type: accuracy value: 0.9987248963978324 --- # mdeberta-v3-base-vnrte-100 This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the tmnam20/VieGLUE/VNRTE dataset. It achieves the following results on the evaluation set: - Loss: 0.0063 - Accuracy: 0.9987 ## 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: 32 - eval_batch_size: 16 - seed: 100 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0031 | 1.28 | 500 | 0.0002 | 1.0 | | 0.0002 | 2.55 | 1000 | 0.0011 | 0.9997 | ### Framework versions - Transformers 4.36.0 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0