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# xlm-roberta-base-vsmec-10

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the tmnam20/VieGLUE/VSMEC dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2904
- Accuracy: 0.5306

## 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: 10
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1928        | 2.87  | 500  | 1.2880          | 0.5379   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.2.0.dev20231203+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0

{"language": ["en"], "license": "mit", "tags": ["generated_from_trainer"], "datasets": ["tmnam20/VieGLUE"], "metrics": ["accuracy"], "base_model": "xlm-roberta-base", "model-index": [{"name": "xlm-roberta-base-vsmec-10", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "tmnam20/VieGLUE/VSMEC", "type": "tmnam20/VieGLUE", "config": "vsmec", "split": "validation", "args": "vsmec"}, "metrics": [{"type": "accuracy", "value": 0.5306122448979592, "name": "Accuracy"}]}]}]}
transformers safetensors xlm-roberta text-classification generated_from_trainer en dataset:tmnam20/VieGLUE base_model:xlm-roberta-base license:mit model-index autotrain_compatible endpoints_compatible region:us