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xlnet-base-cased_crows_pairs_finetuned

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PyTorch
xlnet
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Eval Results (legacy)
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@@ -21,7 +21,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.7119205298013245
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -31,8 +31,12 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the crows_pairs dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 2.5652
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- - Accuracy: 0.7119
 
 
 
 
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  ## Model description
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@@ -51,75 +55,65 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 5e-05
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  - train_batch_size: 64
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  - eval_batch_size: 64
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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- - num_epochs: 30
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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- |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | 0.728 | 0.53 | 10 | 0.6939 | 0.4901 |
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- | 0.6914 | 1.05 | 20 | 0.6939 | 0.4901 |
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- | 0.705 | 1.58 | 30 | 0.6925 | 0.5066 |
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- | 0.6993 | 2.11 | 40 | 0.6949 | 0.5066 |
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- | 0.6979 | 2.63 | 50 | 0.6996 | 0.5066 |
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- | 0.7152 | 3.16 | 60 | 0.6940 | 0.4901 |
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- | 0.7158 | 3.68 | 70 | 0.7007 | 0.4934 |
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- | 0.6968 | 4.21 | 80 | 0.6999 | 0.5066 |
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- | 0.7164 | 4.74 | 90 | 0.6977 | 0.4934 |
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- | 0.6698 | 5.26 | 100 | 0.7079 | 0.4536 |
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- | 0.611 | 5.79 | 110 | 0.8882 | 0.5099 |
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- | 0.6487 | 6.32 | 120 | 0.8360 | 0.5066 |
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- | 0.5223 | 6.84 | 130 | 0.8047 | 0.5728 |
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- | 0.2879 | 7.37 | 140 | 1.1483 | 0.5795 |
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- | 0.2369 | 7.89 | 150 | 1.1773 | 0.5993 |
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- | 0.2542 | 8.42 | 160 | 0.9170 | 0.6424 |
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- | 0.1743 | 8.95 | 170 | 1.3674 | 0.6424 |
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- | 0.1307 | 9.47 | 180 | 1.0740 | 0.7152 |
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- | 0.0718 | 10.0 | 190 | 1.4397 | 0.6424 |
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- | 0.0278 | 10.53 | 200 | 1.9821 | 0.6523 |
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- | 0.0519 | 11.05 | 210 | 1.6970 | 0.6755 |
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- | 0.0269 | 11.58 | 220 | 1.8299 | 0.6656 |
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- | 0.0556 | 12.11 | 230 | 1.9459 | 0.7086 |
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- | 0.0455 | 12.63 | 240 | 1.6443 | 0.6854 |
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- | 0.0665 | 13.16 | 250 | 1.9887 | 0.6821 |
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- | 0.009 | 13.68 | 260 | 2.0236 | 0.6788 |
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- | 0.0146 | 14.21 | 270 | 1.8515 | 0.7152 |
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- | 0.0034 | 14.74 | 280 | 1.9315 | 0.7252 |
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- | 0.0248 | 15.26 | 290 | 2.0754 | 0.7119 |
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- | 0.0536 | 15.79 | 300 | 2.0371 | 0.7053 |
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- | 0.0393 | 16.32 | 310 | 1.9381 | 0.6987 |
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- | 0.0255 | 16.84 | 320 | 1.9074 | 0.6788 |
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- | 0.0116 | 17.37 | 330 | 2.2182 | 0.6623 |
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- | 0.0128 | 17.89 | 340 | 2.3002 | 0.6689 |
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- | 0.0006 | 18.42 | 350 | 2.2353 | 0.6788 |
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- | 0.0053 | 18.95 | 360 | 2.4277 | 0.6755 |
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- | 0.0013 | 19.47 | 370 | 2.5156 | 0.6490 |
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- | 0.0004 | 20.0 | 380 | 2.5091 | 0.6689 |
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- | 0.0003 | 20.53 | 390 | 2.4096 | 0.6854 |
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- | 0.0017 | 21.05 | 400 | 2.3497 | 0.6921 |
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- | 0.0001 | 21.58 | 410 | 2.3376 | 0.6854 |
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- | 0.012 | 22.11 | 420 | 2.3832 | 0.6854 |
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- | 0.0002 | 22.63 | 430 | 2.4388 | 0.7053 |
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- | 0.0001 | 23.16 | 440 | 2.4821 | 0.7152 |
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- | 0.0001 | 23.68 | 450 | 2.5027 | 0.7119 |
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- | 0.0001 | 24.21 | 460 | 2.5105 | 0.7152 |
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- | 0.0001 | 24.74 | 470 | 2.5145 | 0.7152 |
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- | 0.0002 | 25.26 | 480 | 2.5143 | 0.6954 |
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- | 0.0001 | 25.79 | 490 | 2.5629 | 0.6821 |
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- | 0.0002 | 26.32 | 500 | 2.5414 | 0.6887 |
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- | 0.0001 | 26.84 | 510 | 2.5301 | 0.7119 |
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- | 0.0012 | 27.37 | 520 | 2.5360 | 0.7020 |
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- | 0.0 | 27.89 | 530 | 2.5428 | 0.6921 |
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- | 0.0117 | 28.42 | 540 | 2.5455 | 0.6954 |
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- | 0.0001 | 28.95 | 550 | 2.5598 | 0.7086 |
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- | 0.0001 | 29.47 | 560 | 2.5648 | 0.7119 |
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- | 0.0001 | 30.0 | 570 | 2.5652 | 0.7119 |
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  ### Framework versions
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.5
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  ---
26
 
27
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
31
 
32
  This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the crows_pairs dataset.
33
  It achieves the following results on the evaluation set:
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+ - Loss: 0.6933
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+ - Accuracy: 0.5
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+ - Tp: 0.5
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+ - Tn: 0.0
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+ - Fp: 0.5
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+ - Fn: 0.0
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  ## Model description
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55
  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 0.0001
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  - train_batch_size: 64
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  - eval_batch_size: 64
61
  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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+ - num_epochs: 50
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66
  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | Tp | Tn | Fp | Fn |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:------:|:------:|
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+ | 0.7406 | 1.05 | 20 | 0.6941 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
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+ | 0.7008 | 2.11 | 40 | 0.6959 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
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+ | 0.7067 | 3.16 | 60 | 0.6932 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
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+ | 0.7029 | 4.21 | 80 | 0.6937 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 |
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+ | 0.7103 | 5.26 | 100 | 0.6932 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 |
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+ | 0.7085 | 6.32 | 120 | 0.7004 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
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+ | 0.7061 | 7.37 | 140 | 0.6933 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
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+ | 0.7013 | 8.42 | 160 | 0.6954 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 |
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+ | 0.6952 | 9.47 | 180 | 0.6933 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 |
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+ | 0.7084 | 10.53 | 200 | 0.7079 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 |
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+ | 0.71 | 11.58 | 220 | 0.6999 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
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+ | 0.7036 | 12.63 | 240 | 0.6932 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
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+ | 0.7043 | 13.68 | 260 | 0.6942 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
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+ | 0.7058 | 14.74 | 280 | 0.6947 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
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+ | 0.6993 | 15.79 | 300 | 0.6951 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
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+ | 0.7009 | 16.84 | 320 | 0.6936 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 |
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+ | 0.7069 | 17.89 | 340 | 0.7002 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 |
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+ | 0.7068 | 18.95 | 360 | 0.6970 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
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+ | 0.7042 | 20.0 | 380 | 0.6935 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
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+ | 0.6999 | 21.05 | 400 | 0.6957 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
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+ | 0.6966 | 22.11 | 420 | 0.6936 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
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+ | 0.6975 | 23.16 | 440 | 0.6934 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
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+ | 0.7043 | 24.21 | 460 | 0.6934 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 |
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+ | 0.7002 | 25.26 | 480 | 0.6932 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 |
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+ | 0.7039 | 26.32 | 500 | 0.7004 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
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+ | 0.6927 | 27.37 | 520 | 0.6932 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
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+ | 0.7078 | 28.42 | 540 | 0.6941 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 |
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+ | 0.6999 | 29.47 | 560 | 0.6969 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 |
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+ | 0.7063 | 30.53 | 580 | 0.6936 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 |
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+ | 0.7011 | 31.58 | 600 | 0.6934 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 |
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+ | 0.7061 | 32.63 | 620 | 0.6958 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 |
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+ | 0.6971 | 33.68 | 640 | 0.6932 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
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+ | 0.7007 | 34.74 | 660 | 0.6932 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
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+ | 0.7014 | 35.79 | 680 | 0.6954 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 |
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+ | 0.6976 | 36.84 | 700 | 0.6951 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
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+ | 0.6957 | 37.89 | 720 | 0.6936 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 |
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+ | 0.7009 | 38.95 | 740 | 0.6950 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 |
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+ | 0.6941 | 40.0 | 760 | 0.6933 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
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+ | 0.6989 | 41.05 | 780 | 0.6948 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 |
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+ | 0.6935 | 42.11 | 800 | 0.6974 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
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+ | 0.6939 | 43.16 | 820 | 0.6956 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 |
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+ | 0.6975 | 44.21 | 840 | 0.6955 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
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+ | 0.669 | 45.26 | 860 | 0.7089 | 0.5132 | 0.1623 | 0.3510 | 0.1490 | 0.3377 |
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+ | 0.6896 | 46.32 | 880 | 0.7088 | 0.4669 | 0.4106 | 0.0563 | 0.4437 | 0.0894 |
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+ | 0.6942 | 47.37 | 900 | 0.6944 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
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+ | 0.6942 | 48.42 | 920 | 0.6933 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
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+ | 0.6921 | 49.47 | 940 | 0.6933 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions