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albert_model

This model is a fine-tuned version of albert-base-v2 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6560
  • Accuracy: 0.9070
  • F1: 0.8852
  • Recall: 0.9122

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: 1e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 15

Training results

Training Loss Epoch Step Validation Loss Accuracy F1 Recall
No log 1.0 167 0.3571 0.8351 0.8142 0.9198
No log 2.0 334 0.2670 0.8891 0.8683 0.9313
0.3358 3.0 501 0.2643 0.9115 0.8885 0.8969
0.3358 4.0 668 0.3804 0.9130 0.8910 0.9046
0.3358 5.0 835 0.4376 0.9070 0.8848 0.9084
0.1007 6.0 1002 0.4957 0.9100 0.8859 0.8893
0.1007 7.0 1169 0.6375 0.8801 0.8601 0.9389
0.1007 8.0 1336 0.5978 0.8996 0.8780 0.9198
0.012 9.0 1503 0.6101 0.9025 0.8816 0.9237
0.012 10.0 1670 0.6209 0.9085 0.8847 0.8931
0.012 11.0 1837 0.6485 0.9010 0.8787 0.9122
0.0007 12.0 2004 0.6480 0.9070 0.8852 0.9122
0.0007 13.0 2171 0.6527 0.9055 0.8835 0.9122
0.0007 14.0 2338 0.6557 0.9055 0.8835 0.9122
0.0002 15.0 2505 0.6560 0.9070 0.8852 0.9122

Framework versions

  • Transformers 4.29.2
  • Pytorch 2.0.1+cu118
  • Datasets 2.12.0
  • Tokenizers 0.13.3
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