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--- |
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base_model: yihongLiu/furina |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: furina_afr_corr_5e-06 |
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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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# furina_afr_corr_5e-06 |
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This model is a fine-tuned version of [yihongLiu/furina](https://huggingface.co/yihongLiu/furina) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0213 |
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- Spearman Corr: 0.7660 |
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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 | 0.85 | 200 | 0.0813 | 0.0483 | |
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| No log | 1.69 | 400 | 0.0439 | 0.3738 | |
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| 0.1125 | 2.54 | 600 | 0.0360 | 0.6162 | |
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| 0.1125 | 3.38 | 800 | 0.0287 | 0.6660 | |
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| 0.035 | 4.23 | 1000 | 0.0271 | 0.6987 | |
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| 0.035 | 5.07 | 1200 | 0.0248 | 0.7139 | |
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| 0.035 | 5.92 | 1400 | 0.0252 | 0.7230 | |
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| 0.026 | 6.77 | 1600 | 0.0246 | 0.7173 | |
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| 0.026 | 7.61 | 1800 | 0.0230 | 0.7344 | |
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| 0.0227 | 8.46 | 2000 | 0.0236 | 0.7361 | |
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| 0.0227 | 9.3 | 2200 | 0.0223 | 0.7470 | |
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| 0.0203 | 10.15 | 2400 | 0.0227 | 0.7462 | |
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| 0.0203 | 10.99 | 2600 | 0.0224 | 0.7477 | |
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| 0.0203 | 11.84 | 2800 | 0.0242 | 0.7483 | |
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| 0.0186 | 12.68 | 3000 | 0.0218 | 0.7537 | |
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| 0.0186 | 13.53 | 3200 | 0.0229 | 0.7528 | |
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| 0.0175 | 14.38 | 3400 | 0.0227 | 0.7552 | |
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| 0.0175 | 15.22 | 3600 | 0.0221 | 0.7602 | |
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| 0.0163 | 16.07 | 3800 | 0.0216 | 0.7612 | |
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| 0.0163 | 16.91 | 4000 | 0.0216 | 0.7619 | |
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| 0.0163 | 17.76 | 4200 | 0.0217 | 0.7616 | |
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| 0.0154 | 18.6 | 4400 | 0.0215 | 0.7612 | |
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| 0.0154 | 19.45 | 4600 | 0.0212 | 0.7651 | |
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| 0.0146 | 20.3 | 4800 | 0.0216 | 0.7634 | |
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| 0.0146 | 21.14 | 5000 | 0.0213 | 0.7641 | |
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| 0.0141 | 21.99 | 5200 | 0.0223 | 0.7629 | |
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| 0.0141 | 22.83 | 5400 | 0.0213 | 0.7664 | |
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| 0.0141 | 23.68 | 5600 | 0.0214 | 0.7635 | |
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| 0.0134 | 24.52 | 5800 | 0.0214 | 0.7649 | |
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| 0.0134 | 25.37 | 6000 | 0.0216 | 0.7637 | |
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| 0.013 | 26.22 | 6200 | 0.0214 | 0.7644 | |
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| 0.013 | 27.06 | 6400 | 0.0213 | 0.7656 | |
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| 0.013 | 27.91 | 6600 | 0.0214 | 0.7661 | |
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| 0.0127 | 28.75 | 6800 | 0.0212 | 0.7655 | |
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| 0.0127 | 29.6 | 7000 | 0.0213 | 0.7660 | |
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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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