| Below is a question that describes a data request, paired with an input that describes a SQL table. Write a SQL query that retrieves the data. | |
| ### Question: How many games does novica veličković have when there's more than 24 rebounds? | |
| ### Input: Table 2-16050349-8 has columns Rank (real),Name (text),Team (text),Games (real),Rebounds (real). | |
| ### Answer: SELECT COUNT Games FROM 2-16050349-8 WHERE Name = 'novica veličković' AND Rebounds > 24 | |
| Below is a question that describes a data request, paired with an input that describes a SQL table. Write a SQL query that retrieves the data. | |
| ### Question: What is the number of capacity at somerset park? | |
| ### Input: Table 1-11206787-5 has columns Team (text),Stadium (text),Capacity (real),Highest (real),Lowest (real),Average (real). | |
| ### Answer: SELECT COUNT Capacity FROM 1-11206787-5 WHERE Stadium = 'Somerset Park' | |
| Below is a question that describes a data request, paired with an input that describes a SQL table. Write a SQL query that retrieves the data. | |
| ### Question: What is the number & name with an Undergoing overhaul, restoration or repairs date? | |
| ### Input: Table 2-11913905-6 has columns Number & Name (text),Description (text),Livery (text),Owner(s) (text),Date (text). | |
| ### Answer: SELECT Number & Name FROM 2-11913905-6 WHERE Date = 'undergoing overhaul, restoration or repairs' | |
| Below is a question that describes a data request, paired with an input that describes a SQL table. Write a SQL query that retrieves the data. | |
| ### Question: What year did Orlando have a School/Club team in Clemson? | |
| ### Input: Table 2-15621965-7 has columns Player (text),Nationality (text),Position (text),Years in Orlando (text),School/Club Team (text). | |
| ### Answer: SELECT Years in Orlando FROM 2-15621965-7 WHERE School/Club Team = 'clemson' | |
| Below is a question that describes a data request, paired with an input that describes a SQL table. Write a SQL query that retrieves the data. | |
| ### Question: How many Deaths have a Fate of damaged, and a Tonnage (GRT) smaller than 4,917? | |
| ### Input: Table 2-18914307-1 has columns Date (text),Ship Name (text),Flag (text),Tonnage ( GRT ) (real),Fate (text),Deaths (real). | |
| ### Answer: SELECT COUNT Deaths FROM 2-18914307-1 WHERE Fate = 'damaged' AND Tonnage ( GRT ) < 4,917 | |
| {'phase': 1, 'table_id': '1-1000181-1', 'question': 'Tell me what the notes are for South Australia ', 'sql': {'sel': 5, 'conds': [[3, 0, 'SOUTH AUSTRALIA']], 'agg': 0}} | |
| 1-1000181-1 | |
| ['State/territory', 'Text/background colour', 'Format', 'Current slogan', 'Current series', 'Notes'] | |
| {'id': '1-1000181-1', 'header': ['State/territory', 'Text/background colour', 'Format', 'Current slogan', 'Current series', 'Notes'], 'types': ['text', 'text', 'text', 'text', 'text', 'text'], 'rows': [['Australian Capital Territory', 'blue/white', 'Yaa·nna', 'ACT · CELEBRATION OF A CENTURY 2013', 'YIL·00A', 'Slogan screenprinted on plate'], ['New South Wales', 'black/yellow', 'aa·nn·aa', 'NEW SOUTH WALES', 'BX·99·HI', 'No slogan on current series'], ['New South Wales', 'black/white', 'aaa·nna', 'NSW', 'CPX·12A', 'Optional white slimline series'], ['Northern Territory', 'ochre/white', 'Ca·nn·aa', 'NT · OUTBACK AUSTRALIA', 'CB·06·ZZ', 'New series began in June 2011'], ['Queensland', 'maroon/white', 'nnn·aaa', 'QUEENSLAND · SUNSHINE STATE', '999·TLG', 'Slogan embossed on plate'], ['South Australia', 'black/white', 'Snnn·aaa', 'SOUTH AUSTRALIA', 'S000·AZD', 'No slogan on current series'], ['Victoria', 'blue/white', 'aaa·nnn', 'VICTORIA - THE PLACE TO BE', 'ZZZ·562', 'Current series will be exhausted this year']], 'name': 'table_1000181_1'} | |
| SELECT col5 FROM table WHERE col3 = SOUTH AUSTRALIA | |
| SELECT Notes FROM table WHERE Current slogan = SOUTH AUSTRALIA | |
| fatal: destination path 'WikiSQL' already exists and is not an empty directory. | |
| data/ | |
| data/train.jsonl | |
| data/test.tables.jsonl | |
| data/test.db | |
| data/dev.tables.jsonl | |
| data/dev.db | |
| data/test.jsonl | |
| data/train.tables.jsonl | |
| data/train.db | |
| data/dev.jsonl | |
| /home/matt/hf/sqllama-V0/.venv/lib/python3.7/site-packages/transformers/generation/utils.py:1220: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use a generation configuration file (see https://huggingface.co/docs/transformers/main_classes/text_generation) | |
| "You have modified the pretrained model configuration to control generation. This is a" | |
| ⁇ hey dude, talk to me. | |
| I'm a 20 year old guy from the UK. I'm a bit of a nerd, I like to read, I like to write, I like to play video games, I like to watch movies, I like to listen | |
| /home/matt/hf/sqllama-V0/.venv/lib/python3.7/site-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: /opt/conda did not contain libcudart.so as expected! Searching further paths... | |
| warn(msg) | |
| The tokenizer class you load from this checkpoint is not the same type as the class this function is called from. It may result in unexpected tokenization. | |
| The tokenizer class you load from this checkpoint is 'LLaMATokenizer'. | |
| The class this function is called from is 'LlamaTokenizer'. | |
| ===================================BUG REPORT=================================== | |
| Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues | |
| ================================================================================ | |
| CUDA SETUP: CUDA runtime path found: /usr/local/cuda/lib64/libcudart.so | |
| CUDA SETUP: Highest compute capability among GPUs detected: 7.5 | |
| CUDA SETUP: Detected CUDA version 113 | |
| CUDA SETUP: Loading binary /home/matt/hf/sqllama-V0/.venv/lib/python3.7/site-packages/bitsandbytes/libbitsandbytes_cuda113.so... | |
| True | |
| [440/440 11:19:07, Epoch 0/1] | |
| Step Training Loss | |
| 1 2.517200 | |
| 2 2.482300 | |
| 3 2.444100 | |
| 4 2.456500 | |
| 5 2.441400 | |
| 6 2.484600 | |
| 7 2.424000 | |
| 8 2.477900 | |
| 9 2.429700 | |
| 10 2.436000 | |
| 11 2.422000 | |
| 12 2.408800 | |
| 13 2.402900 | |
| 14 2.424500 | |
| 15 2.421800 | |
| 16 2.424100 | |
| 17 2.404000 | |
| 18 2.386900 | |
| 19 2.414400 | |
| 20 2.370600 | |
| 21 2.382500 | |
| 22 2.350700 | |
| 23 2.385700 | |
| 24 2.350400 | |
| 25 2.354900 | |
| 26 2.345400 | |
| 27 2.373000 | |
| 28 2.343200 | |
| 29 2.374300 | |
| 30 2.325000 | |
| 31 2.352000 | |
| 32 2.344600 | |
| 33 2.360000 | |
| 34 2.347400 | |
| 35 2.346700 | |
| 36 2.329000 | |
| 37 2.314600 | |
| 38 2.306000 | |
| 39 2.292600 | |
| 40 2.333800 | |
| 41 2.311500 | |
| 42 2.308300 | |
| 43 2.287400 | |
| 44 2.314100 | |
| 45 2.280400 | |
| 46 2.261300 | |
| 47 2.274200 | |
| 48 2.246900 | |
| 49 2.257100 | |
| 50 2.274500 | |
| 51 2.245500 | |
| 52 2.250700 | |
| 53 2.296600 | |
| 54 2.261000 | |
| 55 2.223800 | |
| 56 2.244000 | |
| 57 2.228500 | |
| 58 2.229100 | |
| 59 2.162300 | |
| 60 2.238000 | |
| 61 2.246000 | |
| 62 2.184800 | |
| 63 2.195000 | |
| 64 2.199500 | |
| 65 2.180000 | |
| 66 2.179800 | |
| 67 2.149700 | |
| 68 2.177000 | |
| 69 2.156600 | |
| 70 2.193400 | |
| 71 2.163400 | |
| 72 2.147400 | |
| 73 2.134700 | |
| 74 2.133200 | |
| 75 2.118000 | |
| 76 2.139000 | |
| 77 2.102000 | |
| 78 2.109100 | |
| 79 2.099000 | |
| 80 2.097500 | |
| 81 2.073200 | |
| 82 2.055200 | |
| 83 2.078100 | |
| 84 2.104800 | |
| 85 2.061100 | |
| 86 2.066500 | |
| 87 2.073500 | |
| 88 2.010500 | |
| 89 2.045700 | |
| 90 2.026700 | |
| 91 2.046500 | |
| 92 2.015300 | |
| 93 2.019100 | |
| 94 2.008600 | |
| 95 1.961000 | |
| 96 1.974300 | |
| 97 1.991700 | |
| 98 1.984700 | |
| 99 1.975900 | |
| 100 1.963900 | |
| 101 1.934300 | |
| 102 1.990400 | |
| 103 1.914900 | |
| 104 1.956100 | |
| 105 1.943400 | |
| 106 1.931000 | |
| 107 1.919000 | |
| 108 1.912800 | |
| 109 1.920400 | |
| 110 1.878300 | |
| 111 1.890800 | |
| 112 1.881900 | |
| 113 1.885400 | |
| 114 1.908400 | |
| 115 1.871200 | |
| 116 1.900000 | |
| 117 1.888000 | |
| 118 1.875100 | |
| 119 1.855000 | |
| 120 1.852100 | |
| 121 1.851200 | |
| 122 1.821800 | |
| 123 1.853000 | |
| 124 1.854700 | |
| 125 1.806900 | |
| 126 1.845300 | |
| 127 1.797800 | |
| 128 1.795300 | |
| 129 1.799500 | |
| 130 1.853900 | |
| 131 1.780100 | |
| 132 1.789400 | |
| 133 1.776700 | |
| 134 1.747300 | |
| 135 1.753700 | |
| 136 1.761300 | |
| 137 1.725500 | |
| 138 1.710800 | |
| 139 1.733500 | |
| 140 1.727000 | |
| 141 1.744300 | |
| 142 1.728900 | |
| 143 1.725100 | |
| 144 1.708000 | |
| 145 1.709000 | |
| 146 1.704600 | |
| 147 1.684600 | |
| 148 1.676100 | |
| 149 1.682800 | |
| 150 1.669900 | |
| 151 1.636400 | |
| 152 1.671500 | |
| 153 1.673200 | |
| 154 1.644300 | |
| 155 1.620800 | |
| 156 1.617500 | |
| 157 1.647700 | |
| 158 1.629300 | |
| 159 1.608800 | |
| 160 1.633000 | |
| 161 1.618200 | |
| 162 1.634300 | |
| 163 1.588400 | |
| 164 1.581100 | |
| 165 1.584500 | |
| 166 1.594800 | |
| 167 1.563800 | |
| 168 1.576900 | |
| 169 1.546300 | |
| 170 1.569800 | |
| 171 1.592300 | |
| 172 1.537800 | |
| 173 1.519200 | |
| 174 1.512100 | |
| 175 1.581500 | |
| 176 1.534500 | |
| 177 1.509400 | |
| 178 1.521300 | |
| 179 1.528500 | |
| 180 1.494300 | |
| 181 1.495000 | |
| 182 1.499700 | |
| 183 1.461300 | |
| 184 1.469200 | |
| 185 1.495200 | |
| 186 1.467400 | |
| 187 1.437000 | |
| 188 1.463000 | |
| 189 1.437900 | |
| 190 1.467400 | |
| 191 1.472300 | |
| 192 1.434000 | |
| 193 1.411500 | |
| 194 1.432500 | |
| 195 1.459800 | |
| 196 1.431900 | |
| 197 1.456200 | |
| 198 1.394800 | |
| 199 1.422700 | |
| 200 1.412800 | |
| 201 1.413800 | |
| 202 1.380000 | |
| 203 1.407400 | |
| 204 1.406200 | |
| 205 1.396100 | |
| 206 1.407100 | |
| 207 1.379600 | |
| 208 1.360600 | |
| 209 1.395100 | |
| 210 1.352500 | |
| 211 1.358900 | |
| 212 1.369100 | |
| 213 1.342600 | |
| 214 1.358900 | |
| 215 1.320300 | |
| 216 1.355700 | |
| 217 1.315700 | |
| 218 1.348800 | |
| 219 1.319800 | |
| 220 1.336500 | |
| 221 1.339600 | |
| 222 1.319500 | |
| 223 1.319600 | |
| 224 1.330200 | |
| 225 1.271700 | |
| 226 1.317300 | |
| 227 1.287400 | |
| 228 1.283300 | |
| 229 1.280500 | |
| 230 1.274200 | |
| 231 1.297000 | |
| 232 1.266400 | |
| 233 1.253100 | |
| 234 1.273100 | |
| 235 1.293300 | |
| 236 1.293000 | |
| 237 1.273500 | |
| 238 1.253100 | |
| 239 1.257700 | |
| 240 1.232500 | |
| 241 1.233100 | |
| 242 1.226000 | |
| 243 1.218400 | |
| 244 1.222800 | |
| 245 1.232100 | |
| 246 1.214800 | |
| 247 1.205700 | |
| 248 1.228400 | |
| 249 1.202600 | |
| 250 1.207700 | |
| 251 1.205800 | |
| 252 1.198400 | |
| 253 1.207800 | |
| 254 1.198600 | |
| 255 1.201700 | |
| 256 1.195500 | |
| 257 1.190500 | |
| 258 1.197100 | |
| 259 1.165100 | |
| 260 1.173200 | |
| 261 1.163400 | |
| 262 1.191500 | |
| 263 1.173700 | |
| 264 1.134400 | |
| 265 1.165500 | |
| 266 1.134800 | |
| 267 1.149500 | |
| 268 1.173100 | |
| 269 1.137000 | |
| 270 1.171200 | |
| 271 1.120600 | |
| 272 1.147600 | |
| 273 1.128300 | |
| 274 1.150300 | |
| 275 1.147700 | |
| 276 1.150200 | |
| 277 1.106900 | |
| 278 1.145400 | |
| 279 1.117300 | |
| 280 1.121900 | |
| 281 1.139400 | |
| 282 1.109100 | |
| 283 1.142100 | |
| 284 1.117300 | |
| 285 1.104200 | |
| 286 1.134200 | |
| 287 1.100400 | |
| 288 1.092100 | |
| 289 1.120500 | |
| 290 1.088100 | |
| 291 1.128600 | |
| 292 1.105400 | |
| 293 1.094000 | |
| 294 1.108900 | |
| 295 1.073100 | |
| 296 1.100900 | |
| 297 1.092400 | |
| 298 1.090300 | |
| 299 1.079400 | |
| 300 1.090300 | |
| 301 1.086100 | |
| 302 1.080300 | |
| 303 1.075600 | |
| 304 1.075900 | |
| 305 1.092200 | |
| 306 1.070600 | |
| 307 1.068800 | |
| 308 1.071300 | |
| 309 1.073900 | |
| 310 1.055400 | |
| 311 1.067900 | |
| 312 1.041000 | |
| 313 1.048600 | |
| 314 1.072600 | |
| 315 1.058800 | |
| 316 1.039000 | |
| 317 1.072300 | |
| 318 1.056600 | |
| 319 1.035100 | |
| 320 1.052800 | |
| 321 1.046700 | |
| 322 1.073400 | |
| 323 1.054000 | |
| 324 1.077100 | |
| 325 1.035200 | |
| 326 1.027700 | |
| 327 1.060000 | |
| 328 1.048900 | |
| 329 1.040000 | |
| 330 1.026900 | |
| 331 1.049300 | |
| 332 1.017100 | |
| 333 0.996200 | |
| 334 1.006400 | |
| 335 1.026700 | |
| 336 1.073700 | |
| 337 1.039200 | |
| 338 1.041100 | |
| 339 1.054300 | |
| 340 1.013500 | |
| 341 1.024900 | |
| 342 1.003300 | |
| 343 0.993400 | |
| 344 1.037300 | |
| 345 1.009300 | |
| 346 1.030400 | |
| 347 1.001400 | |
| 348 1.012100 | |
| 349 1.027300 | |
| 350 1.012700 | |
| 351 1.013400 | |
| 352 1.004400 | |
| 353 1.024800 | |
| 354 0.990700 | |
| 355 1.048600 | |
| 356 0.992700 | |
| 357 0.991800 | |
| 358 0.985300 | |
| 359 1.019100 | |
| 360 1.007300 | |
| 361 1.025500 | |
| 362 0.999100 | |
| 363 0.997900 | |
| 364 1.013300 | |
| 365 1.014700 | |
| 366 1.037700 | |
| 367 0.992400 | |
| 368 0.988800 | |
| 369 0.993900 | |
| 370 0.999500 | |
| 371 0.973000 | |
| 372 0.972200 | |
| 373 0.989200 | |
| 374 0.994500 | |
| 375 0.995800 | |
| 376 0.992000 | |
| 377 0.977800 | |
| 378 0.975700 | |
| 379 0.973700 | |
| 380 0.986200 | |
| 381 1.008000 | |
| 382 0.954100 | |
| 383 1.015900 | |
| 384 1.008200 | |
| 385 0.974700 | |
| 386 0.987500 | |
| 387 0.993700 | |
| 388 0.999200 | |
| 389 1.000700 | |
| 390 0.978600 | |
| 391 0.956200 | |
| 392 1.001600 | |
| 393 0.971300 | |
| 394 0.965800 | |
| 395 0.981000 | |
| 396 0.965400 | |
| 397 0.974200 | |
| 398 0.970700 | |
| 399 0.953500 | |
| 400 0.979700 | |
| 401 0.957700 | |
| 402 0.984600 | |
| 403 1.015600 | |
| 404 0.976800 | |
| 405 0.969100 | |
| 406 0.974200 | |
| 407 0.983300 | |
| 408 0.974300 | |
| 409 0.980600 | |
| 410 0.986300 | |
| 411 0.968100 | |
| 412 0.980500 | |
| 413 0.976200 | |
| 414 0.987300 | |
| 415 0.971600 | |
| 416 0.985200 | |
| 417 0.989800 | |
| 418 0.972000 | |
| 419 0.971100 | |
| 420 0.988800 | |
| 421 0.965600 | |
| 422 1.020400 | |
| 423 0.978000 | |
| 424 0.987800 | |
| 425 0.953700 | |
| 426 0.990400 | |
| 427 0.982900 | |
| 428 0.989100 | |
| 429 0.983800 | |
| 430 0.981500 | |
| 431 0.966900 | |
| 432 0.967300 | |
| 433 0.999400 | |
| 434 0.973100 | |
| 435 0.980500 | |
| 436 0.995500 | |
| 437 0.960300 | |
| 438 0.953700 | |
| 439 0.993600 | |
| 440 0.965100 | |
| Dataset({ | |
| features: ['input_ids', 'attention_mask'], | |
| num_rows: 56355 | |
| }) | |
| {'input_ids': [0, 13866, 338, 263, 1139, 393, 16612, 263, 848, 2009, 29892, 3300, 2859, 411, 385, 1881, 393, 16612, 263, 3758, 1591, 29889, 29871, 14350, 263, 3758, 2346, 393, 5663, 17180, 278, 848, 29889, 13, 2277, 29937, 894, 29901, 24948, 592, 825, 278, 11486, 526, 363, 4275, 8314, 29871, 13, 2277, 29937, 10567, 29901, 6137, 29871, 29896, 29899, 29896, 29900, 29900, 29900, 29896, 29947, 29896, 29899, 29896, 756, 4341, 4306, 29914, 357, 768, 706, 313, 726, 511, 1626, 29914, 7042, 12384, 313, 726, 511, 5809, 313, 726, 511, 7583, 269, 1188, 273, 313, 726, 511, 7583, 3652, 313, 726, 511, 3664, 267, 313, 726, 467, 259, 13, 2277, 29937, 673, 29901, 5097, 29871, 8695, 3895, 29871, 29896, 29899, 29896, 29900, 29900, 29900, 29896, 29947, 29896, 29899, 29896, 5754, 9626, 269, 1188, 273, 353, 525, 6156, 2692, 29950, 319, 29965, 10810, 1964, 10764, 29915, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]} |