Update README.md
Browse files
README.md
CHANGED
|
@@ -11,6 +11,8 @@ metrics:
|
|
| 11 |
model-index:
|
| 12 |
- name: fine-tuned-distilbert-autofill
|
| 13 |
results: []
|
|
|
|
|
|
|
| 14 |
---
|
| 15 |
|
| 16 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
|
@@ -82,308 +84,18 @@ The following hyperparameters were used during training:
|
|
| 82 |
|
| 83 |
### Training results
|
| 84 |
|
| 85 |
-
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
|
| 86 |
-
|
| 87 |
-
| 0.902 | 1.0 | 598 | 0.2462 | 0.9202 | 0.9304 | 0.9208 |
|
| 88 |
-
|
| 89 |
-
|
| 90 |
-
|
| 91 |
-
|
| 92 |
-
|
| 93 |
-
|
| 94 |
-
|
| 95 |
-
|
| 96 |
-
|
| 97 |
-
[ 0 0 0 0 0 55 0 0 0 0 0 0 0 0
|
| 98 |
-
0]
|
| 99 |
-
[ 0 0 0 0 0 0 38 0 0 0 0 0 0 0
|
| 100 |
-
3]
|
| 101 |
-
[ 0 0 0 0 0 0 15 23 0 0 0 0 0 0
|
| 102 |
-
3]
|
| 103 |
-
[ 3 0 0 0 0 13 0 0 31 0 0 0 0 0
|
| 104 |
-
0]
|
| 105 |
-
[ 0 0 0 0 0 5 0 0 0 27 0 0 0 0
|
| 106 |
-
0]
|
| 107 |
-
[ 0 0 0 0 2 0 12 0 0 0 0 0 0 0
|
| 108 |
-
0]
|
| 109 |
-
[ 0 0 0 0 0 1 5 2 0 0 0 10 0 0
|
| 110 |
-
0]
|
| 111 |
-
[ 0 0 0 0 0 0 0 0 0 6 0 0 0 0
|
| 112 |
-
0]
|
| 113 |
-
[ 0 0 0 0 0 0 0 0 0 6 0 0 0 0
|
| 114 |
-
0]
|
| 115 |
-
[ 2 0 16 4 10 5 4 0 1 6 0 0 0 0
|
| 116 |
-
2452]] |
|
| 117 |
-
| 0.4122 | 2.0 | 1196 | 0.2071 | 0.9561 | 0.9456 | 0.9457 | [[ 100 0 0 0 0 0 0 0 0 0 0 0 0 0
|
| 118 |
-
0]
|
| 119 |
-
[ 36 22 0 0 0 0 0 0 0 0 0 0 0 0
|
| 120 |
-
0]
|
| 121 |
-
[ 0 0 52 0 0 0 0 0 0 0 0 0 0 0
|
| 122 |
-
8]
|
| 123 |
-
[ 0 0 0 33 0 0 0 0 2 0 0 0 0 0
|
| 124 |
-
0]
|
| 125 |
-
[ 0 0 1 0 66 0 0 0 0 0 2 0 0 0
|
| 126 |
-
7]
|
| 127 |
-
[ 0 0 0 1 0 53 0 0 1 0 0 0 0 0
|
| 128 |
-
0]
|
| 129 |
-
[ 0 0 0 0 0 0 39 0 0 0 0 0 0 0
|
| 130 |
-
2]
|
| 131 |
-
[ 0 0 0 0 0 0 6 33 0 0 0 0 0 0
|
| 132 |
-
2]
|
| 133 |
-
[ 1 0 0 0 0 1 0 0 45 0 0 0 0 0
|
| 134 |
-
0]
|
| 135 |
-
[ 0 0 0 1 0 0 0 0 0 30 0 0 0 1
|
| 136 |
-
0]
|
| 137 |
-
[ 0 0 0 0 2 0 6 0 0 0 6 0 0 0
|
| 138 |
-
0]
|
| 139 |
-
[ 0 0 0 0 0 0 5 0 0 0 0 13 0 0
|
| 140 |
-
0]
|
| 141 |
-
[ 0 0 0 0 0 0 0 0 0 3 0 0 3 0
|
| 142 |
-
0]
|
| 143 |
-
[ 0 0 0 0 0 0 0 0 0 2 0 0 1 3
|
| 144 |
-
0]
|
| 145 |
-
[ 3 0 30 7 14 1 8 0 7 4 0 0 2 1
|
| 146 |
-
2423]] |
|
| 147 |
-
| 0.2816 | 3.0 | 1794 | 0.2004 | 0.9566 | 0.9508 | 0.9518 | [[ 97 3 0 0 0 0 0 0 0 0 0 0 0 0
|
| 148 |
-
0]
|
| 149 |
-
[ 25 33 0 0 0 0 0 0 0 0 0 0 0 0
|
| 150 |
-
0]
|
| 151 |
-
[ 0 0 50 0 0 0 0 0 0 0 0 0 0 0
|
| 152 |
-
10]
|
| 153 |
-
[ 0 0 0 32 0 0 0 0 2 1 0 0 0 0
|
| 154 |
-
0]
|
| 155 |
-
[ 0 0 0 0 67 0 0 0 0 0 0 0 0 0
|
| 156 |
-
9]
|
| 157 |
-
[ 0 0 0 0 0 53 0 0 1 1 0 0 0 0
|
| 158 |
-
0]
|
| 159 |
-
[ 0 0 0 0 0 0 35 1 0 0 3 0 0 0
|
| 160 |
-
2]
|
| 161 |
-
[ 0 0 0 0 0 0 3 34 0 0 2 0 0 0
|
| 162 |
-
2]
|
| 163 |
-
[ 1 0 0 0 0 1 0 0 43 2 0 0 0 0
|
| 164 |
-
0]
|
| 165 |
-
[ 0 0 0 1 0 0 0 0 0 31 0 0 0 0
|
| 166 |
-
0]
|
| 167 |
-
[ 0 0 0 0 2 0 0 0 0 0 12 0 0 0
|
| 168 |
-
0]
|
| 169 |
-
[ 0 0 0 0 0 0 0 2 0 0 1 15 0 0
|
| 170 |
-
0]
|
| 171 |
-
[ 0 0 0 0 0 0 0 0 0 0 0 0 5 0
|
| 172 |
-
1]
|
| 173 |
-
[ 0 0 0 0 0 0 0 0 0 1 0 0 0 5
|
| 174 |
-
0]
|
| 175 |
-
[ 4 0 23 6 18 1 6 0 7 3 3 0 3 1
|
| 176 |
-
2425]] |
|
| 177 |
-
| 0.2272 | 4.0 | 2392 | 0.1930 | 0.9651 | 0.9595 | 0.9606 | [[ 99 1 0 0 0 0 0 0 0 0 0 0 0 0
|
| 178 |
-
0]
|
| 179 |
-
[ 19 39 0 0 0 0 0 0 0 0 0 0 0 0
|
| 180 |
-
0]
|
| 181 |
-
[ 0 0 51 0 0 0 0 0 0 0 0 0 0 0
|
| 182 |
-
9]
|
| 183 |
-
[ 0 0 0 34 0 0 0 0 1 0 0 0 0 0
|
| 184 |
-
0]
|
| 185 |
-
[ 0 0 1 0 64 0 0 0 0 0 0 0 0 0
|
| 186 |
-
11]
|
| 187 |
-
[ 0 0 0 1 0 53 0 0 1 0 0 0 0 0
|
| 188 |
-
0]
|
| 189 |
-
[ 0 0 0 0 0 0 35 3 0 0 0 0 0 0
|
| 190 |
-
3]
|
| 191 |
-
[ 0 0 0 0 0 0 0 39 0 0 0 0 0 0
|
| 192 |
-
2]
|
| 193 |
-
[ 0 0 0 1 0 0 0 0 46 0 0 0 0 0
|
| 194 |
-
0]
|
| 195 |
-
[ 0 0 0 1 0 0 0 0 1 30 0 0 0 0
|
| 196 |
-
0]
|
| 197 |
-
[ 0 0 0 0 2 0 0 3 0 0 9 0 0 0
|
| 198 |
-
0]
|
| 199 |
-
[ 0 0 0 0 0 0 0 0 0 0 1 17 0 0
|
| 200 |
-
0]
|
| 201 |
-
[ 0 0 0 1 0 0 0 0 0 0 0 0 5 0
|
| 202 |
-
0]
|
| 203 |
-
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 6
|
| 204 |
-
0]
|
| 205 |
-
[ 2 0 33 7 6 1 1 2 6 0 1 0 2 2
|
| 206 |
-
2437]] |
|
| 207 |
-
| 0.1854 | 5.0 | 2990 | 0.2301 | 0.9593 | 0.9501 | 0.9529 | [[ 95 5 0 0 0 0 0 0 0 0 0 0 0 0
|
| 208 |
-
0]
|
| 209 |
-
[ 17 41 0 0 0 0 0 0 0 0 0 0 0 0
|
| 210 |
-
0]
|
| 211 |
-
[ 0 0 51 0 0 0 0 0 0 0 0 0 0 0
|
| 212 |
-
9]
|
| 213 |
-
[ 0 0 0 35 0 0 0 0 0 0 0 0 0 0
|
| 214 |
-
0]
|
| 215 |
-
[ 0 0 2 0 68 0 0 0 0 0 0 0 0 0
|
| 216 |
-
6]
|
| 217 |
-
[ 0 0 0 1 0 54 0 0 0 0 0 0 0 0
|
| 218 |
-
0]
|
| 219 |
-
[ 0 0 0 0 0 0 35 0 0 0 3 0 0 0
|
| 220 |
-
3]
|
| 221 |
-
[ 0 0 0 0 0 0 0 34 0 0 5 0 0 0
|
| 222 |
-
2]
|
| 223 |
-
[ 0 0 0 2 0 1 0 0 44 0 0 0 0 0
|
| 224 |
-
0]
|
| 225 |
-
[ 0 0 0 0 0 1 0 0 0 31 0 0 0 0
|
| 226 |
-
0]
|
| 227 |
-
[ 0 0 0 0 2 0 0 0 0 0 12 0 0 0
|
| 228 |
-
0]
|
| 229 |
-
[ 0 0 0 0 0 0 0 2 0 0 1 15 0 0
|
| 230 |
-
0]
|
| 231 |
-
[ 0 0 0 1 0 0 0 0 0 0 0 0 5 0
|
| 232 |
-
0]
|
| 233 |
-
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 6
|
| 234 |
-
0]
|
| 235 |
-
[ 5 0 40 9 19 2 3 1 4 1 3 0 3 1
|
| 236 |
-
2409]] |
|
| 237 |
-
| 0.1064 | 6.0 | 3588 | 0.2330 | 0.9593 | 0.9518 | 0.9539 | [[ 97 3 0 0 0 0 0 0 0 0 0 0 0 0
|
| 238 |
-
0]
|
| 239 |
-
[ 18 40 0 0 0 0 0 0 0 0 0 0 0 0
|
| 240 |
-
0]
|
| 241 |
-
[ 0 0 50 0 0 0 0 0 0 0 0 0 0 0
|
| 242 |
-
10]
|
| 243 |
-
[ 0 0 0 32 0 0 0 0 2 1 0 0 0 0
|
| 244 |
-
0]
|
| 245 |
-
[ 0 0 1 0 67 0 0 0 0 0 0 0 0 0
|
| 246 |
-
8]
|
| 247 |
-
[ 0 0 0 0 0 52 0 0 0 2 0 0 0 0
|
| 248 |
-
1]
|
| 249 |
-
[ 0 0 0 0 0 0 37 1 0 0 0 0 0 0
|
| 250 |
-
3]
|
| 251 |
-
[ 0 0 0 0 0 0 4 35 0 0 0 0 0 0
|
| 252 |
-
2]
|
| 253 |
-
[ 0 0 0 0 0 1 0 0 44 2 0 0 0 0
|
| 254 |
-
0]
|
| 255 |
-
[ 0 0 0 0 0 0 0 0 0 32 0 0 0 0
|
| 256 |
-
0]
|
| 257 |
-
[ 0 0 0 0 2 0 0 2 0 0 10 0 0 0
|
| 258 |
-
0]
|
| 259 |
-
[ 0 0 0 0 0 0 0 1 0 0 1 16 0 0
|
| 260 |
-
0]
|
| 261 |
-
[ 0 0 0 0 0 0 0 0 0 0 0 0 6 0
|
| 262 |
-
0]
|
| 263 |
-
[ 0 0 0 0 0 0 0 0 0 1 0 0 0 5
|
| 264 |
-
0]
|
| 265 |
-
[ 3 0 43 3 16 0 4 0 5 2 2 0 4 1
|
| 266 |
-
2417]] |
|
| 267 |
-
| 0.0756 | 7.0 | 4186 | 0.2207 | 0.9641 | 0.9592 | 0.9606 | [[ 95 5 0 0 0 0 0 0 0 0 0 0 0 0
|
| 268 |
-
0]
|
| 269 |
-
[ 16 42 0 0 0 0 0 0 0 0 0 0 0 0
|
| 270 |
-
0]
|
| 271 |
-
[ 0 0 51 0 0 0 0 0 0 0 0 0 0 0
|
| 272 |
-
9]
|
| 273 |
-
[ 0 0 0 32 0 0 0 0 2 1 0 0 0 0
|
| 274 |
-
0]
|
| 275 |
-
[ 0 0 1 0 66 0 0 0 0 0 0 0 0 0
|
| 276 |
-
9]
|
| 277 |
-
[ 0 0 0 0 0 53 0 0 1 1 0 0 0 0
|
| 278 |
-
0]
|
| 279 |
-
[ 0 0 0 0 0 0 35 2 0 0 0 1 0 0
|
| 280 |
-
3]
|
| 281 |
-
[ 0 0 0 0 0 0 2 36 0 0 0 1 0 0
|
| 282 |
-
2]
|
| 283 |
-
[ 0 0 0 0 0 0 0 0 47 0 0 0 0 0
|
| 284 |
-
0]
|
| 285 |
-
[ 0 0 0 0 0 0 0 0 0 32 0 0 0 0
|
| 286 |
-
0]
|
| 287 |
-
[ 0 0 0 0 0 0 2 0 0 0 12 0 0 0
|
| 288 |
-
0]
|
| 289 |
-
[ 0 0 0 0 0 0 0 1 0 0 1 16 0 0
|
| 290 |
-
0]
|
| 291 |
-
[ 0 0 0 0 0 0 0 0 0 0 0 0 6 0
|
| 292 |
-
0]
|
| 293 |
-
[ 0 0 0 0 0 0 0 0 0 1 0 0 0 5
|
| 294 |
-
0]
|
| 295 |
-
[ 3 0 35 3 7 0 2 1 7 0 2 0 3 2
|
| 296 |
-
2435]] |
|
| 297 |
-
| 0.0815 | 8.0 | 4784 | 0.2203 | 0.9651 | 0.9615 | 0.9621 | [[ 99 1 0 0 0 0 0 0 0 0 0 0 0 0
|
| 298 |
-
0]
|
| 299 |
-
[ 19 39 0 0 0 0 0 0 0 0 0 0 0 0
|
| 300 |
-
0]
|
| 301 |
-
[ 0 0 50 0 0 0 0 0 0 0 0 0 0 0
|
| 302 |
-
10]
|
| 303 |
-
[ 0 0 0 33 0 0 0 0 1 1 0 0 0 0
|
| 304 |
-
0]
|
| 305 |
-
[ 0 0 1 0 63 0 0 0 0 0 0 0 0 0
|
| 306 |
-
12]
|
| 307 |
-
[ 0 0 0 0 0 53 0 0 1 1 0 0 0 0
|
| 308 |
-
0]
|
| 309 |
-
[ 0 0 0 0 0 0 36 1 0 0 0 0 0 0
|
| 310 |
-
4]
|
| 311 |
-
[ 0 0 0 0 0 0 3 35 0 0 0 0 0 0
|
| 312 |
-
3]
|
| 313 |
-
[ 0 0 0 0 0 1 0 0 45 0 0 1 0 0
|
| 314 |
-
0]
|
| 315 |
-
[ 0 0 0 0 0 0 0 0 0 31 0 0 0 0
|
| 316 |
-
1]
|
| 317 |
-
[ 0 0 0 0 2 0 0 2 0 0 10 0 0 0
|
| 318 |
-
0]
|
| 319 |
-
[ 0 0 0 0 0 0 0 1 0 0 1 16 0 0
|
| 320 |
-
0]
|
| 321 |
-
[ 0 0 0 0 0 0 0 0 0 0 0 0 6 0
|
| 322 |
-
0]
|
| 323 |
-
[ 0 0 0 0 0 0 0 0 0 1 0 0 0 5
|
| 324 |
-
0]
|
| 325 |
-
[ 2 0 31 2 7 0 2 0 3 0 1 0 2 1
|
| 326 |
-
2449]] |
|
| 327 |
-
| 0.0677 | 9.0 | 5382 | 0.2214 | 0.9636 | 0.9586 | 0.9602 | [[ 93 6 0 0 0 0 0 0 0 0 0 0 0 0
|
| 328 |
-
1]
|
| 329 |
-
[ 12 46 0 0 0 0 0 0 0 0 0 0 0 0
|
| 330 |
-
0]
|
| 331 |
-
[ 0 0 51 0 0 0 0 0 0 0 0 0 0 0
|
| 332 |
-
9]
|
| 333 |
-
[ 0 0 0 33 0 0 0 0 1 1 0 0 0 0
|
| 334 |
-
0]
|
| 335 |
-
[ 0 0 1 0 66 0 0 0 0 0 0 0 0 0
|
| 336 |
-
9]
|
| 337 |
-
[ 0 0 0 0 0 53 0 0 1 1 0 0 0 0
|
| 338 |
-
0]
|
| 339 |
-
[ 0 0 0 0 0 0 37 1 0 0 0 0 0 0
|
| 340 |
-
3]
|
| 341 |
-
[ 0 0 0 0 0 0 4 35 0 0 0 0 0 0
|
| 342 |
-
2]
|
| 343 |
-
[ 0 0 0 0 0 1 0 0 46 0 0 0 0 0
|
| 344 |
-
0]
|
| 345 |
-
[ 0 0 0 0 0 0 0 0 0 32 0 0 0 0
|
| 346 |
-
0]
|
| 347 |
-
[ 0 0 0 0 0 0 0 2 0 0 12 0 0 0
|
| 348 |
-
0]
|
| 349 |
-
[ 0 0 0 0 0 0 0 1 0 0 1 16 0 0
|
| 350 |
-
0]
|
| 351 |
-
[ 0 0 0 0 0 0 0 0 0 1 0 0 5 0
|
| 352 |
-
0]
|
| 353 |
-
[ 0 0 0 0 0 0 0 0 0 1 0 0 0 5
|
| 354 |
-
0]
|
| 355 |
-
[ 2 0 37 3 10 0 4 0 7 0 2 0 3 1
|
| 356 |
-
2431]] |
|
| 357 |
-
| 0.0579 | 10.0 | 5980 | 0.2261 | 0.9637 | 0.9582 | 0.9601 | [[ 93 7 0 0 0 0 0 0 0 0 0 0 0 0
|
| 358 |
-
0]
|
| 359 |
-
[ 9 49 0 0 0 0 0 0 0 0 0 0 0 0
|
| 360 |
-
0]
|
| 361 |
-
[ 0 0 51 0 0 0 0 0 0 0 0 0 0 0
|
| 362 |
-
9]
|
| 363 |
-
[ 0 0 0 33 0 0 0 0 1 1 0 0 0 0
|
| 364 |
-
0]
|
| 365 |
-
[ 0 0 1 0 66 0 0 0 0 0 0 0 0 0
|
| 366 |
-
9]
|
| 367 |
-
[ 0 0 0 0 0 53 0 0 1 1 0 0 0 0
|
| 368 |
-
0]
|
| 369 |
-
[ 0 0 0 0 0 0 37 1 0 0 0 0 0 0
|
| 370 |
-
3]
|
| 371 |
-
[ 0 0 0 0 0 0 4 35 0 0 0 0 0 0
|
| 372 |
-
2]
|
| 373 |
-
[ 0 0 0 0 0 1 0 0 46 0 0 0 0 0
|
| 374 |
-
0]
|
| 375 |
-
[ 0 0 0 0 0 0 0 0 0 32 0 0 0 0
|
| 376 |
-
0]
|
| 377 |
-
[ 0 0 0 0 0 0 0 2 0 0 12 0 0 0
|
| 378 |
-
0]
|
| 379 |
-
[ 0 0 0 0 0 0 0 1 0 0 1 16 0 0
|
| 380 |
-
0]
|
| 381 |
-
[ 0 0 0 0 0 0 0 0 0 1 0 0 5 0
|
| 382 |
-
0]
|
| 383 |
-
[ 0 0 0 0 0 0 0 0 0 1 0 0 0 5
|
| 384 |
-
0]
|
| 385 |
-
[ 2 0 39 3 12 0 4 0 6 1 2 0 3 1
|
| 386 |
-
2427]] |
|
| 387 |
|
| 388 |
|
| 389 |
### Framework versions
|
|
@@ -391,4 +103,4 @@ The following hyperparameters were used during training:
|
|
| 391 |
- Transformers 4.44.2
|
| 392 |
- Pytorch 2.1.2
|
| 393 |
- Datasets 2.19.2
|
| 394 |
-
- Tokenizers 0.19.1
|
|
|
|
| 11 |
model-index:
|
| 12 |
- name: fine-tuned-distilbert-autofill
|
| 13 |
results: []
|
| 14 |
+
datasets:
|
| 15 |
+
- vazish/autofill_15_labels
|
| 16 |
---
|
| 17 |
|
| 18 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
|
|
|
| 84 |
|
| 85 |
### Training results
|
| 86 |
|
| 87 |
+
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
|
| 88 |
+
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
|
| 89 |
+
| 0.902 | 1.0 | 598 | 0.2462 | 0.9202 | 0.9304 | 0.9208 |
|
| 90 |
+
| 0.4122 | 2.0 | 1196 | 0.2071 | 0.9561 | 0.9456 | 0.9457 |
|
| 91 |
+
| 0.2816 | 3.0 | 1794 | 0.2004 | 0.9566 | 0.9508 | 0.9518 |
|
| 92 |
+
| 0.2272 | 4.0 | 2392 | 0.1930 | 0.9651 | 0.9595 | 0.9606 |
|
| 93 |
+
| 0.1854 | 5.0 | 2990 | 0.2301 | 0.9593 | 0.9501 | 0.9529 |
|
| 94 |
+
| 0.1064 | 6.0 | 3588 | 0.2330 | 0.9593 | 0.9518 | 0.9539 |
|
| 95 |
+
| 0.0756 | 7.0 | 4186 | 0.2207 | 0.9641 | 0.9592 | 0.9606 |
|
| 96 |
+
| 0.0815 | 8.0 | 4784 | 0.2203 | 0.9651 | 0.9615 | 0.9621 |
|
| 97 |
+
| 0.0677 | 9.0 | 5382 | 0.2214 | 0.9636 | 0.9586 | 0.9602 |
|
| 98 |
+
| 0.0579 | 10.0 | 5980 | 0.2261 | 0.9637 | 0.9582 | 0.9601 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 99 |
|
| 100 |
|
| 101 |
### Framework versions
|
|
|
|
| 103 |
- Transformers 4.44.2
|
| 104 |
- Pytorch 2.1.2
|
| 105 |
- Datasets 2.19.2
|
| 106 |
+
- Tokenizers 0.19.1
|