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Error code: DatasetGenerationError Exception: CastError Message: Couldn't cast id: int64 name: string steps: string ingredients: list<item: string> child 0, item: string categories: list<item: string> child 0, item: string name_tokens: list<item: string> child 0, item: string -- schema metadata -- pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 776 to {'base': Value(dtype='int64', id=None), 'target': Value(dtype='int64', id=None), 'name_overlap': Value(dtype='string', id=None), 'name_iou': Value(dtype='float64', id=None), 'categories': Value(dtype='string', id=None)} because column names don't match Traceback: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1492, in compute_config_parquet_and_info_response fill_builder_info(builder, hf_endpoint=hf_endpoint, hf_token=hf_token, validate=validate) File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 702, in fill_builder_info num_examples_and_sizes: list[tuple[int, int]] = thread_map( File "/src/services/worker/.venv/lib/python3.9/site-packages/tqdm/contrib/concurrent.py", line 69, in thread_map return _executor_map(ThreadPoolExecutor, fn, *iterables, **tqdm_kwargs) File "/src/services/worker/.venv/lib/python3.9/site-packages/tqdm/contrib/concurrent.py", line 51, in _executor_map return list(tqdm_class(ex.map(fn, *iterables, chunksize=chunksize), **kwargs)) File "/src/services/worker/.venv/lib/python3.9/site-packages/tqdm/std.py", line 1169, in __iter__ for obj in iterable: File "/usr/local/lib/python3.9/concurrent/futures/_base.py", line 609, in result_iterator yield fs.pop().result() File "/usr/local/lib/python3.9/concurrent/futures/_base.py", line 446, in result return self.__get_result() File "/usr/local/lib/python3.9/concurrent/futures/_base.py", line 391, in __get_result raise self._exception File "/usr/local/lib/python3.9/concurrent/futures/thread.py", line 58, in run result = self.fn(*self.args, **self.kwargs) File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 574, in retry_validate_get_num_examples_and_size validate(pf) File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 640, in validate raise TooBigRowGroupsError( worker.job_runners.config.parquet_and_info.TooBigRowGroupsError: Parquet file has too big row groups. First row group has 312181004 which exceeds the limit of 300000000 During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1995, in _prepare_split_single for _, table in generator: File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 797, in wrapped for item in generator(*args, **kwargs): File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/parquet/parquet.py", line 97, in _generate_tables yield f"{file_idx}_{batch_idx}", self._cast_table(pa_table) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/parquet/parquet.py", line 75, in _cast_table pa_table = table_cast(pa_table, self.info.features.arrow_schema) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast return cast_table_to_schema(table, schema) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema raise CastError( datasets.table.CastError: Couldn't cast id: int64 name: string steps: string ingredients: list<item: string> child 0, item: string categories: list<item: string> child 0, item: string name_tokens: list<item: string> child 0, item: string -- schema metadata -- pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 776 to {'base': Value(dtype='int64', id=None), 'target': Value(dtype='int64', id=None), 'name_overlap': Value(dtype='string', id=None), 'name_iou': Value(dtype='float64', id=None), 'categories': Value(dtype='string', id=None)} because column names don't match The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1505, in compute_config_parquet_and_info_response parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet( File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1099, in stream_convert_to_parquet builder._prepare_split( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2038, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset
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base
int64 | target
int64 | name_overlap
string | name_iou
float64 | categories
string |
---|---|---|---|---|
96,313 | 86,230 | grilled garlic ch | 0.5312 | ['dairy_free'] |
86,230 | 96,313 | grilled garlic ch | 0.5312 | ['low_calorie', 'low_sugar', 'vegetarian'] |
107,592 | 96,313 | garlic cheese grits | 0.7037 | ['low_calorie', 'low_sugar'] |
442,284 | 96,313 | garlic cheese grits | 0.5135 | ['low_calorie', 'low_carb', 'low_sugar', 'vegetarian'] |
96,313 | 379,443 | grilled garlic ch | 0.5312 | ['dairy_free'] |
379,443 | 96,313 | grilled garlic ch | 0.5312 | ['low_calorie', 'low_carb', 'low_sugar', 'vegetarian'] |
216,364 | 96,313 | ed garlic cheese grits | 0.5116 | ['low_calorie', 'low_carb', 'low_sugar', 'vegetarian'] |
96,313 | 316,225 | grilled garlic ch | 0.5312 | ['dairy_free'] |
316,225 | 96,313 | grilled garlic ch | 0.5312 | ['low_calorie', 'low_carb', 'low_sugar', 'vegetarian'] |
67,385 | 96,313 | garlic cheese grits | 0.7037 | ['low_calorie', 'low_carb', 'low_sugar', 'vegetarian'] |
155,316 | 96,313 | garlic cheese grits | 0.7037 | ['low_calorie', 'low_carb', 'low_sugar'] |
96,313 | 375,457 | garlic cheese grits | 0.5758 | ['low_fat'] |
375,457 | 96,313 | garlic cheese grits | 0.5758 | ['low_carb', 'low_sugar', 'vegetarian'] |
353,738 | 96,313 | garlic cheese grits | 0.5135 | ['low_calorie', 'low_carb', 'low_sugar'] |
202,932 | 96,313 | garlic cheese grits | 0.7037 | ['low_calorie', 'low_carb', 'low_sugar'] |
472,759 | 96,313 | ed garlic cheese grits | 0.7333 | ['low_calorie', 'low_sugar', 'vegetarian'] |
232,037 | 28,165 | shrimp and andouille jambalaya | 0.6522 | ['low_carb', 'low_sugar'] |
232,047 | 185,154 | with dried cherries | 0.5135 | ['vegan', 'vegetarian'] |
326,752 | 232,050 | almond brittle | 0.5833 | ['dairy_free', 'vegan'] |
341,532 | 232,050 | sweet almond brittle | 0.8 | ['dairy_free', 'vegan'] |
44,541 | 232,050 | almond brittle | 0.5833 | ['dairy_free', 'vegan'] |
232,083 | 182,639 | asparagus omelette | 0.75 | ['low_carb'] |
232,083 | 376,507 | omelette wrap | 0.5417 | ['dairy_free', 'low_calorie', 'low_carb', 'low_sugar'] |
376,507 | 232,083 | omelette wrap | 0.5417 | ['vegetarian'] |
232,083 | 414,590 | asparagus omelette | 0.5294 | ['low_carb'] |
232,083 | 474,068 | asparagus omelette | 0.5625 | ['low_calorie', 'low_carb', 'low_fat', 'low_sodium'] |
232,083 | 370,616 | asparagus omelette | 0.75 | ['dairy_free'] |
232,083 | 6,813 | asparagus omelet | 0.6667 | ['low_calorie', 'low_carb'] |
182,662 | 79,222 | crab chowder | 0.5 | ['low_calorie', 'low_fat', 'low_sodium'] |
323,601 | 79,222 | crab chowder | 0.6316 | ['low_calorie', 'low_fat', 'low_sodium'] |
393,078 | 79,222 | crab chowder | 0.6316 | ['low_calorie', 'low_fat', 'low_sodium'] |
79,222 | 46,379 | crab chowder | 0.6316 | ['low_carb'] |
46,379 | 79,222 | crab chowder | 0.6316 | ['low_fat'] |
287,512 | 79,222 | crab chowder | 0.6316 | ['low_calorie', 'low_fat', 'low_sodium'] |
218,807 | 393,638 | simple sloppy joes | 0.5 | ['low_carb'] |
367,891 | 393,638 | simple sloppy joes | 0.6429 | ['gluten_free', 'low_carb'] |
378,423 | 393,638 | simple sloppy joes | 0.6429 | ['low_carb'] |
367,041 | 393,638 | simple sloppy joes | 0.5294 | ['gluten_free', 'low_carb'] |
429,666 | 393,638 | simple sloppy joes | 0.6429 | ['low_carb'] |
417,270 | 393,638 | simple sloppy joes | 0.6429 | ['dairy_free', 'low_carb'] |
232,086 | 34,803 | chocolate chip muffins | 0.5116 | ['low_fat', 'low_sodium'] |
232,086 | 515,481 | chocolate chip muffins | 0.5238 | ['dairy_free'] |
232,086 | 323,164 | chocolate chip muffins | 0.5 | ['dairy_free', 'vegan'] |
232,086 | 218,089 | chocolate chip muffins | 0.6111 | ['low_calorie', 'low_carb', 'low_sodium'] |
232,086 | 468,387 | n chocolate chip muffins | 0.6857 | ['dairy_free'] |
468,387 | 232,086 | n chocolate chip muffins | 0.6857 | ['gluten_free'] |
232,086 | 419,713 | chocolate chip muffins | 0.5238 | ['dairy_free', 'vegan'] |
232,086 | 166,157 | n chocolate chip muffins | 0.6857 | ['dairy_free', 'vegan'] |
232,086 | 167,897 | chocolate chip muffins | 0.5238 | ['dairy_free'] |
232,086 | 225,340 | chocolate chip muffins | 0.6111 | ['low_calorie', 'low_carb', 'low_sodium'] |
261,757 | 232,086 | chocolate chip muffins | 0.5 | ['gluten_free'] |
232,086 | 316,472 | chocolate chip muffins | 0.55 | ['dairy_free'] |
374,729 | 232,086 | chocolate chip muffins | 0.55 | ['gluten_free'] |
232,086 | 416,820 | chocolate chip muffins | 0.6111 | ['dairy_free'] |
232,086 | 259,320 | chocolate chip muffins | 0.5 | ['low_fat'] |
232,086 | 172,945 | chocolate chip muffins | 0.5116 | ['dairy_free'] |
232,086 | 107,296 | chocolate chip muffins | 0.5946 | ['low_fat'] |
232,086 | 179,045 | chocolate chip muffins | 0.5 | ['low_calorie'] |
232,086 | 334,902 | n chocolate chip muffins | 0.6857 | ['dairy_free'] |
232,086 | 435,314 | chocolate chip muffins | 0.5238 | ['dairy_free', 'vegan'] |
232,086 | 302,314 | chocolate chip muffins | 0.6111 | ['dairy_free'] |
232,086 | 50,535 | chocolate chip muffins | 0.5 | ['low_fat'] |
232,086 | 260,347 | chocolate chip muffins | 0.6111 | ['dairy_free'] |
232,086 | 343,462 | chocolate chip muffins | 0.6111 | ['dairy_free'] |
232,086 | 418,073 | n chocolate chip muffins | 0.6857 | ['dairy_free'] |
232,086 | 470,977 | chocolate chip muffins | 0.5366 | ['dairy_free'] |
232,086 | 37,585 | chocolate chip muffins | 0.6111 | ['dairy_free'] |
232,086 | 382,365 | chocolate chip muffins | 0.6111 | ['dairy_free'] |
232,086 | 336,219 | chocolate chip muffins | 0.5116 | ['dairy_free', 'low_fat', 'vegan'] |
232,086 | 3,564 | n chocolate chip muffins | 0.6857 | ['dairy_free'] |
411,261 | 232,086 | golden chocolate c | 0.5625 | ['gluten_free'] |
232,086 | 308,079 | chocolate chip muffins | 0.6286 | ['dairy_free'] |
232,086 | 388,388 | chocolate chip muffins | 0.5789 | ['dairy_free'] |
232,086 | 462,079 | n chocolate chip muffins | 0.6 | ['dairy_free'] |
462,079 | 232,086 | n chocolate chip muffins | 0.6 | ['gluten_free'] |
232,086 | 57,079 | chocolate chip muffins | 0.7586 | ['dairy_free'] |
232,086 | 80,619 | chocolate chip muffins | 0.6286 | ['low_calorie'] |
232,086 | 84,426 | chocolate chip muffins | 0.5946 | ['dairy_free', 'low_fat'] |
232,086 | 395,494 | chocolate chip muffins | 0.6111 | ['dairy_free', 'vegan'] |
232,086 | 516,457 | n chocolate chip muffins | 0.6857 | ['dairy_free'] |
232,086 | 377,421 | chocolate chip muffins | 0.5238 | ['dairy_free', 'low_fat'] |
232,086 | 226,077 | n chocolate chip muffins | 0.6857 | ['dairy_free'] |
232,086 | 460,688 | chocolate chip muffins | 0.5 | ['dairy_free', 'vegan'] |
232,086 | 220,379 | chocolate chip muffins | 0.6111 | ['dairy_free'] |
232,086 | 290,118 | chocolate chip muffins | 0.6111 | ['dairy_free'] |
232,086 | 298,488 | chocolate chip muffins | 0.5238 | ['dairy_free', 'vegan'] |
232,086 | 19,424 | chocolate chip muffins | 0.6111 | ['dairy_free'] |
232,086 | 350,003 | chocolate chip muffins | 0.6111 | ['dairy_free', 'vegan'] |
350,003 | 232,086 | chocolate chip muffins | 0.6111 | ['gluten_free'] |
232,086 | 330,569 | n chocolate chip muffins | 0.5333 | ['dairy_free'] |
232,086 | 260,351 | chocolate chip muffins | 0.6111 | ['dairy_free', 'low_sodium'] |
232,086 | 441,002 | n chocolate chip muffins | 0.6316 | ['dairy_free', 'vegan'] |
232,086 | 255,845 | chocolate chip muffin | 0.7 | ['low_calorie', 'low_carb', 'low_sodium'] |
139,831 | 232,088 | chocolate chip cookies | 0.5238 | ['dairy_free'] |
232,088 | 443,705 | chocolate chip cookies | 0.6471 | ['low_sodium'] |
443,705 | 232,088 | chocolate chip cookies | 0.6471 | ['dairy_free'] |
355,169 | 232,088 | chocolate chip cookies | 0.5366 | ['dairy_free'] |
20,763 | 232,088 | chocolate chip cookies | 0.5366 | ['dairy_free'] |
142,101 | 232,088 | chocolate chip cookies | 0.6286 | ['dairy_free'] |
232,088 | 390,542 | chocolate chip cookies | 0.5238 | ['low_calorie'] |
End of preview.
YAML Metadata
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"annotations_creators" must be an array
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"language_creators" must be an array
RecipePairs dataset, originally from the 2022 EMNLP paper: "SHARE: a System for Hierarchical Assistive Recipe Editing" by Shuyang Li, Yufei Li, Jianmo Ni, and Julian McAuley.
This version (1.5.0) has been updated with 6.9M pairs of base -> target
recipes, alongside their name overlap, IOU (longest common subsequence / union), and target dietary categories.
These cover the 459K recipes from the original GeniusKitcen/Food.com dataset.
If you would like to use this data or found it useful in your work/research, please cite the following papers:
@inproceedings{li-etal-2022-share,
title = "{SHARE}: a System for Hierarchical Assistive Recipe Editing",
author = "Li, Shuyang and
Li, Yufei and
Ni, Jianmo and
McAuley, Julian",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.761",
pages = "11077--11090",
abstract = "The large population of home cooks with dietary restrictions is under-served by existing cooking resources and recipe generation models. To help them, we propose the task of controllable recipe editing: adapt a base recipe to satisfy a user-specified dietary constraint. This task is challenging, and cannot be adequately solved with human-written ingredient substitution rules or existing end-to-end recipe generation models. We tackle this problem with SHARE: a System for Hierarchical Assistive Recipe Editing, which performs simultaneous ingredient substitution before generating natural-language steps using the edited ingredients. By decoupling ingredient and step editing, our step generator can explicitly integrate the available ingredients. Experiments on the novel RecipePairs dataset{---}83K pairs of similar recipes where each recipe satisfies one of seven dietary constraints{---}demonstrate that SHARE produces convincing, coherent recipes that are appropriate for a target dietary constraint. We further show through human evaluations and real-world cooking trials that recipes edited by SHARE can be easily followed by home cooks to create appealing dishes.",
}
@inproceedings{majumder-etal-2019-generating,
title = "Generating Personalized Recipes from Historical User Preferences",
author = "Majumder, Bodhisattwa Prasad and
Li, Shuyang and
Ni, Jianmo and
McAuley, Julian",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1613",
doi = "10.18653/v1/D19-1613",
pages = "5976--5982",
abstract = "Existing approaches to recipe generation are unable to create recipes for users with culinary preferences but incomplete knowledge of ingredients in specific dishes. We propose a new task of personalized recipe generation to help these users: expanding a name and incomplete ingredient details into complete natural-text instructions aligned with the user{'}s historical preferences. We attend on technique- and recipe-level representations of a user{'}s previously consumed recipes, fusing these {`}user-aware{'} representations in an attention fusion layer to control recipe text generation. Experiments on a new dataset of 180K recipes and 700K interactions show our model{'}s ability to generate plausible and personalized recipes compared to non-personalized baselines.",
}
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