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furina_seed42_eng_kin_amh

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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_amh
    results: []

furina_seed42_eng_kin_amh

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

  • Loss: 0.0191
  • Spearman Corr: 0.7822

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 0.65 200 0.0386 0.5856
No log 1.3 400 0.0256 0.6781
No log 1.95 600 0.0236 0.7129
0.0506 2.61 800 0.0225 0.7250
0.0506 3.26 1000 0.0225 0.7343
0.0506 3.91 1200 0.0215 0.7338
0.022 4.56 1400 0.0210 0.7492
0.022 5.21 1600 0.0219 0.7512
0.022 5.86 1800 0.0199 0.7600
0.016 6.51 2000 0.0209 0.7623
0.016 7.17 2200 0.0201 0.7579
0.016 7.82 2400 0.0203 0.7589
0.0115 8.47 2600 0.0204 0.7604
0.0115 9.12 2800 0.0201 0.7655
0.0115 9.77 3000 0.0201 0.7647
0.0089 10.42 3200 0.0195 0.7652
0.0089 11.07 3400 0.0191 0.7728
0.0089 11.73 3600 0.0197 0.7685
0.0072 12.38 3800 0.0190 0.7709
0.0072 13.03 4000 0.0192 0.7707
0.0072 13.68 4200 0.0189 0.7734
0.006 14.33 4400 0.0193 0.7743
0.006 14.98 4600 0.0191 0.7756
0.006 15.64 4800 0.0189 0.7757
0.0051 16.29 5000 0.0193 0.7733
0.0051 16.94 5200 0.0189 0.7796
0.0051 17.59 5400 0.0185 0.7810
0.0046 18.24 5600 0.0194 0.7781
0.0046 18.89 5800 0.0191 0.7787
0.0046 19.54 6000 0.0186 0.7789
0.0041 20.2 6200 0.0188 0.7798
0.0041 20.85 6400 0.0189 0.7796
0.0041 21.5 6600 0.0187 0.7797
0.0037 22.15 6800 0.0189 0.7787
0.0037 22.8 7000 0.0191 0.7822

Framework versions

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