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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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metrics: |
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- accuracy |
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model-index: |
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- name: xlm-roberta-base-finetuned-partypredictor |
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results: [] |
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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-finetuned-partypredictor |
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.6783 |
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- Accuracy: 0.2495 |
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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: 8 |
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- eval_batch_size: 8 |
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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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- num_epochs: 16 |
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### Training results |
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| Training Loss | Epoch | Step | Accuracy | Validation Loss | |
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|:-------------:|:-----:|:-----:|:--------:|:---------------:| |
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| 1.7766 | 0.76 | 5000 | 0.1331 | 1.8909 | |
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| 1.7572 | 1.52 | 10000 | 0.1331 | 1.7809 | |
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| 1.7543 | 2.28 | 15000 | 0.1031 | 1.8126 | |
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| 1.7273 | 3.05 | 20000 | 0.1331 | 1.8048 | |
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| 1.7435 | 3.81 | 25000 | 0.2675 | 1.7892 | |
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| 1.7606 | 4.99 | 30000 | 0.3121 | 1.7848 | |
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| 1.7546 | 5.82 | 35000 | 0.3121 | 1.7737 | |
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| 1.7417 | 6.65 | 40000 | 0.3121 | 1.7699 | |
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| 1.7007 | 7.48 | 45000 | 0.1529 | 1.7088 | |
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| 1.7542 | 7.87 | 50000 | 0.1331 | 1.8058 | |
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| 1.75 | 8.66 | 55000 | 0.1331 | 1.8347 | |
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| 1.7505 | 10.05 | 60000 | 1.8079 | 0.1231 | |
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| 1.7545 | 10.88 | 65000 | 1.7756 | 0.3121 | |
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| 1.7322 | 11.72 | 70000 | 1.7371 | 0.2707 | |
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| 1.7082 | 12.56 | 75000 | 1.6886 | 0.2419 | |
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| 1.7035 | 13.4 | 80000 | 1.6844 | 0.2638 | |
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| 1.6889 | 14.23 | 85000 | 1.6728 | 0.2525 | |
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| 1.6779 | 15.07 | 90000 | 1.6737 | 0.2490 | |
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| 1.6821 | 15.91 | 95000 | 1.6783 | 0.2495 | |
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### Framework versions |
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- Transformers 4.26.0 |
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- Pytorch 1.13.1 |
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- Datasets 2.9.0 |
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- Tokenizers 0.13.2 |
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