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roberta-large-sst-2-16-13

Text Classification
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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-2-16-13
    results: []

roberta-large-sst-2-16-13

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

  • Loss: 0.3222
  • Accuracy: 0.8438

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: 500
  • num_epochs: 150

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 1.0 1 0.7045 0.5
No log 2.0 2 0.7045 0.5
No log 3.0 3 0.7045 0.5
No log 4.0 4 0.7045 0.5
No log 5.0 5 0.7045 0.5
No log 6.0 6 0.7045 0.5
No log 7.0 7 0.7044 0.5
No log 8.0 8 0.7044 0.5
No log 9.0 9 0.7044 0.5
0.7125 10.0 10 0.7043 0.5
0.7125 11.0 11 0.7043 0.5
0.7125 12.0 12 0.7042 0.5
0.7125 13.0 13 0.7042 0.5
0.7125 14.0 14 0.7041 0.5
0.7125 15.0 15 0.7041 0.5
0.7125 16.0 16 0.7040 0.5
0.7125 17.0 17 0.7040 0.5
0.7125 18.0 18 0.7039 0.5
0.7125 19.0 19 0.7039 0.5
0.6935 20.0 20 0.7038 0.5
0.6935 21.0 21 0.7038 0.5
0.6935 22.0 22 0.7037 0.5
0.6935 23.0 23 0.7037 0.5
0.6935 24.0 24 0.7037 0.5
0.6935 25.0 25 0.7036 0.5
0.6935 26.0 26 0.7036 0.5
0.6935 27.0 27 0.7035 0.5
0.6935 28.0 28 0.7035 0.5
0.6935 29.0 29 0.7034 0.5
0.7031 30.0 30 0.7033 0.5
0.7031 31.0 31 0.7032 0.5
0.7031 32.0 32 0.7031 0.5
0.7031 33.0 33 0.7030 0.5
0.7031 34.0 34 0.7029 0.5
0.7031 35.0 35 0.7027 0.5
0.7031 36.0 36 0.7027 0.5
0.7031 37.0 37 0.7026 0.5
0.7031 38.0 38 0.7025 0.5
0.7031 39.0 39 0.7024 0.5
0.7021 40.0 40 0.7023 0.5
0.7021 41.0 41 0.7022 0.5
0.7021 42.0 42 0.7021 0.5
0.7021 43.0 43 0.7019 0.5
0.7021 44.0 44 0.7017 0.5
0.7021 45.0 45 0.7016 0.5
0.7021 46.0 46 0.7014 0.5
0.7021 47.0 47 0.7012 0.5
0.7021 48.0 48 0.7010 0.5
0.7021 49.0 49 0.7007 0.5
0.7009 50.0 50 0.7005 0.5
0.7009 51.0 51 0.7003 0.5
0.7009 52.0 52 0.7001 0.5
0.7009 53.0 53 0.6998 0.5
0.7009 54.0 54 0.6996 0.5
0.7009 55.0 55 0.6994 0.5
0.7009 56.0 56 0.6993 0.5
0.7009 57.0 57 0.6992 0.5
0.7009 58.0 58 0.6990 0.5
0.7009 59.0 59 0.6988 0.5
0.6866 60.0 60 0.6986 0.5
0.6866 61.0 61 0.6984 0.5
0.6866 62.0 62 0.6983 0.5
0.6866 63.0 63 0.6981 0.5
0.6866 64.0 64 0.6979 0.5
0.6866 65.0 65 0.6977 0.5
0.6866 66.0 66 0.6976 0.4688
0.6866 67.0 67 0.6974 0.4688
0.6866 68.0 68 0.6972 0.4688
0.6866 69.0 69 0.6970 0.4688
0.6818 70.0 70 0.6968 0.4688
0.6818 71.0 71 0.6966 0.4688
0.6818 72.0 72 0.6964 0.4688
0.6818 73.0 73 0.6961 0.4688
0.6818 74.0 74 0.6960 0.4688
0.6818 75.0 75 0.6959 0.4688
0.6818 76.0 76 0.6957 0.4688
0.6818 77.0 77 0.6955 0.4688
0.6818 78.0 78 0.6953 0.4688
0.6818 79.0 79 0.6948 0.4688
0.6639 80.0 80 0.6940 0.4688
0.6639 81.0 81 0.6932 0.4688
0.6639 82.0 82 0.6925 0.4688
0.6639 83.0 83 0.6916 0.4688
0.6639 84.0 84 0.6908 0.5
0.6639 85.0 85 0.6899 0.5
0.6639 86.0 86 0.6889 0.5
0.6639 87.0 87 0.6878 0.5
0.6639 88.0 88 0.6869 0.5
0.6639 89.0 89 0.6859 0.4688
0.6652 90.0 90 0.6850 0.4688
0.6652 91.0 91 0.6842 0.4688
0.6652 92.0 92 0.6836 0.5312
0.6652 93.0 93 0.6829 0.5312
0.6652 94.0 94 0.6818 0.5625
0.6652 95.0 95 0.6806 0.5938
0.6652 96.0 96 0.6792 0.5938
0.6652 97.0 97 0.6783 0.5938
0.6652 98.0 98 0.6771 0.5938
0.6652 99.0 99 0.6758 0.5938
0.621 100.0 100 0.6743 0.5938
0.621 101.0 101 0.6725 0.5938
0.621 102.0 102 0.6711 0.5938
0.621 103.0 103 0.6708 0.5938
0.621 104.0 104 0.6713 0.625
0.621 105.0 105 0.6693 0.5938
0.621 106.0 106 0.6605 0.5938
0.621 107.0 107 0.6499 0.5938
0.621 108.0 108 0.6439 0.5625
0.621 109.0 109 0.6434 0.625
0.5331 110.0 110 0.6439 0.5938
0.5331 111.0 111 0.6418 0.5625
0.5331 112.0 112 0.6388 0.5625
0.5331 113.0 113 0.6346 0.5625
0.5331 114.0 114 0.6307 0.5625
0.5331 115.0 115 0.6275 0.5625
0.5331 116.0 116 0.6230 0.5625
0.5331 117.0 117 0.6144 0.5625
0.5331 118.0 118 0.6092 0.5625
0.5331 119.0 119 0.6042 0.5938
0.4594 120.0 120 0.6006 0.5938
0.4594 121.0 121 0.5971 0.5938
0.4594 122.0 122 0.5906 0.5938
0.4594 123.0 123 0.5841 0.5938
0.4594 124.0 124 0.5759 0.6562
0.4594 125.0 125 0.5682 0.6875
0.4594 126.0 126 0.5566 0.6875
0.4594 127.0 127 0.5431 0.6875
0.4594 128.0 128 0.5314 0.6875
0.4594 129.0 129 0.5221 0.7188
0.33 130.0 130 0.5145 0.7188
0.33 131.0 131 0.5062 0.7188
0.33 132.0 132 0.4988 0.7188
0.33 133.0 133 0.4888 0.7188
0.33 134.0 134 0.4689 0.7188
0.33 135.0 135 0.4586 0.75
0.33 136.0 136 0.4464 0.7812
0.33 137.0 137 0.4330 0.7812
0.33 138.0 138 0.4185 0.7812
0.33 139.0 139 0.4004 0.8125
0.2099 140.0 140 0.3852 0.8125
0.2099 141.0 141 0.3724 0.8125
0.2099 142.0 142 0.3610 0.8125
0.2099 143.0 143 0.3613 0.8125
0.2099 144.0 144 0.3731 0.7812
0.2099 145.0 145 0.3655 0.8125
0.2099 146.0 146 0.3553 0.8125
0.2099 147.0 147 0.3457 0.8125
0.2099 148.0 148 0.3380 0.8438
0.2099 149.0 149 0.3315 0.8438
0.0894 150.0 150 0.3222 0.8438

Framework versions

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