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+ ---
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+ language:
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+ - en
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+ license: mit
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+ base_model: xlm-roberta-large
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - tmnam20/VieGLUE
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+ metrics:
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+ - accuracy
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+ - f1
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+ model-index:
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+ - name: xlm-roberta-large-qqp-1
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+ results:
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+ - task:
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+ name: Text Classification
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+ type: text-classification
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+ dataset:
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+ name: tmnam20/VieGLUE/QQP
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+ type: tmnam20/VieGLUE
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+ config: qqp
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+ split: validation
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+ args: qqp
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.9047736829087312
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+ - name: F1
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+ type: f1
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+ value: 0.8721609775534599
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+ ---
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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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+
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+ # xlm-roberta-large-qqp-1
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+
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+ This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the tmnam20/VieGLUE/QQP dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.2725
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+ - Accuracy: 0.9048
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+ - F1: 0.8722
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+ - Combined Score: 0.8885
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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: 16
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+ - seed: 1
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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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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score |
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+ |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
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+ | 0.3045 | 0.88 | 10000 | 0.2747 | 0.8808 | 0.8324 | 0.8566 |
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+ | 0.2256 | 1.76 | 20000 | 0.2695 | 0.8957 | 0.8617 | 0.8787 |
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+ | 0.1643 | 2.64 | 30000 | 0.2808 | 0.9019 | 0.8684 | 0.8851 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.36.0
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+ - Pytorch 2.1.0+cu121
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+ - Datasets 2.15.0
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+ - Tokenizers 0.15.0