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  1. README.md +49 -199
  2. model.safetensors +1 -1
  3. runs/May27_21-10-12_34ec97677d65/events.out.tfevents.1716844213.34ec97677d65.1835.9 +3 -0
  4. runs/May27_21-10-23_34ec97677d65/events.out.tfevents.1716844223.34ec97677d65.1835.10 +3 -0
  5. runs/May27_21-10-23_34ec97677d65/events.out.tfevents.1716844248.34ec97677d65.1835.11 +3 -0
  6. training_args.bin +1 -1
README.md CHANGED
@@ -15,7 +15,7 @@ should probably proofread and complete it, then remove this comment. -->
15
 
16
  This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
17
  It achieves the following results on the evaluation set:
18
- - Loss: 2.6090
19
 
20
  ## Model description
21
 
@@ -40,7 +40,7 @@ The following hyperparameters were used during training:
40
  - seed: 42
41
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
42
  - lr_scheduler_type: linear
43
- - num_epochs: 200
44
 
45
  ### Training results
46
 
@@ -48,204 +48,54 @@ The following hyperparameters were used during training:
48
  |:-------------:|:-----:|:----:|:---------------:|
49
  | No log | 1.0 | 2 | 3.0764 |
50
  | No log | 2.0 | 4 | 2.9682 |
51
- | No log | 3.0 | 6 | 2.9191 |
52
  | No log | 4.0 | 8 | 2.7053 |
53
- | No log | 5.0 | 10 | 3.2640 |
54
- | No log | 6.0 | 12 | 3.0685 |
55
- | No log | 7.0 | 14 | 2.8963 |
56
- | No log | 8.0 | 16 | 2.9284 |
57
- | No log | 9.0 | 18 | 3.2295 |
58
- | No log | 10.0 | 20 | 2.8835 |
59
- | No log | 11.0 | 22 | 2.6665 |
60
- | No log | 12.0 | 24 | 2.7967 |
61
- | No log | 13.0 | 26 | 2.5926 |
62
- | No log | 14.0 | 28 | 2.8099 |
63
- | No log | 15.0 | 30 | 2.7691 |
64
- | No log | 16.0 | 32 | 2.6093 |
65
- | No log | 17.0 | 34 | 3.0369 |
66
- | No log | 18.0 | 36 | 3.1333 |
67
- | No log | 19.0 | 38 | 3.0708 |
68
- | No log | 20.0 | 40 | 2.6644 |
69
- | No log | 21.0 | 42 | 2.5332 |
70
- | No log | 22.0 | 44 | 2.8451 |
71
- | No log | 23.0 | 46 | 3.1622 |
72
- | No log | 24.0 | 48 | 3.0130 |
73
- | No log | 25.0 | 50 | 3.2160 |
74
- | No log | 26.0 | 52 | 3.0518 |
75
- | No log | 27.0 | 54 | 2.6505 |
76
- | No log | 28.0 | 56 | 2.8131 |
77
- | No log | 29.0 | 58 | 2.9358 |
78
- | No log | 30.0 | 60 | 2.8246 |
79
- | No log | 31.0 | 62 | 2.7509 |
80
- | No log | 32.0 | 64 | 2.9384 |
81
- | No log | 33.0 | 66 | 2.5823 |
82
- | No log | 34.0 | 68 | 2.8795 |
83
- | No log | 35.0 | 70 | 2.5153 |
84
- | No log | 36.0 | 72 | 2.9278 |
85
- | No log | 37.0 | 74 | 3.0411 |
86
- | No log | 38.0 | 76 | 2.8683 |
87
- | No log | 39.0 | 78 | 2.7825 |
88
- | No log | 40.0 | 80 | 2.9807 |
89
- | No log | 41.0 | 82 | 3.3741 |
90
- | No log | 42.0 | 84 | 2.9194 |
91
- | No log | 43.0 | 86 | 2.9351 |
92
- | No log | 44.0 | 88 | 2.4051 |
93
- | No log | 45.0 | 90 | 2.8278 |
94
- | No log | 46.0 | 92 | 3.2103 |
95
- | No log | 47.0 | 94 | 2.5392 |
96
- | No log | 48.0 | 96 | 2.7409 |
97
- | No log | 49.0 | 98 | 2.7521 |
98
- | No log | 50.0 | 100 | 3.0397 |
99
- | No log | 51.0 | 102 | 2.9377 |
100
- | No log | 52.0 | 104 | 2.7061 |
101
- | No log | 53.0 | 106 | 2.7856 |
102
- | No log | 54.0 | 108 | 2.6921 |
103
- | No log | 55.0 | 110 | 3.2429 |
104
- | No log | 56.0 | 112 | 2.7693 |
105
- | No log | 57.0 | 114 | 2.9776 |
106
- | No log | 58.0 | 116 | 2.4738 |
107
- | No log | 59.0 | 118 | 3.0559 |
108
- | No log | 60.0 | 120 | 2.5750 |
109
- | No log | 61.0 | 122 | 2.6638 |
110
- | No log | 62.0 | 124 | 2.5890 |
111
- | No log | 63.0 | 126 | 3.1511 |
112
- | No log | 64.0 | 128 | 2.5229 |
113
- | No log | 65.0 | 130 | 2.4948 |
114
- | No log | 66.0 | 132 | 2.7710 |
115
- | No log | 67.0 | 134 | 3.0031 |
116
- | No log | 68.0 | 136 | 2.8321 |
117
- | No log | 69.0 | 138 | 2.7744 |
118
- | No log | 70.0 | 140 | 2.9219 |
119
- | No log | 71.0 | 142 | 2.9745 |
120
- | No log | 72.0 | 144 | 3.0993 |
121
- | No log | 73.0 | 146 | 2.7376 |
122
- | No log | 74.0 | 148 | 2.7306 |
123
- | No log | 75.0 | 150 | 2.7114 |
124
- | No log | 76.0 | 152 | 2.6933 |
125
- | No log | 77.0 | 154 | 2.6704 |
126
- | No log | 78.0 | 156 | 2.8832 |
127
- | No log | 79.0 | 158 | 2.8868 |
128
- | No log | 80.0 | 160 | 3.0212 |
129
- | No log | 81.0 | 162 | 2.8588 |
130
- | No log | 82.0 | 164 | 2.7770 |
131
- | No log | 83.0 | 166 | 3.0724 |
132
- | No log | 84.0 | 168 | 2.9872 |
133
- | No log | 85.0 | 170 | 2.6315 |
134
- | No log | 86.0 | 172 | 2.6071 |
135
- | No log | 87.0 | 174 | 2.8929 |
136
- | No log | 88.0 | 176 | 2.6301 |
137
- | No log | 89.0 | 178 | 2.7766 |
138
- | No log | 90.0 | 180 | 2.7941 |
139
- | No log | 91.0 | 182 | 2.9732 |
140
- | No log | 92.0 | 184 | 3.3441 |
141
- | No log | 93.0 | 186 | 2.7296 |
142
- | No log | 94.0 | 188 | 2.9715 |
143
- | No log | 95.0 | 190 | 2.9928 |
144
- | No log | 96.0 | 192 | 2.8593 |
145
- | No log | 97.0 | 194 | 3.0503 |
146
- | No log | 98.0 | 196 | 2.8252 |
147
- | No log | 99.0 | 198 | 2.8479 |
148
- | No log | 100.0 | 200 | 3.0803 |
149
- | No log | 101.0 | 202 | 2.6038 |
150
- | No log | 102.0 | 204 | 2.8628 |
151
- | No log | 103.0 | 206 | 3.0348 |
152
- | No log | 104.0 | 208 | 2.9459 |
153
- | No log | 105.0 | 210 | 2.8926 |
154
- | No log | 106.0 | 212 | 2.9431 |
155
- | No log | 107.0 | 214 | 2.7569 |
156
- | No log | 108.0 | 216 | 2.7986 |
157
- | No log | 109.0 | 218 | 2.4914 |
158
- | No log | 110.0 | 220 | 2.7286 |
159
- | No log | 111.0 | 222 | 2.7306 |
160
- | No log | 112.0 | 224 | 2.8102 |
161
- | No log | 113.0 | 226 | 2.8561 |
162
- | No log | 114.0 | 228 | 2.8805 |
163
- | No log | 115.0 | 230 | 2.9698 |
164
- | No log | 116.0 | 232 | 3.2196 |
165
- | No log | 117.0 | 234 | 2.8678 |
166
- | No log | 118.0 | 236 | 2.7799 |
167
- | No log | 119.0 | 238 | 2.7113 |
168
- | No log | 120.0 | 240 | 2.9522 |
169
- | No log | 121.0 | 242 | 3.0367 |
170
- | No log | 122.0 | 244 | 2.8870 |
171
- | No log | 123.0 | 246 | 2.9976 |
172
- | No log | 124.0 | 248 | 3.2540 |
173
- | No log | 125.0 | 250 | 2.8957 |
174
- | No log | 126.0 | 252 | 2.7145 |
175
- | No log | 127.0 | 254 | 2.5635 |
176
- | No log | 128.0 | 256 | 2.8628 |
177
- | No log | 129.0 | 258 | 3.0154 |
178
- | No log | 130.0 | 260 | 2.8085 |
179
- | No log | 131.0 | 262 | 3.1380 |
180
- | No log | 132.0 | 264 | 2.9547 |
181
- | No log | 133.0 | 266 | 2.7659 |
182
- | No log | 134.0 | 268 | 2.7255 |
183
- | No log | 135.0 | 270 | 3.0261 |
184
- | No log | 136.0 | 272 | 2.6833 |
185
- | No log | 137.0 | 274 | 2.8733 |
186
- | No log | 138.0 | 276 | 3.0000 |
187
- | No log | 139.0 | 278 | 3.1210 |
188
- | No log | 140.0 | 280 | 2.9426 |
189
- | No log | 141.0 | 282 | 2.6732 |
190
- | No log | 142.0 | 284 | 2.4303 |
191
- | No log | 143.0 | 286 | 2.5880 |
192
- | No log | 144.0 | 288 | 2.8467 |
193
- | No log | 145.0 | 290 | 2.8371 |
194
- | No log | 146.0 | 292 | 2.6999 |
195
- | No log | 147.0 | 294 | 3.1099 |
196
- | No log | 148.0 | 296 | 2.7373 |
197
- | No log | 149.0 | 298 | 3.0492 |
198
- | No log | 150.0 | 300 | 3.1728 |
199
- | No log | 151.0 | 302 | 2.7651 |
200
- | No log | 152.0 | 304 | 2.8977 |
201
- | No log | 153.0 | 306 | 2.9967 |
202
- | No log | 154.0 | 308 | 3.1278 |
203
- | No log | 155.0 | 310 | 2.6165 |
204
- | No log | 156.0 | 312 | 2.8693 |
205
- | No log | 157.0 | 314 | 2.9361 |
206
- | No log | 158.0 | 316 | 3.1438 |
207
- | No log | 159.0 | 318 | 2.9013 |
208
- | No log | 160.0 | 320 | 2.7092 |
209
- | No log | 161.0 | 322 | 2.9289 |
210
- | No log | 162.0 | 324 | 2.9755 |
211
- | No log | 163.0 | 326 | 2.9121 |
212
- | No log | 164.0 | 328 | 2.7537 |
213
- | No log | 165.0 | 330 | 2.5412 |
214
- | No log | 166.0 | 332 | 2.5208 |
215
- | No log | 167.0 | 334 | 2.6742 |
216
- | No log | 168.0 | 336 | 2.6050 |
217
- | No log | 169.0 | 338 | 2.9525 |
218
- | No log | 170.0 | 340 | 2.8997 |
219
- | No log | 171.0 | 342 | 2.9220 |
220
- | No log | 172.0 | 344 | 2.8448 |
221
- | No log | 173.0 | 346 | 2.8170 |
222
- | No log | 174.0 | 348 | 2.6726 |
223
- | No log | 175.0 | 350 | 2.8006 |
224
- | No log | 176.0 | 352 | 2.4927 |
225
- | No log | 177.0 | 354 | 2.9843 |
226
- | No log | 178.0 | 356 | 2.9055 |
227
- | No log | 179.0 | 358 | 2.9204 |
228
- | No log | 180.0 | 360 | 2.7443 |
229
- | No log | 181.0 | 362 | 3.0418 |
230
- | No log | 182.0 | 364 | 2.9705 |
231
- | No log | 183.0 | 366 | 2.9550 |
232
- | No log | 184.0 | 368 | 2.8749 |
233
- | No log | 185.0 | 370 | 2.9289 |
234
- | No log | 186.0 | 372 | 2.8038 |
235
- | No log | 187.0 | 374 | 2.6857 |
236
- | No log | 188.0 | 376 | 3.1484 |
237
- | No log | 189.0 | 378 | 2.5875 |
238
- | No log | 190.0 | 380 | 2.9859 |
239
- | No log | 191.0 | 382 | 2.8266 |
240
- | No log | 192.0 | 384 | 2.7974 |
241
- | No log | 193.0 | 386 | 2.6254 |
242
- | No log | 194.0 | 388 | 2.6446 |
243
- | No log | 195.0 | 390 | 2.7015 |
244
- | No log | 196.0 | 392 | 2.9253 |
245
- | No log | 197.0 | 394 | 2.7782 |
246
- | No log | 198.0 | 396 | 2.6561 |
247
- | No log | 199.0 | 398 | 2.9984 |
248
- | No log | 200.0 | 400 | 2.8353 |
249
 
250
 
251
  ### Framework versions
 
15
 
16
  This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
17
  It achieves the following results on the evaluation set:
18
+ - Loss: 2.9755
19
 
20
  ## Model description
21
 
 
40
  - seed: 42
41
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
42
  - lr_scheduler_type: linear
43
+ - num_epochs: 50
44
 
45
  ### Training results
46
 
 
48
  |:-------------:|:-----:|:----:|:---------------:|
49
  | No log | 1.0 | 2 | 3.0764 |
50
  | No log | 2.0 | 4 | 2.9682 |
51
+ | No log | 3.0 | 6 | 2.9192 |
52
  | No log | 4.0 | 8 | 2.7053 |
53
+ | No log | 5.0 | 10 | 3.2641 |
54
+ | No log | 6.0 | 12 | 3.0686 |
55
+ | No log | 7.0 | 14 | 2.8964 |
56
+ | No log | 8.0 | 16 | 2.9286 |
57
+ | No log | 9.0 | 18 | 3.2297 |
58
+ | No log | 10.0 | 20 | 2.8838 |
59
+ | No log | 11.0 | 22 | 2.6667 |
60
+ | No log | 12.0 | 24 | 2.7971 |
61
+ | No log | 13.0 | 26 | 2.5930 |
62
+ | No log | 14.0 | 28 | 2.8104 |
63
+ | No log | 15.0 | 30 | 2.7695 |
64
+ | No log | 16.0 | 32 | 2.6098 |
65
+ | No log | 17.0 | 34 | 3.0375 |
66
+ | No log | 18.0 | 36 | 3.1342 |
67
+ | No log | 19.0 | 38 | 3.0716 |
68
+ | No log | 20.0 | 40 | 2.6655 |
69
+ | No log | 21.0 | 42 | 2.5342 |
70
+ | No log | 22.0 | 44 | 2.8461 |
71
+ | No log | 23.0 | 46 | 3.1634 |
72
+ | No log | 24.0 | 48 | 3.0142 |
73
+ | No log | 25.0 | 50 | 3.2181 |
74
+ | No log | 26.0 | 52 | 3.0536 |
75
+ | No log | 27.0 | 54 | 2.6519 |
76
+ | No log | 28.0 | 56 | 2.8154 |
77
+ | No log | 29.0 | 58 | 2.9385 |
78
+ | No log | 30.0 | 60 | 2.8281 |
79
+ | No log | 31.0 | 62 | 2.7531 |
80
+ | No log | 32.0 | 64 | 2.9408 |
81
+ | No log | 33.0 | 66 | 2.5850 |
82
+ | No log | 34.0 | 68 | 2.8823 |
83
+ | No log | 35.0 | 70 | 2.5177 |
84
+ | No log | 36.0 | 72 | 2.9296 |
85
+ | No log | 37.0 | 74 | 3.0441 |
86
+ | No log | 38.0 | 76 | 2.8714 |
87
+ | No log | 39.0 | 78 | 2.7857 |
88
+ | No log | 40.0 | 80 | 2.9850 |
89
+ | No log | 41.0 | 82 | 3.3792 |
90
+ | No log | 42.0 | 84 | 2.9246 |
91
+ | No log | 43.0 | 86 | 2.9392 |
92
+ | No log | 44.0 | 88 | 2.4090 |
93
+ | No log | 45.0 | 90 | 2.8323 |
94
+ | No log | 46.0 | 92 | 3.2173 |
95
+ | No log | 47.0 | 94 | 2.5451 |
96
+ | No log | 48.0 | 96 | 2.7456 |
97
+ | No log | 49.0 | 98 | 2.7570 |
98
+ | No log | 50.0 | 100 | 3.0471 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
99
 
100
 
101
  ### Framework versions
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