henryscheible commited on
Commit
25b93b4
1 Parent(s): 91a7161

update model card README.md

Browse files
Files changed (1) hide show
  1. README.md +58 -64
README.md CHANGED
@@ -21,7 +21,7 @@ model-index:
21
  metrics:
22
  - name: Accuracy
23
  type: accuracy
24
- value: 0.7119205298013245
25
  ---
26
 
27
  <!-- 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. -->
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:
34
- - Loss: 2.5652
35
- - Accuracy: 0.7119
 
 
 
 
36
 
37
  ## Model description
38
 
@@ -51,75 +55,65 @@ More information needed
51
  ### Training hyperparameters
52
 
53
  The following hyperparameters were used during training:
54
- - learning_rate: 5e-05
55
  - train_batch_size: 64
56
  - eval_batch_size: 64
57
  - seed: 42
58
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
59
  - lr_scheduler_type: linear
60
- - num_epochs: 30
61
 
62
  ### Training results
63
 
64
- | Training Loss | Epoch | Step | Validation Loss | Accuracy |
65
- |:-------------:|:-----:|:----:|:---------------:|:--------:|
66
- | 0.728 | 0.53 | 10 | 0.6939 | 0.4901 |
67
- | 0.6914 | 1.05 | 20 | 0.6939 | 0.4901 |
68
- | 0.705 | 1.58 | 30 | 0.6925 | 0.5066 |
69
- | 0.6993 | 2.11 | 40 | 0.6949 | 0.5066 |
70
- | 0.6979 | 2.63 | 50 | 0.6996 | 0.5066 |
71
- | 0.7152 | 3.16 | 60 | 0.6940 | 0.4901 |
72
- | 0.7158 | 3.68 | 70 | 0.7007 | 0.4934 |
73
- | 0.6968 | 4.21 | 80 | 0.6999 | 0.5066 |
74
- | 0.7164 | 4.74 | 90 | 0.6977 | 0.4934 |
75
- | 0.6698 | 5.26 | 100 | 0.7079 | 0.4536 |
76
- | 0.611 | 5.79 | 110 | 0.8882 | 0.5099 |
77
- | 0.6487 | 6.32 | 120 | 0.8360 | 0.5066 |
78
- | 0.5223 | 6.84 | 130 | 0.8047 | 0.5728 |
79
- | 0.2879 | 7.37 | 140 | 1.1483 | 0.5795 |
80
- | 0.2369 | 7.89 | 150 | 1.1773 | 0.5993 |
81
- | 0.2542 | 8.42 | 160 | 0.9170 | 0.6424 |
82
- | 0.1743 | 8.95 | 170 | 1.3674 | 0.6424 |
83
- | 0.1307 | 9.47 | 180 | 1.0740 | 0.7152 |
84
- | 0.0718 | 10.0 | 190 | 1.4397 | 0.6424 |
85
- | 0.0278 | 10.53 | 200 | 1.9821 | 0.6523 |
86
- | 0.0519 | 11.05 | 210 | 1.6970 | 0.6755 |
87
- | 0.0269 | 11.58 | 220 | 1.8299 | 0.6656 |
88
- | 0.0556 | 12.11 | 230 | 1.9459 | 0.7086 |
89
- | 0.0455 | 12.63 | 240 | 1.6443 | 0.6854 |
90
- | 0.0665 | 13.16 | 250 | 1.9887 | 0.6821 |
91
- | 0.009 | 13.68 | 260 | 2.0236 | 0.6788 |
92
- | 0.0146 | 14.21 | 270 | 1.8515 | 0.7152 |
93
- | 0.0034 | 14.74 | 280 | 1.9315 | 0.7252 |
94
- | 0.0248 | 15.26 | 290 | 2.0754 | 0.7119 |
95
- | 0.0536 | 15.79 | 300 | 2.0371 | 0.7053 |
96
- | 0.0393 | 16.32 | 310 | 1.9381 | 0.6987 |
97
- | 0.0255 | 16.84 | 320 | 1.9074 | 0.6788 |
98
- | 0.0116 | 17.37 | 330 | 2.2182 | 0.6623 |
99
- | 0.0128 | 17.89 | 340 | 2.3002 | 0.6689 |
100
- | 0.0006 | 18.42 | 350 | 2.2353 | 0.6788 |
101
- | 0.0053 | 18.95 | 360 | 2.4277 | 0.6755 |
102
- | 0.0013 | 19.47 | 370 | 2.5156 | 0.6490 |
103
- | 0.0004 | 20.0 | 380 | 2.5091 | 0.6689 |
104
- | 0.0003 | 20.53 | 390 | 2.4096 | 0.6854 |
105
- | 0.0017 | 21.05 | 400 | 2.3497 | 0.6921 |
106
- | 0.0001 | 21.58 | 410 | 2.3376 | 0.6854 |
107
- | 0.012 | 22.11 | 420 | 2.3832 | 0.6854 |
108
- | 0.0002 | 22.63 | 430 | 2.4388 | 0.7053 |
109
- | 0.0001 | 23.16 | 440 | 2.4821 | 0.7152 |
110
- | 0.0001 | 23.68 | 450 | 2.5027 | 0.7119 |
111
- | 0.0001 | 24.21 | 460 | 2.5105 | 0.7152 |
112
- | 0.0001 | 24.74 | 470 | 2.5145 | 0.7152 |
113
- | 0.0002 | 25.26 | 480 | 2.5143 | 0.6954 |
114
- | 0.0001 | 25.79 | 490 | 2.5629 | 0.6821 |
115
- | 0.0002 | 26.32 | 500 | 2.5414 | 0.6887 |
116
- | 0.0001 | 26.84 | 510 | 2.5301 | 0.7119 |
117
- | 0.0012 | 27.37 | 520 | 2.5360 | 0.7020 |
118
- | 0.0 | 27.89 | 530 | 2.5428 | 0.6921 |
119
- | 0.0117 | 28.42 | 540 | 2.5455 | 0.6954 |
120
- | 0.0001 | 28.95 | 550 | 2.5598 | 0.7086 |
121
- | 0.0001 | 29.47 | 560 | 2.5648 | 0.7119 |
122
- | 0.0001 | 30.0 | 570 | 2.5652 | 0.7119 |
123
 
124
 
125
  ### Framework versions
 
21
  metrics:
22
  - name: Accuracy
23
  type: accuracy
24
+ value: 0.5
25
  ---
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:
34
+ - Loss: 0.6933
35
+ - Accuracy: 0.5
36
+ - Tp: 0.5
37
+ - Tn: 0.0
38
+ - Fp: 0.5
39
+ - Fn: 0.0
40
 
41
  ## Model description
42
 
 
55
  ### Training hyperparameters
56
 
57
  The following hyperparameters were used during training:
58
+ - learning_rate: 0.0001
59
  - train_batch_size: 64
60
  - eval_batch_size: 64
61
  - seed: 42
62
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
63
  - lr_scheduler_type: linear
64
+ - num_epochs: 50
65
 
66
  ### Training results
67
 
68
+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | Tp | Tn | Fp | Fn |
69
+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:------:|:------:|
70
+ | 0.7406 | 1.05 | 20 | 0.6941 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
71
+ | 0.7008 | 2.11 | 40 | 0.6959 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
72
+ | 0.7067 | 3.16 | 60 | 0.6932 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
73
+ | 0.7029 | 4.21 | 80 | 0.6937 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 |
74
+ | 0.7103 | 5.26 | 100 | 0.6932 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 |
75
+ | 0.7085 | 6.32 | 120 | 0.7004 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
76
+ | 0.7061 | 7.37 | 140 | 0.6933 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
77
+ | 0.7013 | 8.42 | 160 | 0.6954 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 |
78
+ | 0.6952 | 9.47 | 180 | 0.6933 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 |
79
+ | 0.7084 | 10.53 | 200 | 0.7079 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 |
80
+ | 0.71 | 11.58 | 220 | 0.6999 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
81
+ | 0.7036 | 12.63 | 240 | 0.6932 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
82
+ | 0.7043 | 13.68 | 260 | 0.6942 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
83
+ | 0.7058 | 14.74 | 280 | 0.6947 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
84
+ | 0.6993 | 15.79 | 300 | 0.6951 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
85
+ | 0.7009 | 16.84 | 320 | 0.6936 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 |
86
+ | 0.7069 | 17.89 | 340 | 0.7002 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 |
87
+ | 0.7068 | 18.95 | 360 | 0.6970 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
88
+ | 0.7042 | 20.0 | 380 | 0.6935 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
89
+ | 0.6999 | 21.05 | 400 | 0.6957 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
90
+ | 0.6966 | 22.11 | 420 | 0.6936 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
91
+ | 0.6975 | 23.16 | 440 | 0.6934 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
92
+ | 0.7043 | 24.21 | 460 | 0.6934 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 |
93
+ | 0.7002 | 25.26 | 480 | 0.6932 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 |
94
+ | 0.7039 | 26.32 | 500 | 0.7004 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
95
+ | 0.6927 | 27.37 | 520 | 0.6932 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
96
+ | 0.7078 | 28.42 | 540 | 0.6941 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 |
97
+ | 0.6999 | 29.47 | 560 | 0.6969 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 |
98
+ | 0.7063 | 30.53 | 580 | 0.6936 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 |
99
+ | 0.7011 | 31.58 | 600 | 0.6934 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 |
100
+ | 0.7061 | 32.63 | 620 | 0.6958 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 |
101
+ | 0.6971 | 33.68 | 640 | 0.6932 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
102
+ | 0.7007 | 34.74 | 660 | 0.6932 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
103
+ | 0.7014 | 35.79 | 680 | 0.6954 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 |
104
+ | 0.6976 | 36.84 | 700 | 0.6951 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
105
+ | 0.6957 | 37.89 | 720 | 0.6936 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 |
106
+ | 0.7009 | 38.95 | 740 | 0.6950 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 |
107
+ | 0.6941 | 40.0 | 760 | 0.6933 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
108
+ | 0.6989 | 41.05 | 780 | 0.6948 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 |
109
+ | 0.6935 | 42.11 | 800 | 0.6974 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
110
+ | 0.6939 | 43.16 | 820 | 0.6956 | 0.5 | 0.0 | 0.5 | 0.0 | 0.5 |
111
+ | 0.6975 | 44.21 | 840 | 0.6955 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
112
+ | 0.669 | 45.26 | 860 | 0.7089 | 0.5132 | 0.1623 | 0.3510 | 0.1490 | 0.3377 |
113
+ | 0.6896 | 46.32 | 880 | 0.7088 | 0.4669 | 0.4106 | 0.0563 | 0.4437 | 0.0894 |
114
+ | 0.6942 | 47.37 | 900 | 0.6944 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
115
+ | 0.6942 | 48.42 | 920 | 0.6933 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
116
+ | 0.6921 | 49.47 | 940 | 0.6933 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
 
 
 
 
 
 
 
 
 
 
117
 
118
 
119
  ### Framework versions