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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_lr5e-06_seed42_basic_original_amh-esp-eng_train |
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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_lr5e-06_seed42_basic_original_amh-esp-eng_train |
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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: 0.0208 |
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- Spearman Corr: 0.7694 |
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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: 5e-06 |
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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 | 1.59 | 200 | 0.0178 | 0.7278 | |
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| 0.0931 | 3.17 | 400 | 0.0197 | 0.7452 | |
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| 0.0276 | 4.76 | 600 | 0.0183 | 0.7513 | |
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| 0.0243 | 6.35 | 800 | 0.0158 | 0.7667 | |
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| 0.0243 | 7.94 | 1000 | 0.0182 | 0.7692 | |
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| 0.021 | 9.52 | 1200 | 0.0189 | 0.7704 | |
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| 0.0192 | 11.11 | 1400 | 0.0184 | 0.7656 | |
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| 0.0176 | 12.7 | 1600 | 0.0226 | 0.7691 | |
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| 0.0163 | 14.29 | 1800 | 0.0225 | 0.7696 | |
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| 0.0163 | 15.87 | 2000 | 0.0207 | 0.7718 | |
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| 0.0156 | 17.46 | 2200 | 0.0242 | 0.7697 | |
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| 0.0143 | 19.05 | 2400 | 0.0207 | 0.7664 | |
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| 0.0138 | 20.63 | 2600 | 0.0201 | 0.7721 | |
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| 0.013 | 22.22 | 2800 | 0.0208 | 0.7694 | |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.17.0 |
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- Tokenizers 0.15.2 |
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