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roberta-large-sst-2-32-13-smoothed

Text Classification
Transformers
PyTorch
roberta
Generated from Trainer
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metadata
license: mit
base_model: roberta-large
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: roberta-large-sst-2-32-13-smoothed
    results: []

roberta-large-sst-2-32-13-smoothed

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

  • Loss: 0.5917
  • Accuracy: 0.8906

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 2 0.7430 0.5
No log 2.0 4 0.7414 0.5
No log 3.0 6 0.7386 0.5
No log 4.0 8 0.7348 0.5
0.7439 5.0 10 0.7302 0.5
0.7439 6.0 12 0.7248 0.5
0.7439 7.0 14 0.7195 0.5
0.7439 8.0 16 0.7143 0.5
0.7439 9.0 18 0.7082 0.5
0.7171 10.0 20 0.7022 0.5
0.7171 11.0 22 0.6977 0.5
0.7171 12.0 24 0.6954 0.5312
0.7171 13.0 26 0.6936 0.5156
0.7171 14.0 28 0.6926 0.5156
0.7024 15.0 30 0.6922 0.5312
0.7024 16.0 32 0.6921 0.5469
0.7024 17.0 34 0.6927 0.5312
0.7024 18.0 36 0.6938 0.5312
0.7024 19.0 38 0.6958 0.5156
0.6826 20.0 40 0.6982 0.5156
0.6826 21.0 42 0.7138 0.5
0.6826 22.0 44 0.7064 0.5312
0.6826 23.0 46 0.6992 0.5625
0.6826 24.0 48 0.6926 0.5625
0.6474 25.0 50 0.6836 0.5781
0.6474 26.0 52 0.6617 0.7344
0.6474 27.0 54 0.6450 0.7656
0.6474 28.0 56 0.6392 0.7812
0.6474 29.0 58 0.6513 0.7344
0.5878 30.0 60 0.6481 0.7812
0.5878 31.0 62 0.6583 0.7969
0.5878 32.0 64 0.6649 0.7812
0.5878 33.0 66 0.6280 0.8125
0.5878 34.0 68 0.6212 0.8594
0.5602 35.0 70 0.6214 0.8281
0.5602 36.0 72 0.6534 0.75
0.5602 37.0 74 0.6334 0.8594
0.5602 38.0 76 0.6060 0.875
0.5602 39.0 78 0.6048 0.875
0.55 40.0 80 0.6064 0.8594
0.55 41.0 82 0.6095 0.8438
0.55 42.0 84 0.6161 0.8438
0.55 43.0 86 0.6068 0.8594
0.55 44.0 88 0.5929 0.875
0.5425 45.0 90 0.5918 0.8906
0.5425 46.0 92 0.5919 0.8906
0.5425 47.0 94 0.5921 0.875
0.5425 48.0 96 0.5925 0.875
0.5425 49.0 98 0.5970 0.8906
0.5415 50.0 100 0.6128 0.8438
0.5415 51.0 102 0.6187 0.8438
0.5415 52.0 104 0.6012 0.8906
0.5415 53.0 106 0.5981 0.8906
0.5415 54.0 108 0.6085 0.8125
0.5434 55.0 110 0.6028 0.8438
0.5434 56.0 112 0.5970 0.8594
0.5434 57.0 114 0.6013 0.8906
0.5434 58.0 116 0.6023 0.8906
0.5434 59.0 118 0.6002 0.8906
0.5397 60.0 120 0.5964 0.8906
0.5397 61.0 122 0.5940 0.8906
0.5397 62.0 124 0.5934 0.8906
0.5397 63.0 126 0.5936 0.8906
0.5397 64.0 128 0.5936 0.8906
0.5403 65.0 130 0.5939 0.8906
0.5403 66.0 132 0.5939 0.8906
0.5403 67.0 134 0.5933 0.8906
0.5403 68.0 136 0.5933 0.8906
0.5403 69.0 138 0.5934 0.8906
0.5394 70.0 140 0.5931 0.8906
0.5394 71.0 142 0.5926 0.8906
0.5394 72.0 144 0.5921 0.8906
0.5394 73.0 146 0.5919 0.8906
0.5394 74.0 148 0.5918 0.8906
0.5394 75.0 150 0.5917 0.8906

Framework versions

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