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furina_afr_corr_5e-06

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metadata
base_model: yihongLiu/furina
tags:
  - generated_from_trainer
model-index:
  - name: furina_afr_corr_5e-06
    results: []

furina_afr_corr_5e-06

This model is a fine-tuned version of yihongLiu/furina on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0213
  • Spearman Corr: 0.7660

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-06
  • train_batch_size: 32
  • eval_batch_size: 128
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 30
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Spearman Corr
No log 0.85 200 0.0813 0.0483
No log 1.69 400 0.0439 0.3738
0.1125 2.54 600 0.0360 0.6162
0.1125 3.38 800 0.0287 0.6660
0.035 4.23 1000 0.0271 0.6987
0.035 5.07 1200 0.0248 0.7139
0.035 5.92 1400 0.0252 0.7230
0.026 6.77 1600 0.0246 0.7173
0.026 7.61 1800 0.0230 0.7344
0.0227 8.46 2000 0.0236 0.7361
0.0227 9.3 2200 0.0223 0.7470
0.0203 10.15 2400 0.0227 0.7462
0.0203 10.99 2600 0.0224 0.7477
0.0203 11.84 2800 0.0242 0.7483
0.0186 12.68 3000 0.0218 0.7537
0.0186 13.53 3200 0.0229 0.7528
0.0175 14.38 3400 0.0227 0.7552
0.0175 15.22 3600 0.0221 0.7602
0.0163 16.07 3800 0.0216 0.7612
0.0163 16.91 4000 0.0216 0.7619
0.0163 17.76 4200 0.0217 0.7616
0.0154 18.6 4400 0.0215 0.7612
0.0154 19.45 4600 0.0212 0.7651
0.0146 20.3 4800 0.0216 0.7634
0.0146 21.14 5000 0.0213 0.7641
0.0141 21.99 5200 0.0223 0.7629
0.0141 22.83 5400 0.0213 0.7664
0.0141 23.68 5600 0.0214 0.7635
0.0134 24.52 5800 0.0214 0.7649
0.0134 25.37 6000 0.0216 0.7637
0.013 26.22 6200 0.0214 0.7644
0.013 27.06 6400 0.0213 0.7656
0.013 27.91 6600 0.0214 0.7661
0.0127 28.75 6800 0.0212 0.7655
0.0127 29.6 7000 0.0213 0.7660

Framework versions

  • Transformers 4.37.2
  • Pytorch 2.2.0+cu121
  • Datasets 2.17.0
  • Tokenizers 0.15.2