Hugging Face's logo

simonycl
/
best_model-yelp_polarity-64-87

Text Classification
Transformers
PyTorch
albert
Generated from Trainer
Inference Endpoints
Model card Files Community
simonycl commited on
Commit
d8049a3
•
1 Parent(s): 4a33bc4

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: best_model-yelp_polarity-64-87
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
+ # best_model-yelp_polarity-64-87
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: 0.6151
21
+ - Accuracy: 0.9453
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 | 0.3505 | 0.9531 |
54
+ | No log | 2.0 | 8 | 0.3554 | 0.9531 |
55
+ | 0.7909 | 3.0 | 12 | 0.3781 | 0.9531 |
56
+ | 0.7909 | 4.0 | 16 | 0.4031 | 0.9531 |
57
+ | 0.5682 | 5.0 | 20 | 0.4409 | 0.9375 |
58
+ | 0.5682 | 6.0 | 24 | 0.5003 | 0.9453 |
59
+ | 0.5682 | 7.0 | 28 | 0.5068 | 0.9453 |
60
+ | 0.6452 | 8.0 | 32 | 0.4511 | 0.9453 |
61
+ | 0.6452 | 9.0 | 36 | 0.3963 | 0.9531 |
62
+ | 0.5947 | 10.0 | 40 | 0.3820 | 0.9531 |
63
+ | 0.5947 | 11.0 | 44 | 0.3797 | 0.9453 |
64
+ | 0.5947 | 12.0 | 48 | 0.4010 | 0.9453 |
65
+ | 0.6099 | 13.0 | 52 | 0.3783 | 0.9531 |
66
+ | 0.6099 | 14.0 | 56 | 0.3875 | 0.9531 |
67
+ | 0.3653 | 15.0 | 60 | 0.3945 | 0.9453 |
68
+ | 0.3653 | 16.0 | 64 | 0.4100 | 0.9453 |
69
+ | 0.3653 | 17.0 | 68 | 0.4161 | 0.9531 |
70
+ | 0.3425 | 18.0 | 72 | 0.4085 | 0.9531 |
71
+ | 0.3425 | 19.0 | 76 | 0.3950 | 0.9453 |
72
+ | 0.3247 | 20.0 | 80 | 0.3743 | 0.9531 |
73
+ | 0.3247 | 21.0 | 84 | 0.4230 | 0.9531 |
74
+ | 0.3247 | 22.0 | 88 | 0.4502 | 0.9453 |
75
+ | 0.2242 | 23.0 | 92 | 0.3965 | 0.9531 |
76
+ | 0.2242 | 24.0 | 96 | 0.3779 | 0.9453 |
77
+ | 0.1052 | 25.0 | 100 | 0.3940 | 0.9297 |
78
+ | 0.1052 | 26.0 | 104 | 0.4213 | 0.9375 |
79
+ | 0.1052 | 27.0 | 108 | 0.4330 | 0.9297 |
80
+ | 0.018 | 28.0 | 112 | 0.4165 | 0.9453 |
81
+ | 0.018 | 29.0 | 116 | 0.4136 | 0.9453 |
82
+ | 0.002 | 30.0 | 120 | 0.4521 | 0.9219 |
83
+ | 0.002 | 31.0 | 124 | 0.4985 | 0.9141 |
84
+ | 0.002 | 32.0 | 128 | 0.5143 | 0.9141 |
85
+ | 0.0002 | 33.0 | 132 | 0.5213 | 0.9141 |
86
+ | 0.0002 | 34.0 | 136 | 0.4808 | 0.9219 |
87
+ | 0.0002 | 35.0 | 140 | 0.4556 | 0.9453 |
88
+ | 0.0002 | 36.0 | 144 | 0.4534 | 0.9453 |
89
+ | 0.0002 | 37.0 | 148 | 0.4546 | 0.9453 |
90
+ | 0.0001 | 38.0 | 152 | 0.4599 | 0.9453 |
91
+ | 0.0001 | 39.0 | 156 | 0.4673 | 0.9453 |
92
+ | 0.0001 | 40.0 | 160 | 0.4749 | 0.9453 |
93
+ | 0.0001 | 41.0 | 164 | 0.4821 | 0.9453 |
94
+ | 0.0001 | 42.0 | 168 | 0.4891 | 0.9453 |
95
+ | 0.0001 | 43.0 | 172 | 0.4956 | 0.9375 |
96
+ | 0.0001 | 44.0 | 176 | 0.4995 | 0.9453 |
97
+ | 0.0001 | 45.0 | 180 | 0.5077 | 0.9375 |
98
+ | 0.0001 | 46.0 | 184 | 0.5162 | 0.9375 |
99
+ | 0.0001 | 47.0 | 188 | 0.5253 | 0.9375 |
100
+ | 0.0 | 48.0 | 192 | 0.5321 | 0.9375 |
101
+ | 0.0 | 49.0 | 196 | 0.5369 | 0.9375 |
102
+ | 0.0001 | 50.0 | 200 | 0.5388 | 0.9375 |
103
+ | 0.0001 | 51.0 | 204 | 0.5248 | 0.9453 |
104
+ | 0.0001 | 52.0 | 208 | 0.5274 | 0.9375 |
105
+ | 0.0001 | 53.0 | 212 | 0.5331 | 0.9297 |
106
+ | 0.0001 | 54.0 | 216 | 0.5374 | 0.9297 |
107
+ | 0.0 | 55.0 | 220 | 0.5403 | 0.9297 |
108
+ | 0.0 | 56.0 | 224 | 0.5447 | 0.9297 |
109
+ | 0.0 | 57.0 | 228 | 0.5478 | 0.9297 |
110
+ | 0.0 | 58.0 | 232 | 0.5497 | 0.9297 |
111
+ | 0.0 | 59.0 | 236 | 0.5505 | 0.9297 |
112
+ | 0.0 | 60.0 | 240 | 0.5511 | 0.9297 |
113
+ | 0.0 | 61.0 | 244 | 0.5518 | 0.9375 |
114
+ | 0.0 | 62.0 | 248 | 0.5498 | 0.9375 |
115
+ | 0.0 | 63.0 | 252 | 0.5507 | 0.9453 |
116
+ | 0.0 | 64.0 | 256 | 0.5542 | 0.9453 |
117
+ | 0.0 | 65.0 | 260 | 0.5578 | 0.9453 |
118
+ | 0.0 | 66.0 | 264 | 0.5610 | 0.9453 |
119
+ | 0.0 | 67.0 | 268 | 0.5637 | 0.9453 |
120
+ | 0.0 | 68.0 | 272 | 0.5662 | 0.9453 |
121
+ | 0.0 | 69.0 | 276 | 0.5685 | 0.9453 |
122
+ | 0.0 | 70.0 | 280 | 0.5705 | 0.9453 |
123
+ | 0.0 | 71.0 | 284 | 0.5725 | 0.9453 |
124
+ | 0.0 | 72.0 | 288 | 0.5738 | 0.9453 |
125
+ | 0.0 | 73.0 | 292 | 0.5753 | 0.9453 |
126
+ | 0.0 | 74.0 | 296 | 0.5768 | 0.9453 |
127
+ | 0.0 | 75.0 | 300 | 0.5780 | 0.9453 |
128
+ | 0.0 | 76.0 | 304 | 0.5790 | 0.9453 |
129
+ | 0.0 | 77.0 | 308 | 0.5798 | 0.9453 |
130
+ | 0.0 | 78.0 | 312 | 0.5802 | 0.9453 |
131
+ | 0.0 | 79.0 | 316 | 0.5807 | 0.9453 |
132
+ | 0.0 | 80.0 | 320 | 0.5816 | 0.9453 |
133
+ | 0.0 | 81.0 | 324 | 0.5825 | 0.9453 |
134
+ | 0.0 | 82.0 | 328 | 0.5833 | 0.9453 |
135
+ | 0.0 | 83.0 | 332 | 0.5842 | 0.9453 |
136
+ | 0.0 | 84.0 | 336 | 0.5852 | 0.9453 |
137
+ | 0.0 | 85.0 | 340 | 0.5860 | 0.9453 |
138
+ | 0.0 | 86.0 | 344 | 0.5865 | 0.9453 |
139
+ | 0.0 | 87.0 | 348 | 0.5869 | 0.9453 |
140
+ | 0.0 | 88.0 | 352 | 0.5875 | 0.9453 |
141
+ | 0.0 | 89.0 | 356 | 0.5885 | 0.9453 |
142
+ | 0.0 | 90.0 | 360 | 0.5897 | 0.9453 |
143
+ | 0.0 | 91.0 | 364 | 0.5908 | 0.9453 |
144
+ | 0.0 | 92.0 | 368 | 0.5921 | 0.9453 |
145
+ | 0.0 | 93.0 | 372 | 0.5932 | 0.9453 |
146
+ | 0.0 | 94.0 | 376 | 0.5943 | 0.9453 |
147
+ | 0.0 | 95.0 | 380 | 0.5955 | 0.9453 |
148
+ | 0.0 | 96.0 | 384 | 0.5965 | 0.9453 |
149
+ | 0.0 | 97.0 | 388 | 0.5976 | 0.9453 |
150
+ | 0.0 | 98.0 | 392 | 0.5986 | 0.9453 |
151
+ | 0.0 | 99.0 | 396 | 0.5982 | 0.9453 |
152
+ | 0.0 | 100.0 | 400 | 0.5981 | 0.9453 |
153
+ | 0.0 | 101.0 | 404 | 0.5980 | 0.9453 |
154
+ | 0.0 | 102.0 | 408 | 0.5978 | 0.9453 |
155
+ | 0.0 | 103.0 | 412 | 0.5979 | 0.9453 |
156
+ | 0.0 | 104.0 | 416 | 0.5974 | 0.9453 |
157
+ | 0.0 | 105.0 | 420 | 0.5970 | 0.9453 |
158
+ | 0.0 | 106.0 | 424 | 0.5978 | 0.9453 |
159
+ | 0.0 | 107.0 | 428 | 0.5986 | 0.9453 |
160
+ | 0.0 | 108.0 | 432 | 0.5995 | 0.9453 |
161
+ | 0.0 | 109.0 | 436 | 0.6002 | 0.9453 |
162
+ | 0.0 | 110.0 | 440 | 0.6014 | 0.9453 |
163
+ | 0.0 | 111.0 | 444 | 0.6027 | 0.9453 |
164
+ | 0.0 | 112.0 | 448 | 0.6042 | 0.9453 |
165
+ | 0.0 | 113.0 | 452 | 0.6054 | 0.9453 |
166
+ | 0.0 | 114.0 | 456 | 0.6067 | 0.9453 |
167
+ | 0.0 | 115.0 | 460 | 0.6078 | 0.9453 |
168
+ | 0.0 | 116.0 | 464 | 0.6086 | 0.9453 |
169
+ | 0.0 | 117.0 | 468 | 0.6092 | 0.9453 |
170
+ | 0.0 | 118.0 | 472 | 0.6098 | 0.9453 |
171
+ | 0.0 | 119.0 | 476 | 0.6103 | 0.9453 |
172
+ | 0.0 | 120.0 | 480 | 0.6110 | 0.9453 |
173
+ | 0.0 | 121.0 | 484 | 0.6117 | 0.9453 |
174
+ | 0.0 | 122.0 | 488 | 0.6124 | 0.9453 |
175
+ | 0.0 | 123.0 | 492 | 0.6128 | 0.9453 |
176
+ | 0.0 | 124.0 | 496 | 0.6129 | 0.9453 |
177
+ | 0.0 | 125.0 | 500 | 0.6129 | 0.9453 |
178
+ | 0.0 | 126.0 | 504 | 0.6130 | 0.9453 |
179
+ | 0.0 | 127.0 | 508 | 0.6133 | 0.9453 |
180
+ | 0.0 | 128.0 | 512 | 0.6136 | 0.9453 |
181
+ | 0.0 | 129.0 | 516 | 0.6139 | 0.9453 |
182
+ | 0.0 | 130.0 | 520 | 0.6143 | 0.9453 |
183
+ | 0.0 | 131.0 | 524 | 0.6146 | 0.9453 |
184
+ | 0.0 | 132.0 | 528 | 0.6149 | 0.9453 |
185
+ | 0.0 | 133.0 | 532 | 0.6151 | 0.9453 |
186
+ | 0.0 | 134.0 | 536 | 0.6150 | 0.9453 |
187
+ | 0.0 | 135.0 | 540 | 0.6144 | 0.9453 |
188
+ | 0.0 | 136.0 | 544 | 0.6141 | 0.9453 |
189
+ | 0.0 | 137.0 | 548 | 0.6140 | 0.9453 |
190
+ | 0.0 | 138.0 | 552 | 0.6141 | 0.9453 |
191
+ | 0.0 | 139.0 | 556 | 0.6141 | 0.9453 |
192
+ | 0.0 | 140.0 | 560 | 0.6140 | 0.9453 |
193
+ | 0.0 | 141.0 | 564 | 0.6139 | 0.9453 |
194
+ | 0.0 | 142.0 | 568 | 0.6139 | 0.9453 |
195
+ | 0.0 | 143.0 | 572 | 0.6140 | 0.9453 |
196
+ | 0.0 | 144.0 | 576 | 0.6143 | 0.9453 |
197
+ | 0.0 | 145.0 | 580 | 0.6146 | 0.9453 |
198
+ | 0.0 | 146.0 | 584 | 0.6148 | 0.9453 |
199
+ | 0.0 | 147.0 | 588 | 0.6149 | 0.9453 |
200
+ | 0.0 | 148.0 | 592 | 0.6150 | 0.9453 |
201
+ | 0.0 | 149.0 | 596 | 0.6150 | 0.9453 |
202
+ | 0.0 | 150.0 | 600 | 0.6151 | 0.9453 |
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