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README.md
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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-base
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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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model-index:
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- name: xlm-roberta-base-sst2-100
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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/SST2
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type: tmnam20/VieGLUE
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config: sst2
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split: validation
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args: sst2
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.8944954128440367
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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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# xlm-roberta-base-sst2-100
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the tmnam20/VieGLUE/SST2 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3776
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- Accuracy: 0.8945
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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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: 100
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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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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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| 0.4011 | 0.24 | 500 | 0.3866 | 0.8475 |
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| 0.313 | 0.48 | 1000 | 0.3352 | 0.8647 |
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| 0.2626 | 0.71 | 1500 | 0.4805 | 0.8349 |
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| 0.2597 | 0.95 | 2000 | 0.3691 | 0.8681 |
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| 0.2068 | 1.19 | 2500 | 0.3089 | 0.8991 |
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| 0.2347 | 1.43 | 3000 | 0.3957 | 0.8842 |
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| 0.2133 | 1.66 | 3500 | 0.3049 | 0.8991 |
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| 0.1986 | 1.9 | 4000 | 0.3184 | 0.8956 |
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| 0.1596 | 2.14 | 4500 | 0.3846 | 0.8853 |
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| 0.1457 | 2.38 | 5000 | 0.3667 | 0.8968 |
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| 0.1861 | 2.61 | 5500 | 0.3675 | 0.8922 |
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| 0.1401 | 2.85 | 6000 | 0.3853 | 0.8899 |
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### Framework versions
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- Transformers 4.35.2
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- Pytorch 2.2.0.dev20231203+cu121
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- Datasets 2.15.0
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- Tokenizers 0.15.0
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