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bert-base-cased-qnli

Text Classification
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PyTorch
English
bert
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metadata
language:
  - en
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - glue
metrics:
  - accuracy
model-index:
  - name: bert-base-cased-qnli
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: GLUE QNLI
          type: glue
          args: qnli
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9086582463847702

bert-base-cased-qnli

This model is a fine-tuned version of bert-base-cased on the GLUE QNLI dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2569
  • Accuracy: 0.9087

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: 2e-05
  • train_batch_size: 16
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.06
  • num_epochs: 10.0

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.6529 0.08 500 0.4945 0.7734
0.4731 0.15 1000 0.3888 0.8406
0.4113 0.23 1500 0.3605 0.8523
0.4006 0.31 2000 0.3175 0.8649
0.3615 0.38 2500 0.3220 0.8678
0.3662 0.46 3000 0.3198 0.8667
0.3578 0.53 3500 0.2970 0.8779
0.3349 0.61 4000 0.2864 0.8834
0.3548 0.69 4500 0.2664 0.8946
0.3254 0.76 5000 0.2651 0.8929
0.3212 0.84 5500 0.2943 0.8817
0.3195 0.92 6000 0.2630 0.8960
0.3044 0.99 6500 0.2766 0.8882
0.245 1.07 7000 0.3268 0.9013
0.2415 1.15 7500 0.2987 0.8991
0.248 1.22 8000 0.3373 0.8812
0.2328 1.3 8500 0.3474 0.8808
0.2427 1.37 9000 0.2986 0.8913
0.243 1.45 9500 0.2835 0.8982
0.2375 1.53 10000 0.2893 0.8909
0.2253 1.6 10500 0.2942 0.8960
0.2429 1.68 11000 0.2852 0.9028
0.238 1.76 11500 0.2672 0.9037
0.2344 1.83 12000 0.2672 0.9021
0.2368 1.91 12500 0.2754 0.9019
0.2424 1.99 13000 0.2569 0.9087
0.1542 2.06 13500 0.3807 0.9026
0.1416 2.14 14000 0.3915 0.8980
0.1445 2.21 14500 0.4292 0.8984
0.1631 2.29 15000 0.4097 0.8971
0.1512 2.37 15500 0.3880 0.9012
0.1624 2.44 16000 0.4083 0.8955
0.1616 2.52 16500 0.3950 0.9039
0.1587 2.6 17000 0.3579 0.9103
0.1615 2.67 17500 0.3931 0.9012
0.1623 2.75 18000 0.3697 0.9059
0.1687 2.83 18500 0.3473 0.9037
0.1627 2.9 19000 0.3851 0.8982
0.1593 2.98 19500 0.4039 0.9019
0.1136 3.05 20000 0.4835 0.9024
0.0965 3.13 20500 0.5061 0.9012
0.0931 3.21 21000 0.5279 0.8991
0.0993 3.28 21500 0.4856 0.9019
0.1187 3.36 22000 0.4883 0.9032
0.1008 3.44 22500 0.5054 0.9015
0.1013 3.51 23000 0.5025 0.9023
0.1092 3.59 23500 0.4485 0.8986
0.1135 3.67 24000 0.5123 0.8977
0.1042 3.74 24500 0.4884 0.9010
0.1178 3.82 25000 0.4130 0.9006
0.1041 3.89 25500 0.4847 0.8951
0.1025 3.97 26000 0.4706 0.8986
0.0833 4.05 26500 0.5018 0.9054
0.0632 4.12 27000 0.5426 0.9055
0.0613 4.2 27500 0.5629 0.9021
0.0703 4.28 28000 0.5763 0.9033
0.0687 4.35 28500 0.5274 0.9039
0.0707 4.43 29000 0.5682 0.9019
0.0717 4.51 29500 0.5382 0.9004
0.0692 4.58 30000 0.5901 0.9010
0.0701 4.66 30500 0.5817 0.8990
0.0708 4.73 31000 0.5580 0.9019
0.07 4.81 31500 0.5640 0.8990
0.0725 4.89 32000 0.5768 0.8993
0.0701 4.96 32500 0.5289 0.9035
0.0441 5.04 33000 0.6401 0.9010
0.0388 5.12 33500 0.6446 0.8991
0.0417 5.19 34000 0.6327 0.9039
0.039 5.27 34500 0.6385 0.9048
0.0407 5.35 35000 0.6510 0.9030
0.0446 5.42 35500 0.5788 0.9030
0.0422 5.5 36000 0.6723 0.9024
0.04 5.58 36500 0.6602 0.9037
0.0514 5.65 37000 0.6407 0.9024
0.0462 5.73 37500 0.6145 0.9048
0.0479 5.8 38000 0.5881 0.9008
0.0503 5.88 38500 0.6001 0.9008
0.0385 5.96 39000 0.6464 0.9052
0.0436 6.03 39500 0.6683 0.9039
0.0296 6.11 40000 0.6965 0.9037
0.0275 6.19 40500 0.7193 0.9039
0.0258 6.26 41000 0.7229 0.9039
0.0223 6.34 41500 0.6974 0.9074
0.0268 6.42 42000 0.7045 0.9028
0.0297 6.49 42500 0.7289 0.9055
0.0295 6.57 43000 0.6810 0.9050
0.0265 6.64 43500 0.6833 0.9065
0.0268 6.72 44000 0.7155 0.9035
0.0293 6.8 44500 0.7632 0.9015
0.0289 6.87 45000 0.7229 0.9033
0.0286 6.95 45500 0.6671 0.9054
0.024 7.03 46000 0.7659 0.9033
0.0141 7.1 46500 0.7981 0.9023
0.0212 7.18 47000 0.7021 0.9088
0.0183 7.26 47500 0.7122 0.9096
0.0221 7.33 48000 0.7080 0.9065
0.0146 7.41 48500 0.7344 0.9074
0.0181 7.48 49000 0.7273 0.9105
0.0161 7.56 49500 0.7332 0.9083
0.0193 7.64 50000 0.7117 0.9094
0.0165 7.71 50500 0.7797 0.9070
0.0173 7.79 51000 0.7128 0.9107
0.0158 7.87 51500 0.7420 0.9110
0.0157 7.94 52000 0.7198 0.9129
0.0142 8.02 52500 0.7033 0.9114
0.0107 8.1 53000 0.7761 0.9109
0.0122 8.17 53500 0.7777 0.9110
0.013 8.25 54000 0.7880 0.9076
0.0092 8.32 54500 0.8070 0.9074
0.0102 8.4 55000 0.8113 0.9055
0.0099 8.48 55500 0.8153 0.9079
0.0087 8.55 56000 0.8045 0.9112
0.0133 8.63 56500 0.8173 0.9081
0.017 8.71 57000 0.7646 0.9101
0.0093 8.78 57500 0.7681 0.9120
0.0085 8.86 58000 0.8067 0.9083
0.0137 8.94 58500 0.7645 0.9107
0.0099 9.01 59000 0.7742 0.9123
0.0074 9.09 59500 0.8052 0.9059
0.0057 9.16 60000 0.7906 0.9132
0.0046 9.24 60500 0.7915 0.9136
0.0036 9.32 61000 0.8114 0.9101
0.0036 9.39 61500 0.8382 0.9087
0.0079 9.47 62000 0.8013 0.9110
0.0079 9.55 62500 0.8116 0.9099
0.0054 9.62 63000 0.8143 0.9110
0.0036 9.7 63500 0.8138 0.9114
0.0053 9.78 64000 0.8158 0.9107
0.0035 9.85 64500 0.8146 0.9112
0.0051 9.93 65000 0.8168 0.9107

Framework versions

  • Transformers 4.21.3
  • Pytorch 1.7.1
  • Datasets 1.18.3
  • Tokenizers 0.11.6