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bert-base-uncased-sst-2-16-13-smoothed

Text Classification
Transformers
PyTorch
bert
Generated from Trainer
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metadata
license: apache-2.0
base_model: bert-base-uncased
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: bert-base-uncased-sst-2-16-13-smoothed
    results: []

bert-base-uncased-sst-2-16-13-smoothed

This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6686
  • Accuracy: 0.6875

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: 32
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 50
  • num_epochs: 75
  • label_smoothing_factor: 0.45

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 1.0 1 0.7007 0.5
No log 2.0 2 0.7007 0.5
No log 3.0 3 0.7005 0.5
No log 4.0 4 0.7004 0.5
No log 5.0 5 0.7001 0.5
No log 6.0 6 0.6998 0.5
No log 7.0 7 0.6994 0.5
No log 8.0 8 0.6990 0.5
No log 9.0 9 0.6986 0.5
0.6996 10.0 10 0.6982 0.5
0.6996 11.0 11 0.6977 0.5
0.6996 12.0 12 0.6972 0.5
0.6996 13.0 13 0.6967 0.5312
0.6996 14.0 14 0.6962 0.5312
0.6996 15.0 15 0.6956 0.5
0.6996 16.0 16 0.6950 0.5312
0.6996 17.0 17 0.6944 0.5625
0.6996 18.0 18 0.6939 0.625
0.6996 19.0 19 0.6935 0.5938
0.6877 20.0 20 0.6930 0.5625
0.6877 21.0 21 0.6926 0.5625
0.6877 22.0 22 0.6920 0.5938
0.6877 23.0 23 0.6915 0.5938
0.6877 24.0 24 0.6909 0.5938
0.6877 25.0 25 0.6903 0.5938
0.6877 26.0 26 0.6897 0.5625
0.6877 27.0 27 0.6892 0.5625
0.6877 28.0 28 0.6888 0.5625
0.6877 29.0 29 0.6880 0.5625
0.6693 30.0 30 0.6873 0.5625
0.6693 31.0 31 0.6872 0.5625
0.6693 32.0 32 0.6869 0.5938
0.6693 33.0 33 0.6863 0.5625
0.6693 34.0 34 0.6857 0.5625
0.6693 35.0 35 0.6851 0.5625
0.6693 36.0 36 0.6844 0.5938
0.6693 37.0 37 0.6836 0.5938
0.6693 38.0 38 0.6825 0.5938
0.6693 39.0 39 0.6818 0.5625
0.6173 40.0 40 0.6815 0.5625
0.6173 41.0 41 0.6816 0.5625
0.6173 42.0 42 0.6812 0.5938
0.6173 43.0 43 0.6804 0.625
0.6173 44.0 44 0.6794 0.6562
0.6173 45.0 45 0.6781 0.6562
0.6173 46.0 46 0.6770 0.6562
0.6173 47.0 47 0.6761 0.6562
0.6173 48.0 48 0.6757 0.6562
0.6173 49.0 49 0.6746 0.6562
0.5541 50.0 50 0.6736 0.6562
0.5541 51.0 51 0.6730 0.6562
0.5541 52.0 52 0.6725 0.6562
0.5541 53.0 53 0.6703 0.6562
0.5541 54.0 54 0.6682 0.6562
0.5541 55.0 55 0.6671 0.6562
0.5541 56.0 56 0.6660 0.6562
0.5541 57.0 57 0.6652 0.6562
0.5541 58.0 58 0.6651 0.6562
0.5541 59.0 59 0.6652 0.6562
0.5403 60.0 60 0.6654 0.6875
0.5403 61.0 61 0.6660 0.6562
0.5403 62.0 62 0.6664 0.6562
0.5403 63.0 63 0.6666 0.6562
0.5403 64.0 64 0.6675 0.6562
0.5403 65.0 65 0.6684 0.6562
0.5403 66.0 66 0.6692 0.6562
0.5403 67.0 67 0.6699 0.6562
0.5403 68.0 68 0.6703 0.6562
0.5403 69.0 69 0.6705 0.6562
0.538 70.0 70 0.6706 0.6562
0.538 71.0 71 0.6703 0.6562
0.538 72.0 72 0.6696 0.6562
0.538 73.0 73 0.6691 0.6562
0.538 74.0 74 0.6688 0.6562
0.538 75.0 75 0.6686 0.6875

Framework versions

  • Transformers 4.32.0.dev0
  • Pytorch 2.0.1+cu118
  • Datasets 2.4.0
  • Tokenizers 0.13.3