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furina_seed42_eng_kin_hau

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xlm-roberta
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metadata
base_model: yihongLiu/furina
tags:
  - generated_from_trainer
model-index:
  - name: furina_seed42_eng_kin_hau
    results: []

furina_seed42_eng_kin_hau

This model is a fine-tuned version of yihongLiu/furina on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0202
  • Spearman Corr: 0.7925

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: 2e-05
  • 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 0.59 200 0.0427 0.4859
No log 1.18 400 0.0345 0.6546
No log 1.77 600 0.0295 0.6863
0.0527 2.36 800 0.0272 0.6929
0.0527 2.95 1000 0.0284 0.7195
0.0527 3.55 1200 0.0271 0.7192
0.0244 4.14 1400 0.0253 0.7366
0.0244 4.73 1600 0.0236 0.7434
0.0244 5.32 1800 0.0239 0.7436
0.0244 5.91 2000 0.0255 0.7483
0.017 6.5 2200 0.0230 0.7575
0.017 7.09 2400 0.0231 0.7541
0.017 7.68 2600 0.0230 0.7568
0.0123 8.27 2800 0.0228 0.7575
0.0123 8.86 3000 0.0233 0.7633
0.0123 9.45 3200 0.0228 0.7679
0.0092 10.04 3400 0.0226 0.7647
0.0092 10.64 3600 0.0220 0.7704
0.0092 11.23 3800 0.0214 0.7717
0.0092 11.82 4000 0.0219 0.7768
0.0074 12.41 4200 0.0215 0.7760
0.0074 13.0 4400 0.0209 0.7792
0.0074 13.59 4600 0.0206 0.7796
0.006 14.18 4800 0.0211 0.7770
0.006 14.77 5000 0.0211 0.7801
0.006 15.36 5200 0.0216 0.7807
0.006 15.95 5400 0.0205 0.7841
0.0052 16.54 5600 0.0211 0.7846
0.0052 17.13 5800 0.0206 0.7873
0.0052 17.73 6000 0.0202 0.7863
0.0045 18.32 6200 0.0205 0.7858
0.0045 18.91 6400 0.0202 0.7886
0.0045 19.5 6600 0.0208 0.7876
0.0042 20.09 6800 0.0203 0.7902
0.0042 20.68 7000 0.0211 0.7850
0.0042 21.27 7200 0.0204 0.7899
0.0042 21.86 7400 0.0207 0.7905
0.0037 22.45 7600 0.0202 0.7925

Framework versions

  • Transformers 4.37.2
  • Pytorch 2.1.0+cu121
  • Datasets 2.16.1
  • Tokenizers 0.15.1