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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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datasets: |
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- common_language |
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metrics: |
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- accuracy |
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model-index: |
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- name: language-detection-fine-tuned-on-xlm-roberta-base |
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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: common_language |
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type: common_language |
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args: full |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9738386718094919 |
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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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# language-detection-fine-tuned-on-xlm-roberta-base |
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the [common_language](https://huggingface.co/datasets/common_language) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1886 |
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- Accuracy: 0.9738 |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-05 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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- seed: 42 |
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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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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 1 |
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- mixed_precision_training: Native AMP |
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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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| 0.1 | 1.0 | 22194 | 0.1886 | 0.9738 | |
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### Framework versions |
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- Transformers 4.12.5 |
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- Pytorch 1.10.0+cu111 |
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- Datasets 1.15.1 |
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- Tokenizers 0.10.3 |
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### Notebook |
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[notebook](https://github.com/IvanLauLinTiong/language-detector/blob/main/xlm_roberta_base_commonlanguage_language_detector.ipynb) |
