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, 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