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hbertv1-emotion-logit_KD-tiny_ffn_2

This model is a fine-tuned version of gokuls/model_v1_complete_training_wt_init_48_tiny_freeze_new_ffn_2 on the emotion dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4740
  • Accuracy: 0.9005

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-05
  • train_batch_size: 64
  • eval_batch_size: 64
  • seed: 33
  • distributed_type: multi-GPU
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 50

Training results

Training Loss Epoch Step Validation Loss Accuracy
3.1189 1.0 250 2.6103 0.514
2.0804 2.0 500 1.4939 0.7695
1.2677 3.0 750 0.8999 0.8445
0.8885 4.0 1000 0.6887 0.874
0.7023 5.0 1250 0.5821 0.889
0.5796 6.0 1500 0.5364 0.8875
0.5106 7.0 1750 0.5043 0.89
0.4603 8.0 2000 0.5055 0.889
0.405 9.0 2250 0.4903 0.89
0.3782 10.0 2500 0.4793 0.8965
0.3488 11.0 2750 0.4832 0.8945
0.3301 12.0 3000 0.4740 0.9005
0.3163 13.0 3250 0.4768 0.89
0.2983 14.0 3500 0.4925 0.887
0.2835 15.0 3750 0.4764 0.898
0.2702 16.0 4000 0.4856 0.8905
0.2522 17.0 4250 0.4829 0.897

Framework versions

  • Transformers 4.35.2
  • Pytorch 1.14.0a0+410ce96
  • Datasets 2.15.0
  • Tokenizers 0.15.0
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Dataset used to train gokuls/hbertv1-emotion-logit_KD-tiny_ffn_2

Evaluation results