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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 9 new columns ({'id', 'intent', 'relevance_rules', 'difficulty', 'lang', 'relevant_product_ids', 'gt_size', 'query', 'type'}) and 7 missing columns ({'per_query', 'total_pairs', 'exact_agreement', 'binary_agreement', 'confusion', 'exact_agreement_rate', 'binary_agreement_rate'}).
This happened while the json dataset builder was generating data using
hf://datasets/theagilemonkeys/aifindr-search-eval/queries/eval-queries-v4.json (at revision 0878172f680c8e174db9686a38536cadc20bb50e), [/tmp/hf-datasets-cache/medium/datasets/89554068569982-config-parquet-and-info-theagilemonkeys-aifindr-s-da5996bc/hub/datasets--theagilemonkeys--aifindr-search-eval/snapshots/0878172f680c8e174db9686a38536cadc20bb50e/queries/eval-queries-v4.json (origin=hf://datasets/theagilemonkeys/aifindr-search-eval@0878172f680c8e174db9686a38536cadc20bb50e/queries/eval-queries-v4.json), /tmp/hf-datasets-cache/medium/datasets/89554068569982-config-parquet-and-info-theagilemonkeys-aifindr-s-da5996bc/hub/datasets--theagilemonkeys--aifindr-search-eval/snapshots/0878172f680c8e174db9686a38536cadc20bb50e/results/v6-re-evaluation-results.json (origin=hf://datasets/theagilemonkeys/aifindr-search-eval@0878172f680c8e174db9686a38536cadc20bb50e/results/v6-re-evaluation-results.json)]
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1887, in _prepare_split_single
writer.write_table(table)
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 675, in write_table
pa_table = table_cast(pa_table, self._schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
id: string
query: string
lang: string
type: string
difficulty: string
intent: string
relevance_rules: struct<color_groups: list<item: string>, colors: list<item: string>, enrichment_match: struct<occasi (... 344 chars omitted)
child 0, color_groups: list<item: string>
child 0, item: string
child 1, colors: list<item: string>
child 0, item: string
child 2, enrichment_match: struct<occasions: list<item: string>, seasons: list<item: string>, style_descriptors: list<item: str (... 36 chars omitted)
child 0, occasions: list<item: string>
child 0, item: string
child 1, seasons: list<item: string>
child 0, item: string
child 2, style_descriptors: list<item: string>
child 0, item: string
child 3, use_cases: list<item: string>
child 0, item: string
child 3, exclude_colors: list<item: string>
child 0, item: string
child 4, exclude_materials: list<item: string>
child 0, item: string
child 5, exclude_types: list<item: string>
child 0, item: string
child 6, logic: string
child 7, materials: list<item: string>
child 0, item: string
child 8, product_types: list<item: string>
child 0, item: string
child 9, seasons: list<item: string>
child 0, item: string
relevant_product_ids: list<item: string>
child 0, item: string
gt_size: int64
-- schema metadata --
pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 1186
to
{'total_pairs': Value('int64'), 'exact_agreement': Value('int64'), 'exact_agreement_rate': Value('float64'), 'binary_agreement': Value('int64'), 'binary_agreement_rate': Value('float64'), 'confusion': {'I': {'E': Value('int64'), 'S': Value('int64'), 'I': Value('int64'), 'C': Value('int64')}, 'E': {'E': Value('int64'), 'S': Value('int64'), 'I': Value('int64'), 'C': Value('int64')}, 'S': {'E': Value('int64'), 'S': Value('int64'), 'I': Value('int64'), 'C': Value('int64')}, 'C': {'S': Value('int64'), 'I': Value('int64'), 'E': Value('int64'), 'C': Value('int64')}}, 'per_query': List({'query_id': Value('string'), 'query': Value('string'), 'total': Value('int64'), 'binary_agreement': Value('float64')})}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1347, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 980, in convert_to_parquet
builder.download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 884, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 947, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1736, in _prepare_split
for job_id, done, content in self._prepare_split_single(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1889, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 9 new columns ({'id', 'intent', 'relevance_rules', 'difficulty', 'lang', 'relevant_product_ids', 'gt_size', 'query', 'type'}) and 7 missing columns ({'per_query', 'total_pairs', 'exact_agreement', 'binary_agreement', 'confusion', 'exact_agreement_rate', 'binary_agreement_rate'}).
This happened while the json dataset builder was generating data using
hf://datasets/theagilemonkeys/aifindr-search-eval/queries/eval-queries-v4.json (at revision 0878172f680c8e174db9686a38536cadc20bb50e), [/tmp/hf-datasets-cache/medium/datasets/89554068569982-config-parquet-and-info-theagilemonkeys-aifindr-s-da5996bc/hub/datasets--theagilemonkeys--aifindr-search-eval/snapshots/0878172f680c8e174db9686a38536cadc20bb50e/queries/eval-queries-v4.json (origin=hf://datasets/theagilemonkeys/aifindr-search-eval@0878172f680c8e174db9686a38536cadc20bb50e/queries/eval-queries-v4.json), /tmp/hf-datasets-cache/medium/datasets/89554068569982-config-parquet-and-info-theagilemonkeys-aifindr-s-da5996bc/hub/datasets--theagilemonkeys--aifindr-search-eval/snapshots/0878172f680c8e174db9686a38536cadc20bb50e/results/v6-re-evaluation-results.json (origin=hf://datasets/theagilemonkeys/aifindr-search-eval@0878172f680c8e174db9686a38536cadc20bb50e/results/v6-re-evaluation-results.json)]
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
total_pairs int64 | exact_agreement int64 | exact_agreement_rate float64 | binary_agreement int64 | binary_agreement_rate float64 | confusion dict | per_query list |
|---|---|---|---|---|---|---|
28,488 | 17,266 | 0.60608 | 17,712 | 0.621735 | {
"I": {
"E": 2829,
"S": 7640,
"I": 15180,
"C": 87
},
"E": {
"E": 1152,
"S": 53,
"I": 2,
"C": 0
},
"S": {
"E": 278,
"S": 934,
"I": 291,
"C": 2
},
"C": {
"S": 12,
"I": 28,
"E": 0,
"C": 0
}
} | [
{
"query_id": "gs_000",
"query": "abrigos",
"total": 231,
"binary_agreement": 0.2987012987012987
},
{
"query_id": "gs_001",
"query": "accesorios",
"total": 222,
"binary_agreement": 1
},
{
"query_id": "gs_002",
"query": "accesorios_para_bolsos",
"total": 305,
"binary_agreement": 0.7081967213114754
},
{
"query_id": "gs_003",
"query": "accesorios_para_el_cuello",
"total": 261,
"binary_agreement": 0.8773946360153256
},
{
"query_id": "gs_004",
"query": "accesorios_para_el_pelo",
"total": 249,
"binary_agreement": 0.7670682730923695
},
{
"query_id": "gs_005",
"query": "accesorios_para_lluvia",
"total": 219,
"binary_agreement": 0.7214611872146118
},
{
"query_id": "gs_006",
"query": "accesorios_para_moviles",
"total": 215,
"binary_agreement": 0.9441860465116279
},
{
"query_id": "gs_007",
"query": "americana",
"total": 283,
"binary_agreement": 1
},
{
"query_id": "gs_008",
"query": "anillo_abeja",
"total": 216,
"binary_agreement": 1
},
{
"query_id": "gs_009",
"query": "anillo_catarina",
"total": 108,
"binary_agreement": 0.7407407407407407
},
{
"query_id": "gs_010",
"query": "anillo_corazon",
"total": 250,
"binary_agreement": 0.628
},
{
"query_id": "gs_011",
"query": "anillo_logo_marfil",
"total": 226,
"binary_agreement": 0.7566371681415929
},
{
"query_id": "gs_012",
"query": "anillos",
"total": 226,
"binary_agreement": 0.6991150442477876
},
{
"query_id": "gs_013",
"query": "animal-print",
"total": 227,
"binary_agreement": 0.986784140969163
},
{
"query_id": "gs_014",
"query": "aretes",
"total": 145,
"binary_agreement": 1
},
{
"query_id": "gs_015",
"query": "aretes_de_aro_dorados",
"total": 255,
"binary_agreement": 0.6392156862745098
},
{
"query_id": "gs_016",
"query": "aretes_dorados",
"total": 227,
"binary_agreement": 0.6255506607929515
},
{
"query_id": "gs_017",
"query": "bailarina_de_yute",
"total": 248,
"binary_agreement": 1
},
{
"query_id": "gs_018",
"query": "bailarinas",
"total": 102,
"binary_agreement": 0.39215686274509803
},
{
"query_id": "gs_019",
"query": "bailarinas_con_tachuelas",
"total": 148,
"binary_agreement": 0.43243243243243246
},
{
"query_id": "gs_020",
"query": "bailarinas_de_leopardo",
"total": 145,
"binary_agreement": 0.4
},
{
"query_id": "gs_021",
"query": "bailarinas_deportivas",
"total": 108,
"binary_agreement": 0.37962962962962965
},
{
"query_id": "gs_022",
"query": "bandolera_caqui_con_asa_de_hombro_y_bolsillo_delantero",
"total": 151,
"binary_agreement": 1
},
{
"query_id": "gs_023",
"query": "bandolera_caqui_con_bolsillo_delantero",
"total": 232,
"binary_agreement": 0.09913793103448276
},
{
"query_id": "gs_024",
"query": "bandolera_con_bolsillo_delantero",
"total": 247,
"binary_agreement": 0.4493927125506073
},
{
"query_id": "gs_025",
"query": "bermudas",
"total": 260,
"binary_agreement": 1
},
{
"query_id": "gs_026",
"query": "billeteras",
"total": 29,
"binary_agreement": 1
},
{
"query_id": "gs_027",
"query": "biquinis",
"total": 293,
"binary_agreement": 1
},
{
"query_id": "gs_028",
"query": "bisuteria",
"total": 56,
"binary_agreement": 1
},
{
"query_id": "gs_029",
"query": "bisuteria_letra_a",
"total": 258,
"binary_agreement": 0.8798449612403101
},
{
"query_id": "gs_030",
"query": "blusas",
"total": 255,
"binary_agreement": 1
},
{
"query_id": "gs_031",
"query": "bolsa_rosa_con_correa_de_piel",
"total": 241,
"binary_agreement": 1
},
{
"query_id": "gs_032",
"query": "bolsa_rosa_de_piel",
"total": 286,
"binary_agreement": 0.22377622377622378
},
{
"query_id": "gs_033",
"query": "bolso_azul_con_cadena_dorada",
"total": 297,
"binary_agreement": 0.5252525252525253
},
{
"query_id": "gs_034",
"query": "bolso_azul_de_red_con_logo_en_el_asa",
"total": 255,
"binary_agreement": 0.5529411764705883
},
{
"query_id": "gs_035",
"query": "bolso_bucket_con_asa_corta",
"total": 246,
"binary_agreement": 0.32113821138211385
},
{
"query_id": "gs_036",
"query": "bolso_burdeos",
"total": 215,
"binary_agreement": 0.2558139534883721
},
{
"query_id": "gs_037",
"query": "bolso_caqui_con_estampado_de_leopardo",
"total": 264,
"binary_agreement": 0.696969696969697
},
{
"query_id": "gs_038",
"query": "bolso_caqui_mini_con_estampado_de_leopardo",
"total": 186,
"binary_agreement": 0.3010752688172043
},
{
"query_id": "gs_039",
"query": "bolso_con_tachuelas",
"total": 170,
"binary_agreement": 0.9235294117647059
},
{
"query_id": "gs_040",
"query": "bolso_de_rayas",
"total": 293,
"binary_agreement": 0.8498293515358362
},
{
"query_id": "gs_041",
"query": "bolso_de_red",
"total": 268,
"binary_agreement": 0.6156716417910447
},
{
"query_id": "gs_042",
"query": "bolso_de_tela",
"total": 264,
"binary_agreement": 0.10227272727272728
},
{
"query_id": "gs_043",
"query": "bolso_dorado_en_forma_de_flor",
"total": 264,
"binary_agreement": 0.9810606060606061
},
{
"query_id": "gs_044",
"query": "bolso_en_forma_de_pelota_con_cierre_ajustable",
"total": 259,
"binary_agreement": 0.7374517374517374
},
{
"query_id": "gs_045",
"query": "bolso_hobo_con_swarovski",
"total": 108,
"binary_agreement": 0.8240740740740741
},
{
"query_id": "gs_046",
"query": "bolso_hobo_con_tachuelas",
"total": 249,
"binary_agreement": 0.3614457831325301
},
{
"query_id": "gs_047",
"query": "bolso_hobo_negro_grande",
"total": 253,
"binary_agreement": 0.4505928853754941
},
{
"query_id": "gs_048",
"query": "bolso_marfil",
"total": 243,
"binary_agreement": 0.23868312757201646
},
{
"query_id": "gs_049",
"query": "bolso_naranja_de_piel",
"total": 252,
"binary_agreement": 0.3373015873015873
},
{
"query_id": "gs_050",
"query": "bolso_negro_de_piel",
"total": 264,
"binary_agreement": 0.29924242424242425
},
{
"query_id": "gs_051",
"query": "bolso_negro_mini_de_piel",
"total": 273,
"binary_agreement": 0.14652014652014653
},
{
"query_id": "gs_052",
"query": "bolso_paper_tostado",
"total": 256,
"binary_agreement": 0.484375
},
{
"query_id": "gs_053",
"query": "bolso_paper_tostado_mediano",
"total": 254,
"binary_agreement": 0.18503937007874016
},
{
"query_id": "gs_054",
"query": "bolso_paper_tostado_mediano_nylon",
"total": 246,
"binary_agreement": 0.13414634146341464
},
{
"query_id": "gs_055",
"query": "bolso_pelota_amarillo_mediano_con_cierre_ajustable",
"total": 247,
"binary_agreement": 0.3724696356275304
},
{
"query_id": "gs_056",
"query": "bolso_pequeno",
"total": 107,
"binary_agreement": 0.18691588785046728
},
{
"query_id": "gs_057",
"query": "bolso_pequeno_de_nylon",
"total": 280,
"binary_agreement": 0.14642857142857144
},
{
"query_id": "gs_058",
"query": "bolso_plateado",
"total": 264,
"binary_agreement": 0.42803030303030304
},
{
"query_id": "gs_059",
"query": "bolso_rojo",
"total": 263,
"binary_agreement": 0.17870722433460076
},
{
"query_id": "gs_060",
"query": "bolso_rojo_de_piel",
"total": 261,
"binary_agreement": 0.19540229885057472
},
{
"query_id": "gs_061",
"query": "bolso_rojo_pequeno",
"total": 268,
"binary_agreement": 0.23134328358208955
},
{
"query_id": "gs_062",
"query": "bolso_rosa",
"total": 234,
"binary_agreement": 0.11965811965811966
},
{
"query_id": "gs_063",
"query": "bolso_xs",
"total": 285,
"binary_agreement": 0.17192982456140352
},
{
"query_id": "gs_064",
"query": "bolsos",
"total": 294,
"binary_agreement": 0.41496598639455784
},
{
"query_id": "gs_065",
"query": "bolsos_bandolera",
"total": 285,
"binary_agreement": 0.10877192982456141
},
{
"query_id": "gs_066",
"query": "bolsos_bucket",
"total": 284,
"binary_agreement": 0.3415492957746479
},
{
"query_id": "gs_067",
"query": "bolsos_canopy",
"total": 246,
"binary_agreement": 0.483739837398374
},
{
"query_id": "gs_068",
"query": "bolsos_chihuahua",
"total": 269,
"binary_agreement": 0.6988847583643123
},
{
"query_id": "gs_069",
"query": "bolsos_hobo",
"total": 293,
"binary_agreement": 0.25597269624573377
},
{
"query_id": "gs_070",
"query": "bolsos_negros",
"total": 236,
"binary_agreement": 0.4279661016949153
},
{
"query_id": "gs_071",
"query": "bolsos_paper",
"total": 265,
"binary_agreement": 0.44528301886792454
},
{
"query_id": "gs_072",
"query": "bolsos_pelota",
"total": 226,
"binary_agreement": 0.504424778761062
},
{
"query_id": "gs_073",
"query": "bolsos_pocket",
"total": 265,
"binary_agreement": 0.12830188679245283
},
{
"query_id": "gs_074",
"query": "bolsos_tote",
"total": 137,
"binary_agreement": 0.18248175182481752
},
{
"query_id": "gs_075",
"query": "bolsos_trapecio",
"total": 165,
"binary_agreement": 0.06060606060606061
},
{
"query_id": "gs_076",
"query": "botas",
"total": 237,
"binary_agreement": 1
},
{
"query_id": "gs_077",
"query": "botas_chelsea",
"total": 196,
"binary_agreement": 0.5357142857142857
},
{
"query_id": "gs_078",
"query": "botas_de_agua",
"total": 13,
"binary_agreement": 0.46153846153846156
},
{
"query_id": "gs_079",
"query": "botas_gauchas",
"total": 187,
"binary_agreement": 0.5187165775401069
},
{
"query_id": "gs_080",
"query": "botas_negras",
"total": 168,
"binary_agreement": 0.47023809523809523
},
{
"query_id": "gs_081",
"query": "botines",
"total": 96,
"binary_agreement": 0.5520833333333334
},
{
"query_id": "gs_082",
"query": "bufandas",
"total": 147,
"binary_agreement": 1
},
{
"query_id": "gs_083",
"query": "camisas",
"total": 183,
"binary_agreement": 1
},
{
"query_id": "gs_084",
"query": "camisetas",
"total": 250,
"binary_agreement": 0.432
},
{
"query_id": "gs_085",
"query": "cangureras",
"total": 268,
"binary_agreement": 1
},
{
"query_id": "gs_086",
"query": "capas",
"total": 124,
"binary_agreement": 1
},
{
"query_id": "gs_087",
"query": "carcasa_de_movil",
"total": 10,
"binary_agreement": 1
},
{
"query_id": "gs_088",
"query": "carcasa_para_iphone15",
"total": 255,
"binary_agreement": 0.7490196078431373
},
{
"query_id": "gs_089",
"query": "cardigans",
"total": 231,
"binary_agreement": 1
},
{
"query_id": "gs_090",
"query": "carteras",
"total": 220,
"binary_agreement": 1
},
{
"query_id": "gs_091",
"query": "chales",
"total": 250,
"binary_agreement": 1
},
{
"query_id": "gs_092",
"query": "chanclas",
"total": 230,
"binary_agreement": 1
},
{
"query_id": "gs_093",
"query": "chaquetas",
"total": 213,
"binary_agreement": 1
},
{
"query_id": "gs_094",
"query": "chaquetas_biker",
"total": 244,
"binary_agreement": 0.7131147540983607
},
{
"query_id": "gs_095",
"query": "chaquetas_bomber",
"total": 213,
"binary_agreement": 0.3380281690140845
},
{
"query_id": "gs_096",
"query": "chaquetas_sastre",
"total": 189,
"binary_agreement": 0.7248677248677249
},
{
"query_id": "gs_097",
"query": "charms",
"total": 237,
"binary_agreement": 1
},
{
"query_id": "gs_098",
"query": "charms_de_bolso",
"total": 250,
"binary_agreement": 0.084
},
{
"query_id": "gs_099",
"query": "charms_de_panuelo",
"total": 248,
"binary_agreement": 0.4717741935483871
},
{
"query_id": "gs_100",
"query": "cinturones",
"total": 225,
"binary_agreement": 1
},
{
"query_id": "gs_101",
"query": "deportivas_azules",
"total": 10,
"binary_agreement": 1
},
{
"query_id": "gs_102",
"query": "faldas",
"total": 249,
"binary_agreement": 1
},
{
"query_id": "gs_103",
"query": "gabardinas",
"total": 225,
"binary_agreement": 1
},
{
"query_id": "gs_104",
"query": "gorros_bucket",
"total": 218,
"binary_agreement": 1
},
{
"query_id": "gs_105",
"query": "gorros_tejidos",
"total": 219,
"binary_agreement": 0.6027397260273972
},
{
"query_id": "gs_106",
"query": "minifaldas",
"total": 179,
"binary_agreement": 1
},
{
"query_id": "gs_107",
"query": "mochilas",
"total": 182,
"binary_agreement": 1
},
{
"query_id": "gs_108",
"query": "pantalones_cargo",
"total": 87,
"binary_agreement": 1
},
{
"query_id": "gs_109",
"query": "pantalones_carrot",
"total": 22,
"binary_agreement": 0.45454545454545453
},
{
"query_id": "gs_110",
"query": "pantalones_cortos",
"total": 293,
"binary_agreement": 0.5802047781569966
},
{
"query_id": "gs_111",
"query": "pantalones_jogger",
"total": 44,
"binary_agreement": 0.6136363636363636
},
{
"query_id": "gs_112",
"query": "plumiferos",
"total": 219,
"binary_agreement": 1
},
{
"query_id": "gs_113",
"query": "prendas_de_abajo",
"total": 242,
"binary_agreement": 1
},
{
"query_id": "gs_114",
"query": "prendas_de_arriba",
"total": 241,
"binary_agreement": 1
},
{
"query_id": "gs_115",
"query": "prendas_de_una_pieza",
"total": 238,
"binary_agreement": 0.9789915966386554
},
{
"query_id": "gs_116",
"query": "prendas_intermedias",
"total": 235,
"binary_agreement": 0.4425531914893617
},
{
"query_id": "gs_117",
"query": "rafia",
"total": 250,
"binary_agreement": 1
},
{
"query_id": "gs_118",
"query": "ropa",
"total": 109,
"binary_agreement": 0.9724770642201835
},
{
"query_id": "gs_119",
"query": "ropa_de_exterior",
"total": 238,
"binary_agreement": 0.8781512605042017
},
{
"query_id": "gs_120",
"query": "sandalias_de_tacon",
"total": 218,
"binary_agreement": 1
},
{
"query_id": "gs_121",
"query": "sandalias_planas",
"total": 191,
"binary_agreement": 0.13089005235602094
},
{
"query_id": "gs_122",
"query": "tenis_cupsole",
"total": 210,
"binary_agreement": 1
},
{
"query_id": "gs_123",
"query": "tenis_jogger",
"total": 263,
"binary_agreement": 0.3688212927756654
},
{
"query_id": "gs_124",
"query": "tenis_tecnicas",
"total": 250,
"binary_agreement": 0.416
},
{
"query_id": "gs_125",
"query": "toallas",
"total": 244,
"binary_agreement": 1
},
{
"query_id": "gs_126",
"query": "vaqueros_barrel",
"total": 130,
"binary_agreement": 1
},
{
"query_id": "gs_127",
"query": "vaqueros_culotte",
"total": 100,
"binary_agreement": 0.21
},
{
"query_id": "gs_128",
"query": "vaqueros_straight_cropped",
"total": 91,
"binary_agreement": 0.2857142857142857
},
{
"query_id": "gs_129",
"query": "vaqueros_wide_leg",
"total": 97,
"binary_agreement": 0.28865979381443296
},
{
"query_id": "gs_130",
"query": "vestidos",
"total": 101,
"binary_agreement": 1
},
{
"query_id": "gs_131",
"query": "zapatos",
"total": 252,
"binary_agreement": 1
},
{
"query_id": "gs_132",
"query": "zapatos_de_tacon",
"total": 253,
"binary_agreement": 0.6086956521739131
},
{
"query_id": "gs_133",
"query": "zapatos_planos",
"total": 227,
"binary_agreement": 0.31277533039647576
}
] |
AIFindr Search — Evaluation Data
Evaluation datasets for the AIFindr Search research project. Contains labeled query-product relevance judgments, evaluation queries, catalog vocabulary, and experiment results.
Contents
- v5/, v6/: Labeled datasets with ESCI-graded relevance judgments
- queries/: Evaluation query sets (v4 base + v6 expanded)
- catalog/: Product catalog vocabulary for filter generation
- filters/: GPT-5.2 generated query filters and expansions
- results/: Evaluation results and significance tests
Usage
from datasets import load_dataset
ds = load_dataset("theagilemonkeys/aifindr-search-eval", data_files="v6/labeled-dataset-v6.json")
Or with the companion download script:
cd research && uv run python experiments/tools/download_data.py --repo eval
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