Hugging Face's logo

simonycl
/
roberta-base-sst-2-64-13

Text Classification
Transformers
PyTorch
roberta
Generated from Trainer
Inference Endpoints
Model card Files Community
1
simonycl commited on
Commit
ef281ee
·
1 Parent(s): a696b1e

update model card README.md

Browse files
Files changed (1) hide show
  1. README.md +210 -0
README.md ADDED
@@ -0,0 +1,210 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: mit
3
+ base_model: roberta-base
4
+ tags:
5
+ - generated_from_trainer
6
+ metrics:
7
+ - accuracy
8
+ model-index:
9
+ - name: roberta-base-sst-2-64-13
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-base-sst-2-64-13
17
+
18
+ This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
19
+ It achieves the following results on the evaluation set:
20
+ - Loss: 1.0411
21
+ - Accuracy: 0.8672
22
+
23
+ ## Model description
24
+
25
+ More information needed
26
+
27
+ ## Intended uses & limitations
28
+
29
+ More information needed
30
+
31
+ ## Training and evaluation data
32
+
33
+ More information needed
34
+
35
+ ## Training procedure
36
+
37
+ ### Training hyperparameters
38
+
39
+ The following hyperparameters were used during training:
40
+ - learning_rate: 1e-05
41
+ - train_batch_size: 32
42
+ - eval_batch_size: 32
43
+ - seed: 42
44
+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
45
+ - lr_scheduler_type: linear
46
+ - lr_scheduler_warmup_steps: 500
47
+ - num_epochs: 150
48
+
49
+ ### Training results
50
+
51
+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
52
+ |:-------------:|:-----:|:----:|:---------------:|:--------:|
53
+ | No log | 1.0 | 4 | 0.6951 | 0.5 |
54
+ | No log | 2.0 | 8 | 0.6951 | 0.5 |
55
+ | 0.6962 | 3.0 | 12 | 0.6951 | 0.5 |
56
+ | 0.6962 | 4.0 | 16 | 0.6950 | 0.5 |
57
+ | 0.7017 | 5.0 | 20 | 0.6949 | 0.5 |
58
+ | 0.7017 | 6.0 | 24 | 0.6949 | 0.5 |
59
+ | 0.7017 | 7.0 | 28 | 0.6947 | 0.5 |
60
+ | 0.6966 | 8.0 | 32 | 0.6946 | 0.5 |
61
+ | 0.6966 | 9.0 | 36 | 0.6945 | 0.5 |
62
+ | 0.6927 | 10.0 | 40 | 0.6944 | 0.5 |
63
+ | 0.6927 | 11.0 | 44 | 0.6943 | 0.5 |
64
+ | 0.6927 | 12.0 | 48 | 0.6941 | 0.5 |
65
+ | 0.6961 | 13.0 | 52 | 0.6940 | 0.5 |
66
+ | 0.6961 | 14.0 | 56 | 0.6939 | 0.5 |
67
+ | 0.6875 | 15.0 | 60 | 0.6938 | 0.5 |
68
+ | 0.6875 | 16.0 | 64 | 0.6936 | 0.5 |
69
+ | 0.6875 | 17.0 | 68 | 0.6934 | 0.5 |
70
+ | 0.6935 | 18.0 | 72 | 0.6932 | 0.5 |
71
+ | 0.6935 | 19.0 | 76 | 0.6929 | 0.5 |
72
+ | 0.6948 | 20.0 | 80 | 0.6927 | 0.5 |
73
+ | 0.6948 | 21.0 | 84 | 0.6924 | 0.5 |
74
+ | 0.6948 | 22.0 | 88 | 0.6922 | 0.5 |
75
+ | 0.6906 | 23.0 | 92 | 0.6920 | 0.5 |
76
+ | 0.6906 | 24.0 | 96 | 0.6917 | 0.5 |
77
+ | 0.691 | 25.0 | 100 | 0.6913 | 0.5 |
78
+ | 0.691 | 26.0 | 104 | 0.6909 | 0.5 |
79
+ | 0.691 | 27.0 | 108 | 0.6904 | 0.5 |
80
+ | 0.6855 | 28.0 | 112 | 0.6899 | 0.5 |
81
+ | 0.6855 | 29.0 | 116 | 0.6891 | 0.5 |
82
+ | 0.6858 | 30.0 | 120 | 0.6882 | 0.5234 |
83
+ | 0.6858 | 31.0 | 124 | 0.6870 | 0.5156 |
84
+ | 0.6858 | 32.0 | 128 | 0.6852 | 0.6016 |
85
+ | 0.6764 | 33.0 | 132 | 0.6825 | 0.6562 |
86
+ | 0.6764 | 34.0 | 136 | 0.6782 | 0.7266 |
87
+ | 0.6616 | 35.0 | 140 | 0.6703 | 0.7969 |
88
+ | 0.6616 | 36.0 | 144 | 0.6545 | 0.8281 |
89
+ | 0.6616 | 37.0 | 148 | 0.6245 | 0.8516 |
90
+ | 0.6082 | 38.0 | 152 | 0.5651 | 0.8594 |
91
+ | 0.6082 | 39.0 | 156 | 0.4835 | 0.875 |
92
+ | 0.4548 | 40.0 | 160 | 0.4109 | 0.9062 |
93
+ | 0.4548 | 41.0 | 164 | 0.3606 | 0.875 |
94
+ | 0.4548 | 42.0 | 168 | 0.3454 | 0.8594 |
95
+ | 0.2218 | 43.0 | 172 | 0.3403 | 0.8594 |
96
+ | 0.2218 | 44.0 | 176 | 0.3537 | 0.8828 |
97
+ | 0.0892 | 45.0 | 180 | 0.4646 | 0.8516 |
98
+ | 0.0892 | 46.0 | 184 | 0.4402 | 0.875 |
99
+ | 0.0892 | 47.0 | 188 | 0.4719 | 0.8828 |
100
+ | 0.0254 | 48.0 | 192 | 0.5172 | 0.8828 |
101
+ | 0.0254 | 49.0 | 196 | 0.5613 | 0.8828 |
102
+ | 0.0105 | 50.0 | 200 | 0.6035 | 0.875 |
103
+ | 0.0105 | 51.0 | 204 | 0.6341 | 0.875 |
104
+ | 0.0105 | 52.0 | 208 | 0.6591 | 0.875 |
105
+ | 0.006 | 53.0 | 212 | 0.6804 | 0.875 |
106
+ | 0.006 | 54.0 | 216 | 0.6935 | 0.875 |
107
+ | 0.0041 | 55.0 | 220 | 0.7167 | 0.875 |
108
+ | 0.0041 | 56.0 | 224 | 0.7315 | 0.875 |
109
+ | 0.0041 | 57.0 | 228 | 0.7464 | 0.875 |
110
+ | 0.0032 | 58.0 | 232 | 0.7560 | 0.8594 |
111
+ | 0.0032 | 59.0 | 236 | 0.8753 | 0.8516 |
112
+ | 0.0098 | 60.0 | 240 | 0.9437 | 0.8438 |
113
+ | 0.0098 | 61.0 | 244 | 0.7740 | 0.8672 |
114
+ | 0.0098 | 62.0 | 248 | 0.7258 | 0.8828 |
115
+ | 0.0094 | 63.0 | 252 | 0.7815 | 0.8594 |
116
+ | 0.0094 | 64.0 | 256 | 0.7836 | 0.8516 |
117
+ | 0.0021 | 65.0 | 260 | 0.7854 | 0.8516 |
118
+ | 0.0021 | 66.0 | 264 | 0.7817 | 0.8594 |
119
+ | 0.0021 | 67.0 | 268 | 0.7698 | 0.8828 |
120
+ | 0.0019 | 68.0 | 272 | 0.7848 | 0.875 |
121
+ | 0.0019 | 69.0 | 276 | 0.7895 | 0.8828 |
122
+ | 0.0017 | 70.0 | 280 | 0.7971 | 0.8828 |
123
+ | 0.0017 | 71.0 | 284 | 0.8038 | 0.8828 |
124
+ | 0.0017 | 72.0 | 288 | 0.8091 | 0.8828 |
125
+ | 0.0014 | 73.0 | 292 | 0.8139 | 0.8828 |
126
+ | 0.0014 | 74.0 | 296 | 0.8183 | 0.8828 |
127
+ | 0.0014 | 75.0 | 300 | 0.8223 | 0.8828 |
128
+ | 0.0014 | 76.0 | 304 | 0.8274 | 0.8828 |
129
+ | 0.0014 | 77.0 | 308 | 0.8357 | 0.875 |
130
+ | 0.0012 | 78.0 | 312 | 0.8436 | 0.875 |
131
+ | 0.0012 | 79.0 | 316 | 0.8523 | 0.875 |
132
+ | 0.0012 | 80.0 | 320 | 0.8591 | 0.875 |
133
+ | 0.0012 | 81.0 | 324 | 0.8653 | 0.875 |
134
+ | 0.0012 | 82.0 | 328 | 0.8708 | 0.875 |
135
+ | 0.001 | 83.0 | 332 | 0.8271 | 0.8594 |
136
+ | 0.001 | 84.0 | 336 | 1.0450 | 0.8438 |
137
+ | 0.0012 | 85.0 | 340 | 1.1347 | 0.8281 |
138
+ | 0.0012 | 86.0 | 344 | 1.1696 | 0.8281 |
139
+ | 0.0012 | 87.0 | 348 | 0.8631 | 0.8672 |
140
+ | 0.0137 | 88.0 | 352 | 1.1491 | 0.8359 |
141
+ | 0.0137 | 89.0 | 356 | 1.0635 | 0.8516 |
142
+ | 0.0012 | 90.0 | 360 | 0.9027 | 0.875 |
143
+ | 0.0012 | 91.0 | 364 | 0.9503 | 0.8594 |
144
+ | 0.0012 | 92.0 | 368 | 1.0398 | 0.8281 |
145
+ | 0.0185 | 93.0 | 372 | 0.9044 | 0.875 |
146
+ | 0.0185 | 94.0 | 376 | 1.0978 | 0.8438 |
147
+ | 0.0009 | 95.0 | 380 | 0.9955 | 0.8672 |
148
+ | 0.0009 | 96.0 | 384 | 0.9313 | 0.875 |
149
+ | 0.0009 | 97.0 | 388 | 0.9295 | 0.875 |
150
+ | 0.0008 | 98.0 | 392 | 1.0927 | 0.8516 |
151
+ | 0.0008 | 99.0 | 396 | 0.9251 | 0.875 |
152
+ | 0.0007 | 100.0 | 400 | 0.9454 | 0.8594 |
153
+ | 0.0007 | 101.0 | 404 | 1.0023 | 0.8516 |
154
+ | 0.0007 | 102.0 | 408 | 1.0098 | 0.8516 |
155
+ | 0.0006 | 103.0 | 412 | 0.9944 | 0.8594 |
156
+ | 0.0006 | 104.0 | 416 | 0.9832 | 0.8516 |
157
+ | 0.0006 | 105.0 | 420 | 0.9090 | 0.8828 |
158
+ | 0.0006 | 106.0 | 424 | 1.2248 | 0.8359 |
159
+ | 0.0006 | 107.0 | 428 | 0.8722 | 0.8906 |
160
+ | 0.0197 | 108.0 | 432 | 0.8764 | 0.8828 |
161
+ | 0.0197 | 109.0 | 436 | 0.9771 | 0.875 |
162
+ | 0.0005 | 110.0 | 440 | 0.9871 | 0.875 |
163
+ | 0.0005 | 111.0 | 444 | 0.9235 | 0.875 |
164
+ | 0.0005 | 112.0 | 448 | 0.8418 | 0.8828 |
165
+ | 0.0005 | 113.0 | 452 | 0.8653 | 0.8906 |
166
+ | 0.0005 | 114.0 | 456 | 0.9098 | 0.8828 |
167
+ | 0.0005 | 115.0 | 460 | 0.9285 | 0.8828 |
168
+ | 0.0005 | 116.0 | 464 | 0.9443 | 0.875 |
169
+ | 0.0005 | 117.0 | 468 | 0.9584 | 0.8672 |
170
+ | 0.0005 | 118.0 | 472 | 0.9704 | 0.8672 |
171
+ | 0.0005 | 119.0 | 476 | 0.9805 | 0.8672 |
172
+ | 0.0004 | 120.0 | 480 | 0.9904 | 0.8672 |
173
+ | 0.0004 | 121.0 | 484 | 0.9920 | 0.8672 |
174
+ | 0.0004 | 122.0 | 488 | 0.9927 | 0.8672 |
175
+ | 0.0004 | 123.0 | 492 | 1.0015 | 0.8672 |
176
+ | 0.0004 | 124.0 | 496 | 1.0181 | 0.8672 |
177
+ | 0.0004 | 125.0 | 500 | 1.0289 | 0.8672 |
178
+ | 0.0004 | 126.0 | 504 | 1.0374 | 0.8672 |
179
+ | 0.0004 | 127.0 | 508 | 1.0408 | 0.8672 |
180
+ | 0.0004 | 128.0 | 512 | 1.0432 | 0.8672 |
181
+ | 0.0004 | 129.0 | 516 | 1.0472 | 0.8672 |
182
+ | 0.0003 | 130.0 | 520 | 1.0489 | 0.8672 |
183
+ | 0.0003 | 131.0 | 524 | 1.0497 | 0.8672 |
184
+ | 0.0003 | 132.0 | 528 | 1.0496 | 0.8672 |
185
+ | 0.0003 | 133.0 | 532 | 1.0497 | 0.8672 |
186
+ | 0.0003 | 134.0 | 536 | 1.0496 | 0.8672 |
187
+ | 0.0003 | 135.0 | 540 | 1.0492 | 0.8672 |
188
+ | 0.0003 | 136.0 | 544 | 1.0491 | 0.8672 |
189
+ | 0.0003 | 137.0 | 548 | 1.0482 | 0.8672 |
190
+ | 0.0003 | 138.0 | 552 | 1.0471 | 0.8672 |
191
+ | 0.0003 | 139.0 | 556 | 1.0456 | 0.8672 |
192
+ | 0.0003 | 140.0 | 560 | 1.0432 | 0.8672 |
193
+ | 0.0003 | 141.0 | 564 | 1.0411 | 0.8672 |
194
+ | 0.0003 | 142.0 | 568 | 1.0399 | 0.8672 |
195
+ | 0.0003 | 143.0 | 572 | 1.0398 | 0.8672 |
196
+ | 0.0003 | 144.0 | 576 | 1.0396 | 0.8672 |
197
+ | 0.0003 | 145.0 | 580 | 1.0393 | 0.8672 |
198
+ | 0.0003 | 146.0 | 584 | 1.0396 | 0.8672 |
199
+ | 0.0003 | 147.0 | 588 | 1.0400 | 0.8672 |
200
+ | 0.0003 | 148.0 | 592 | 1.0405 | 0.8672 |
201
+ | 0.0003 | 149.0 | 596 | 1.0409 | 0.8672 |
202
+ | 0.0003 | 150.0 | 600 | 1.0411 | 0.8672 |
203
+
204
+
205
+ ### Framework versions
206
+
207
+ - Transformers 4.32.0.dev0
208
+ - Pytorch 2.0.1+cu118
209
+ - Datasets 2.4.0
210
+ - Tokenizers 0.13.3