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xlnet-base-cased_crows_pairs_finetuned

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PyTorch
xlnet
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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: 0.5
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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
@@ -31,12 +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: 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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@@ -67,53 +67,53 @@ The following hyperparameters were used during training:
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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
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.5298013245033113
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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
 
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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: 2.7148
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+ - Accuracy: 0.5298
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+ - Tp: 0.2550
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+ - Tn: 0.2748
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+ - Fp: 0.1987
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+ - Fn: 0.2715
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Tp | Tn | Fp | Fn |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:------:|:------:|
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+ | 0.716 | 1.05 | 20 | 0.6921 | 0.5265 | 0.5265 | 0.0 | 0.4735 | 0.0 |
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+ | 0.7095 | 2.11 | 40 | 0.7040 | 0.4735 | 0.0 | 0.4735 | 0.0 | 0.5265 |
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+ | 0.7124 | 3.16 | 60 | 0.7153 | 0.4735 | 0.0 | 0.4735 | 0.0 | 0.5265 |
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+ | 0.7226 | 4.21 | 80 | 0.7080 | 0.4735 | 0.0 | 0.4735 | 0.0 | 0.5265 |
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+ | 0.7018 | 5.26 | 100 | 0.7256 | 0.4735 | 0.0563 | 0.4172 | 0.0563 | 0.4702 |
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+ | 0.6419 | 6.32 | 120 | 0.8416 | 0.5298 | 0.3212 | 0.2086 | 0.2649 | 0.2053 |
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+ | 0.4401 | 7.37 | 140 | 1.1483 | 0.5497 | 0.2781 | 0.2715 | 0.2020 | 0.2483 |
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+ | 0.2331 | 8.42 | 160 | 1.4366 | 0.5199 | 0.2649 | 0.2550 | 0.2185 | 0.2616 |
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+ | 0.1172 | 9.47 | 180 | 2.4989 | 0.5232 | 0.2616 | 0.2616 | 0.2119 | 0.2649 |
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+ | 0.1918 | 10.53 | 200 | 1.9571 | 0.5629 | 0.2616 | 0.3013 | 0.1722 | 0.2649 |
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+ | 0.0907 | 11.58 | 220 | 2.0011 | 0.5298 | 0.2384 | 0.2914 | 0.1821 | 0.2881 |
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+ | 0.1393 | 12.63 | 240 | 1.8743 | 0.5364 | 0.2815 | 0.2550 | 0.2185 | 0.2450 |
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+ | 0.0994 | 13.68 | 260 | 2.0843 | 0.5166 | 0.2285 | 0.2881 | 0.1854 | 0.2980 |
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+ | 0.0916 | 14.74 | 280 | 1.8777 | 0.5232 | 0.2318 | 0.2914 | 0.1821 | 0.2947 |
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+ | 0.2059 | 15.79 | 300 | 2.5899 | 0.5199 | 0.1689 | 0.3510 | 0.1225 | 0.3576 |
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+ | 0.0534 | 16.84 | 320 | 2.2538 | 0.5364 | 0.2715 | 0.2649 | 0.2086 | 0.2550 |
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+ | 0.056 | 17.89 | 340 | 2.2485 | 0.5298 | 0.2748 | 0.2550 | 0.2185 | 0.2517 |
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+ | 0.0707 | 18.95 | 360 | 1.9060 | 0.5430 | 0.2815 | 0.2616 | 0.2119 | 0.2450 |
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+ | 0.1208 | 20.0 | 380 | 2.4532 | 0.5364 | 0.2781 | 0.2583 | 0.2152 | 0.2483 |
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+ | 0.0831 | 21.05 | 400 | 2.0115 | 0.5397 | 0.2417 | 0.2980 | 0.1755 | 0.2848 |
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+ | 0.0746 | 22.11 | 420 | 2.2016 | 0.5331 | 0.3245 | 0.2086 | 0.2649 | 0.2020 |
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+ | 0.0485 | 23.16 | 440 | 2.1963 | 0.5464 | 0.2781 | 0.2682 | 0.2053 | 0.2483 |
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+ | 0.0254 | 24.21 | 460 | 2.2650 | 0.5265 | 0.2616 | 0.2649 | 0.2086 | 0.2649 |
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+ | 0.0604 | 25.26 | 480 | 2.1988 | 0.5298 | 0.2318 | 0.2980 | 0.1755 | 0.2947 |
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+ | 0.0513 | 26.32 | 500 | 2.2894 | 0.5298 | 0.2881 | 0.2417 | 0.2318 | 0.2384 |
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+ | 0.035 | 27.37 | 520 | 2.2012 | 0.5364 | 0.2219 | 0.3146 | 0.1589 | 0.3046 |
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+ | 0.0632 | 28.42 | 540 | 2.2575 | 0.5397 | 0.2583 | 0.2815 | 0.1921 | 0.2682 |
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+ | 0.0391 | 29.47 | 560 | 2.2376 | 0.5497 | 0.2483 | 0.3013 | 0.1722 | 0.2781 |
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+ | 0.0281 | 30.53 | 580 | 2.3408 | 0.5364 | 0.2682 | 0.2682 | 0.2053 | 0.2583 |
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+ | 0.0286 | 31.58 | 600 | 2.4082 | 0.5397 | 0.2715 | 0.2682 | 0.2053 | 0.2550 |
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+ | 0.0411 | 32.63 | 620 | 2.4859 | 0.5331 | 0.2351 | 0.2980 | 0.1755 | 0.2914 |
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+ | 0.0308 | 33.68 | 640 | 2.5221 | 0.5430 | 0.2947 | 0.2483 | 0.2252 | 0.2318 |
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+ | 0.0419 | 34.74 | 660 | 2.4549 | 0.5166 | 0.2517 | 0.2649 | 0.2086 | 0.2748 |
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+ | 0.0442 | 35.79 | 680 | 2.3828 | 0.5397 | 0.2914 | 0.2483 | 0.2252 | 0.2351 |
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+ | 0.0346 | 36.84 | 700 | 2.4542 | 0.5497 | 0.3179 | 0.2318 | 0.2417 | 0.2086 |
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+ | 0.0277 | 37.89 | 720 | 2.5188 | 0.5265 | 0.2848 | 0.2417 | 0.2318 | 0.2417 |
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+ | 0.0299 | 38.95 | 740 | 2.4768 | 0.5331 | 0.2815 | 0.2517 | 0.2219 | 0.2450 |
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+ | 0.0381 | 40.0 | 760 | 2.4496 | 0.5331 | 0.3013 | 0.2318 | 0.2417 | 0.2252 |
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+ | 0.0317 | 41.05 | 780 | 2.4512 | 0.5265 | 0.2748 | 0.2517 | 0.2219 | 0.2517 |
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+ | 0.0377 | 42.11 | 800 | 2.5661 | 0.5199 | 0.3046 | 0.2152 | 0.2583 | 0.2219 |
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+ | 0.0526 | 43.16 | 820 | 2.6317 | 0.5132 | 0.2881 | 0.2252 | 0.2483 | 0.2384 |
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+ | 0.0321 | 44.21 | 840 | 2.6637 | 0.5132 | 0.2616 | 0.2517 | 0.2219 | 0.2649 |
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+ | 0.0181 | 45.26 | 860 | 2.6816 | 0.5331 | 0.2583 | 0.2748 | 0.1987 | 0.2682 |
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+ | 0.0322 | 46.32 | 880 | 2.6758 | 0.5364 | 0.2517 | 0.2848 | 0.1887 | 0.2748 |
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+ | 0.013 | 47.37 | 900 | 2.6944 | 0.5298 | 0.2517 | 0.2781 | 0.1954 | 0.2748 |
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+ | 0.033 | 48.42 | 920 | 2.7166 | 0.5265 | 0.2550 | 0.2715 | 0.2020 | 0.2715 |
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+ | 0.0229 | 49.47 | 940 | 2.7148 | 0.5298 | 0.2550 | 0.2748 | 0.1987 | 0.2715 |
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  ### Framework versions