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--- |
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license: mit |
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base_model: FacebookAI/xlm-roberta-base |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: xlm-roberta-base_pan_loss_2e-05 |
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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_pan_loss_2e-05 |
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This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/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: 0.0216 |
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- Spearman Corr: 0.7776 |
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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: 128 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 64 |
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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: 30 |
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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 | Spearman Corr | |
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|:-------------:|:-----:|:----:|:---------------:|:-------------:| |
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| No log | 0.85 | 200 | 0.0210 | 0.7733 | |
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| No log | 1.69 | 400 | 0.0215 | 0.7798 | |
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| 0.0009 | 2.54 | 600 | 0.0219 | 0.7770 | |
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| 0.0009 | 3.38 | 800 | 0.0212 | 0.7807 | |
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| 0.0006 | 4.23 | 1000 | 0.0224 | 0.7806 | |
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| 0.0006 | 5.07 | 1200 | 0.0210 | 0.7800 | |
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| 0.0006 | 5.92 | 1400 | 0.0208 | 0.7799 | |
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| 0.0004 | 6.77 | 1600 | 0.0214 | 0.7793 | |
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| 0.0004 | 7.61 | 1800 | 0.0216 | 0.7795 | |
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| 0.0003 | 8.46 | 2000 | 0.0207 | 0.7819 | |
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| 0.0003 | 9.3 | 2200 | 0.0209 | 0.7826 | |
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| 0.0004 | 10.15 | 2400 | 0.0209 | 0.7793 | |
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| 0.0004 | 10.99 | 2600 | 0.0207 | 0.7804 | |
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| 0.0004 | 11.84 | 2800 | 0.0210 | 0.7808 | |
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| 0.0004 | 12.68 | 3000 | 0.0216 | 0.7776 | |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 2.2.0+cu121 |
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- Datasets 2.17.0 |
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- Tokenizers 0.15.2 |
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