Hugging Face's logo

simonycl
/
bert-base-uncased-sst-2-64-13-smoothed

Text Classification
Transformers
PyTorch
bert
Generated from Trainer
Inference Endpoints
Model card Files Community
1
simonycl commited on
Commit
59f9219
·
1 Parent(s): 7e5dbad

update model card README.md

Browse files
Files changed (1) hide show
  1. README.md +82 -82
README.md CHANGED
@@ -1,24 +1,24 @@
1
  ---
2
- license: mit
3
- base_model: roberta-large
4
  tags:
5
  - generated_from_trainer
6
  metrics:
7
  - accuracy
8
  model-index:
9
- - name: roberta-large-sst-2-64-13-smoothed
10
  results: []
11
  ---
12
 
13
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
14
  should probably proofread and complete it, then remove this comment. -->
15
 
16
- # roberta-large-sst-2-64-13-smoothed
17
 
18
- This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset.
19
  It achieves the following results on the evaluation set:
20
- - Loss: 0.5836
21
- - Accuracy: 0.9141
22
 
23
  ## Model description
24
 
@@ -51,81 +51,81 @@ The following hyperparameters were used during training:
51
 
52
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
53
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
54
- | No log | 1.0 | 4 | 0.6951 | 0.5 |
55
- | No log | 2.0 | 8 | 0.6949 | 0.5 |
56
- | 0.7004 | 3.0 | 12 | 0.6942 | 0.5 |
57
- | 0.7004 | 4.0 | 16 | 0.6938 | 0.5 |
58
- | 0.6901 | 5.0 | 20 | 0.6930 | 0.5 |
59
- | 0.6901 | 6.0 | 24 | 0.6925 | 0.4844 |
60
- | 0.6901 | 7.0 | 28 | 0.6920 | 0.5156 |
61
- | 0.6926 | 8.0 | 32 | 0.6914 | 0.5078 |
62
- | 0.6926 | 9.0 | 36 | 0.6910 | 0.4844 |
63
- | 0.6903 | 10.0 | 40 | 0.6899 | 0.5078 |
64
- | 0.6903 | 11.0 | 44 | 0.6880 | 0.6875 |
65
- | 0.6903 | 12.0 | 48 | 0.6806 | 0.7344 |
66
- | 0.6703 | 13.0 | 52 | 0.6399 | 0.8047 |
67
- | 0.6703 | 14.0 | 56 | 0.6491 | 0.8125 |
68
- | 0.6218 | 15.0 | 60 | 0.6052 | 0.8594 |
69
- | 0.6218 | 16.0 | 64 | 0.6020 | 0.8906 |
70
- | 0.6218 | 17.0 | 68 | 0.5837 | 0.9219 |
71
- | 0.5648 | 18.0 | 72 | 0.5865 | 0.8984 |
72
- | 0.5648 | 19.0 | 76 | 0.5906 | 0.8828 |
73
- | 0.5525 | 20.0 | 80 | 0.5898 | 0.875 |
74
- | 0.5525 | 21.0 | 84 | 0.5887 | 0.8984 |
75
- | 0.5525 | 22.0 | 88 | 0.5844 | 0.9062 |
76
- | 0.5448 | 23.0 | 92 | 0.5804 | 0.9219 |
77
- | 0.5448 | 24.0 | 96 | 0.5907 | 0.8984 |
78
- | 0.5446 | 25.0 | 100 | 0.5816 | 0.9141 |
79
- | 0.5446 | 26.0 | 104 | 0.5828 | 0.9219 |
80
- | 0.5446 | 27.0 | 108 | 0.5817 | 0.9219 |
81
- | 0.5397 | 28.0 | 112 | 0.5850 | 0.9219 |
82
- | 0.5397 | 29.0 | 116 | 0.5832 | 0.9219 |
83
- | 0.5385 | 30.0 | 120 | 0.5857 | 0.9141 |
84
- | 0.5385 | 31.0 | 124 | 0.5905 | 0.8984 |
85
- | 0.5385 | 32.0 | 128 | 0.5914 | 0.8984 |
86
- | 0.538 | 33.0 | 132 | 0.5918 | 0.8984 |
87
- | 0.538 | 34.0 | 136 | 0.5900 | 0.8984 |
88
- | 0.5378 | 35.0 | 140 | 0.5888 | 0.8984 |
89
- | 0.5378 | 36.0 | 144 | 0.5904 | 0.8984 |
90
- | 0.5378 | 37.0 | 148 | 0.5885 | 0.8984 |
91
- | 0.5381 | 38.0 | 152 | 0.5883 | 0.9062 |
92
- | 0.5381 | 39.0 | 156 | 0.5869 | 0.9062 |
93
- | 0.5378 | 40.0 | 160 | 0.5903 | 0.9062 |
94
- | 0.5378 | 41.0 | 164 | 0.5900 | 0.8984 |
95
- | 0.5378 | 42.0 | 168 | 0.5951 | 0.8906 |
96
- | 0.5372 | 43.0 | 172 | 0.5912 | 0.9062 |
97
- | 0.5372 | 44.0 | 176 | 0.5816 | 0.9219 |
98
- | 0.5388 | 45.0 | 180 | 0.5810 | 0.9141 |
99
- | 0.5388 | 46.0 | 184 | 0.5840 | 0.9141 |
100
- | 0.5388 | 47.0 | 188 | 0.5838 | 0.9141 |
101
- | 0.5371 | 48.0 | 192 | 0.5852 | 0.9062 |
102
- | 0.5371 | 49.0 | 196 | 0.5852 | 0.9141 |
103
- | 0.5373 | 50.0 | 200 | 0.5880 | 0.9141 |
104
- | 0.5373 | 51.0 | 204 | 0.5867 | 0.9141 |
105
- | 0.5373 | 52.0 | 208 | 0.5867 | 0.9141 |
106
- | 0.5373 | 53.0 | 212 | 0.5874 | 0.9141 |
107
- | 0.5373 | 54.0 | 216 | 0.5861 | 0.9141 |
108
- | 0.5374 | 55.0 | 220 | 0.5865 | 0.9141 |
109
- | 0.5374 | 56.0 | 224 | 0.5857 | 0.9141 |
110
- | 0.5374 | 57.0 | 228 | 0.5853 | 0.9141 |
111
- | 0.5373 | 58.0 | 232 | 0.5870 | 0.9141 |
112
- | 0.5373 | 59.0 | 236 | 0.5866 | 0.9141 |
113
- | 0.5368 | 60.0 | 240 | 0.5850 | 0.9141 |
114
- | 0.5368 | 61.0 | 244 | 0.5834 | 0.9141 |
115
- | 0.5368 | 62.0 | 248 | 0.5845 | 0.9141 |
116
- | 0.5369 | 63.0 | 252 | 0.5860 | 0.9141 |
117
- | 0.5369 | 64.0 | 256 | 0.5848 | 0.9141 |
118
- | 0.5364 | 65.0 | 260 | 0.5828 | 0.9062 |
119
- | 0.5364 | 66.0 | 264 | 0.5832 | 0.9141 |
120
- | 0.5364 | 67.0 | 268 | 0.5840 | 0.9141 |
121
- | 0.5363 | 68.0 | 272 | 0.5846 | 0.9141 |
122
- | 0.5363 | 69.0 | 276 | 0.5845 | 0.9141 |
123
- | 0.5369 | 70.0 | 280 | 0.5841 | 0.9141 |
124
- | 0.5369 | 71.0 | 284 | 0.5839 | 0.9141 |
125
- | 0.5369 | 72.0 | 288 | 0.5839 | 0.9141 |
126
- | 0.5365 | 73.0 | 292 | 0.5837 | 0.9141 |
127
- | 0.5365 | 74.0 | 296 | 0.5836 | 0.9141 |
128
- | 0.5368 | 75.0 | 300 | 0.5836 | 0.9141 |
129
 
130
 
131
  ### Framework versions
 
1
  ---
2
+ license: apache-2.0
3
+ base_model: bert-base-uncased
4
  tags:
5
  - generated_from_trainer
6
  metrics:
7
  - accuracy
8
  model-index:
9
+ - name: bert-base-uncased-sst-2-64-13-smoothed
10
  results: []
11
  ---
12
 
13
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
14
  should probably proofread and complete it, then remove this comment. -->
15
 
16
+ # bert-base-uncased-sst-2-64-13-smoothed
17
 
18
+ This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
19
  It achieves the following results on the evaluation set:
20
+ - Loss: 0.6180
21
+ - Accuracy: 0.8281
22
 
23
  ## Model description
24
 
 
51
 
52
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
53
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
54
+ | No log | 1.0 | 4 | 0.6942 | 0.5234 |
55
+ | No log | 2.0 | 8 | 0.6933 | 0.5547 |
56
+ | 0.6914 | 3.0 | 12 | 0.6922 | 0.5547 |
57
+ | 0.6914 | 4.0 | 16 | 0.6906 | 0.5469 |
58
+ | 0.692 | 5.0 | 20 | 0.6887 | 0.5781 |
59
+ | 0.692 | 6.0 | 24 | 0.6867 | 0.6328 |
60
+ | 0.692 | 7.0 | 28 | 0.6841 | 0.6406 |
61
+ | 0.6784 | 8.0 | 32 | 0.6813 | 0.6406 |
62
+ | 0.6784 | 9.0 | 36 | 0.6780 | 0.6484 |
63
+ | 0.6647 | 10.0 | 40 | 0.6731 | 0.6484 |
64
+ | 0.6647 | 11.0 | 44 | 0.6674 | 0.7188 |
65
+ | 0.6647 | 12.0 | 48 | 0.6615 | 0.7344 |
66
+ | 0.6336 | 13.0 | 52 | 0.6535 | 0.7109 |
67
+ | 0.6336 | 14.0 | 56 | 0.6475 | 0.7422 |
68
+ | 0.5851 | 15.0 | 60 | 0.6394 | 0.7812 |
69
+ | 0.5851 | 16.0 | 64 | 0.6344 | 0.7656 |
70
+ | 0.5851 | 17.0 | 68 | 0.6371 | 0.7578 |
71
+ | 0.5508 | 18.0 | 72 | 0.6343 | 0.7656 |
72
+ | 0.5508 | 19.0 | 76 | 0.6337 | 0.7734 |
73
+ | 0.5412 | 20.0 | 80 | 0.6377 | 0.7578 |
74
+ | 0.5412 | 21.0 | 84 | 0.6349 | 0.7812 |
75
+ | 0.5412 | 22.0 | 88 | 0.6269 | 0.7891 |
76
+ | 0.5393 | 23.0 | 92 | 0.6227 | 0.8281 |
77
+ | 0.5393 | 24.0 | 96 | 0.6209 | 0.8203 |
78
+ | 0.5375 | 25.0 | 100 | 0.6198 | 0.8203 |
79
+ | 0.5375 | 26.0 | 104 | 0.6194 | 0.8359 |
80
+ | 0.5375 | 27.0 | 108 | 0.6205 | 0.8203 |
81
+ | 0.5377 | 28.0 | 112 | 0.6223 | 0.8125 |
82
+ | 0.5377 | 29.0 | 116 | 0.6236 | 0.8047 |
83
+ | 0.537 | 30.0 | 120 | 0.6235 | 0.8047 |
84
+ | 0.537 | 31.0 | 124 | 0.6250 | 0.7891 |
85
+ | 0.537 | 32.0 | 128 | 0.6243 | 0.7969 |
86
+ | 0.5375 | 33.0 | 132 | 0.6215 | 0.8281 |
87
+ | 0.5375 | 34.0 | 136 | 0.6206 | 0.8203 |
88
+ | 0.5368 | 35.0 | 140 | 0.6201 | 0.8281 |
89
+ | 0.5368 | 36.0 | 144 | 0.6200 | 0.8359 |
90
+ | 0.5368 | 37.0 | 148 | 0.6198 | 0.8359 |
91
+ | 0.5363 | 38.0 | 152 | 0.6199 | 0.8281 |
92
+ | 0.5363 | 39.0 | 156 | 0.6202 | 0.8203 |
93
+ | 0.5365 | 40.0 | 160 | 0.6198 | 0.8281 |
94
+ | 0.5365 | 41.0 | 164 | 0.6192 | 0.8359 |
95
+ | 0.5365 | 42.0 | 168 | 0.6192 | 0.8438 |
96
+ | 0.5369 | 43.0 | 172 | 0.6188 | 0.8281 |
97
+ | 0.5369 | 44.0 | 176 | 0.6192 | 0.8203 |
98
+ | 0.5363 | 45.0 | 180 | 0.6196 | 0.8203 |
99
+ | 0.5363 | 46.0 | 184 | 0.6186 | 0.8203 |
100
+ | 0.5363 | 47.0 | 188 | 0.6182 | 0.8359 |
101
+ | 0.5362 | 48.0 | 192 | 0.6181 | 0.8359 |
102
+ | 0.5362 | 49.0 | 196 | 0.6182 | 0.8203 |
103
+ | 0.5365 | 50.0 | 200 | 0.6182 | 0.8203 |
104
+ | 0.5365 | 51.0 | 204 | 0.6179 | 0.8359 |
105
+ | 0.5365 | 52.0 | 208 | 0.6177 | 0.8359 |
106
+ | 0.5359 | 53.0 | 212 | 0.6175 | 0.8359 |
107
+ | 0.5359 | 54.0 | 216 | 0.6174 | 0.8281 |
108
+ | 0.5366 | 55.0 | 220 | 0.6174 | 0.8359 |
109
+ | 0.5366 | 56.0 | 224 | 0.6175 | 0.8359 |
110
+ | 0.5366 | 57.0 | 228 | 0.6176 | 0.8359 |
111
+ | 0.5362 | 58.0 | 232 | 0.6177 | 0.8359 |
112
+ | 0.5362 | 59.0 | 236 | 0.6179 | 0.8359 |
113
+ | 0.5359 | 60.0 | 240 | 0.6179 | 0.8359 |
114
+ | 0.5359 | 61.0 | 244 | 0.6178 | 0.8359 |
115
+ | 0.5359 | 62.0 | 248 | 0.6177 | 0.8359 |
116
+ | 0.5358 | 63.0 | 252 | 0.6178 | 0.8359 |
117
+ | 0.5358 | 64.0 | 256 | 0.6179 | 0.8359 |
118
+ | 0.5361 | 65.0 | 260 | 0.6181 | 0.8281 |
119
+ | 0.5361 | 66.0 | 264 | 0.6182 | 0.8281 |
120
+ | 0.5361 | 67.0 | 268 | 0.6181 | 0.8281 |
121
+ | 0.5358 | 68.0 | 272 | 0.6181 | 0.8281 |
122
+ | 0.5358 | 69.0 | 276 | 0.6181 | 0.8281 |
123
+ | 0.5362 | 70.0 | 280 | 0.6180 | 0.8281 |
124
+ | 0.5362 | 71.0 | 284 | 0.6180 | 0.8359 |
125
+ | 0.5362 | 72.0 | 288 | 0.6180 | 0.8359 |
126
+ | 0.5356 | 73.0 | 292 | 0.6180 | 0.8359 |
127
+ | 0.5356 | 74.0 | 296 | 0.6180 | 0.8281 |
128
+ | 0.5358 | 75.0 | 300 | 0.6180 | 0.8281 |
129
 
130
 
131
  ### Framework versions