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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_lr0.0001_seed42_basic_original_kin-hau-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_lr0.0001_seed42_basic_original_kin-hau-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: 0.0490 |
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- Spearman Corr: 0.0413 |
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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: 0.0001 |
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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 | 0.0521 | 0.0335 | |
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| 0.0641 | 3.19 | 400 | 0.0491 | 0.0517 | |
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| 0.0587 | 4.78 | 600 | 0.0499 | 0.0819 | |
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| 0.0574 | 6.37 | 800 | 0.0551 | -0.0486 | |
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| 0.0575 | 7.97 | 1000 | 0.0498 | 0.0406 | |
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| 0.0575 | 9.56 | 1200 | 0.0525 | -0.0395 | |
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| 0.0566 | 11.16 | 1400 | 0.0499 | 0.0551 | |
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| 0.0564 | 12.75 | 1600 | 0.0509 | 0.0279 | |
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| 0.0557 | 14.34 | 1800 | 0.0513 | -0.0191 | |
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| 0.0558 | 15.94 | 2000 | 0.0516 | 0.0619 | |
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| 0.0558 | 17.53 | 2200 | 0.0511 | 0.0308 | |
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| 0.0555 | 19.12 | 2400 | 0.0490 | 0.0413 | |
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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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