--- language: - en license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - tmnam20/VieGLUE metrics: - accuracy model-index: - name: xlm-roberta-base-vtoc-1 results: - task: name: Text Classification type: text-classification dataset: name: tmnam20/VieGLUE/VTOC type: tmnam20/VieGLUE config: vtoc split: validation args: vtoc metrics: - name: Accuracy type: accuracy value: 0.8306936100491534 --- # xlm-roberta-base-vtoc-1 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the tmnam20/VieGLUE/VTOC dataset. It achieves the following results on the evaluation set: - Loss: 0.6228 - Accuracy: 0.8307 ## 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: 1 - 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5429 | 2.19 | 500 | 0.6635 | 0.8154 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.2.0.dev20231203+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0