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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-sst2-1 |
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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/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: 0.8818807339449541 |
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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-sst2-1 |
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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: 0.3886 |
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- Accuracy: 0.8819 |
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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: 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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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 0.3646 | 0.24 | 500 | 0.3292 | 0.8555 | |
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| 0.3026 | 0.48 | 1000 | 0.4031 | 0.8658 | |
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| 0.2802 | 0.71 | 1500 | 0.3818 | 0.8716 | |
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| 0.2681 | 0.95 | 2000 | 0.3480 | 0.8693 | |
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| 0.2012 | 1.19 | 2500 | 0.3381 | 0.8819 | |
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| 0.2212 | 1.43 | 3000 | 0.3682 | 0.8784 | |
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| 0.2003 | 1.66 | 3500 | 0.3312 | 0.8899 | |
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| 0.2157 | 1.9 | 4000 | 0.3195 | 0.8899 | |
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| 0.1504 | 2.14 | 4500 | 0.3788 | 0.8933 | |
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| 0.1408 | 2.38 | 5000 | 0.4484 | 0.8819 | |
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| 0.1508 | 2.61 | 5500 | 0.4194 | 0.875 | |
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| 0.1604 | 2.85 | 6000 | 0.3730 | 0.8842 | |
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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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