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furina_seed42_eng_amh_esp

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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_amh_esp
    results: []

furina_seed42_eng_amh_esp

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

  • Loss: 0.0131
  • Spearman Corr: 0.8481

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 0.0247 0.6933
No log 1.18 400 0.0227 0.7395
No log 1.76 600 0.0217 0.7604
0.0471 2.35 800 0.0192 0.7685
0.0471 2.94 1000 0.0186 0.7805
0.0471 3.53 1200 0.0178 0.7884
0.0196 4.12 1400 0.0179 0.7961
0.0196 4.71 1600 0.0177 0.7990
0.0196 5.29 1800 0.0178 0.8035
0.0196 5.88 2000 0.0164 0.8057
0.0145 6.47 2200 0.0167 0.8070
0.0145 7.06 2400 0.0159 0.8110
0.0145 7.65 2600 0.0165 0.8121
0.0106 8.24 2800 0.0161 0.8110
0.0106 8.82 3000 0.0159 0.8148
0.0106 9.41 3200 0.0155 0.8195
0.008 10.0 3400 0.0151 0.8227
0.008 10.59 3600 0.0149 0.8253
0.008 11.18 3800 0.0154 0.8244
0.008 11.76 4000 0.0147 0.8251
0.0064 12.35 4200 0.0148 0.8249
0.0064 12.94 4400 0.0149 0.8287
0.0064 13.53 4600 0.0147 0.8297
0.0052 14.12 4800 0.0142 0.8347
0.0052 14.71 5000 0.0148 0.8314
0.0052 15.29 5200 0.0141 0.8341
0.0052 15.88 5400 0.0139 0.8386
0.0045 16.47 5600 0.0139 0.8350
0.0045 17.06 5800 0.0137 0.8389
0.0045 17.65 6000 0.0136 0.8402
0.004 18.24 6200 0.0139 0.8400
0.004 18.82 6400 0.0138 0.8414
0.004 19.41 6600 0.0136 0.8433
0.0036 20.0 6800 0.0140 0.8420
0.0036 20.59 7000 0.0136 0.8434
0.0036 21.18 7200 0.0137 0.8451
0.0036 21.76 7400 0.0133 0.8445
0.0032 22.35 7600 0.0135 0.8451
0.0032 22.94 7800 0.0136 0.8447
0.0032 23.53 8000 0.0136 0.8449
0.003 24.12 8200 0.0132 0.8463
0.003 24.71 8400 0.0131 0.8472
0.003 25.29 8600 0.0133 0.8477
0.003 25.88 8800 0.0135 0.8472
0.0028 26.47 9000 0.0134 0.8478
0.0028 27.06 9200 0.0131 0.8477
0.0028 27.65 9400 0.0131 0.8478
0.0026 28.24 9600 0.0130 0.8482
0.0026 28.82 9800 0.0131 0.8479
0.0026 29.41 10000 0.0131 0.8481

Framework versions

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