Hugging Face's logo

Datasets:
Avature
/
MELO-Benchmark

Tasks:
Text Retrieval
Sentence Similarity
Modalities:
Text
Formats:
parquet
Languages:
Bulgarian
Czech
Danish
Size:
1M - 10M
Tags:
entity-linking
occupations
multilingual
ranking
information-retrieval
ESCO
Libraries:
Datasets
pandas
Polars
License:
Dataset card Data Studio Files
xet
Community
1
Dataset Viewer
Auto-converted to Parquet Duplicate
text
stringlengths
7
60
labels
listlengths
1
71
Weinbautechniker/in (DI)
[ 6305, 6306, 6307, 6308, 6309, 6310, 6311, 6312, 6313 ]
Agrarberater/in (Ing)
[ 15024, 15025, 15026, 15027, 15028 ]
Landwirt/in
[ 3024, 3025, 3026, 3027, 3028 ]
Facharbeiter/in der ländlichen Hauswirtschaft
[ 3365, 3366, 3367, 3368, 3369 ]
Landarbeiter/in
[ 16240, 16241, 16242, 16243, 16244, 16245, 16246, 16247, 16248 ]
Facharbeiter/in Landwirtschaft
[ 6003, 6004, 6005, 6006, 6007, 6008, 6009 ]
Landwirtschaftlich(er)e Gehilf(e)in
[ 16240, 16241, 16242, 16243, 16244, 16245, 16246, 16247, 16248 ]
Landwirtschaftlich(er)e Hilfsarbeiter/in
[ 16240, 16241, 16242, 16243, 16244, 16245, 16246, 16247, 16248 ]
Stallbursch/-mädchen
[ 7522, 7523, 7524, 7525, 7526, 7527, 7528 ]
Wirtschafter/in (Landwirtschaft)
[ 3365, 3366, 3367, 3368, 3369 ]
Erntearbeiter/in
[ 5026, 5027, 5028, 5029, 5030, 5031, 5032, 5033, 5034 ]
Dorfhelfer/in
[ 3365, 3366, 3367, 3368, 3369 ]
Facharbeiter/in ländliches Betriebs- und Haushaltsmanagement
[ 3365, 3366, 3367, 3368, 3369 ]
Facharbeiter/in ländliches Betriebs- und Haushaltsmanagement
[ 3365, 3366, 3367, 3368, 3369 ]
Facharbeiter/in Landwirtschaft
[ 6003, 6004, 6005, 6006, 6007, 6008, 6009 ]
Weinbaugehilf(e)in
[ 18033, 18034, 18035, 18036, 18037, 18038, 18039, 18040, 18041, 18042, 18043 ]
Traktorführer/in
[ 9007, 9008, 9009, 9010, 9011 ]
Pferdepfleger/in
[ 15472, 15473, 15474, 15475, 15476, 15477, 15478 ]
Geflügelzuchtgehilf(e)in
[ 13344, 13345, 13346, 13347, 13348 ]
Imker/in
[ 844, 845, 846, 847, 848, 849, 850, 851, 852 ]
Hundeabrichter/in
[ 3833, 3834, 3835, 3836, 3837, 3838, 3839, 3840, 3841, 3842, 3843, 3844, 3845 ]
Landschaftsgärtner/in
[ 18141, 18142, 18143, 18144, 18145, 18146, 18147, 18148, 18149 ]
Facharbeiter/in Gartenbau
[ 2869, 2870, 2871, 2872, 2873, 2874, 2875 ]
Facharbeiter/in Obstbau und Obstverwertung
[ 14911, 14912, 14913, 14914, 14915, 14916, 14917 ]
Facharbeiter/in Gartenbau
[ 2869, 2870, 2871, 2872, 2873, 2874, 2875 ]
Facharbeiter/in Obstbau und Obstverwertung
[ 14911, 14912, 14913, 14914, 14915, 14916, 14917 ]
Forstwirt/in (Ing)
[ 18666, 18667, 18668, 18669, 18670, 18671, 18672, 18673, 18674, 18675, 18676, 18677, 18678 ]
Förster/in
[ 18888, 18889, 18890, 18891, 18892, 18893, 18894, 18895, 18896, 18897, 18898, 18899, 18900 ]
Facharbeiter/in Forstwirtschaft
[ 19502, 19503, 19504, 19505, 19506, 19507, 19508, 19509, 19510, 19511, 19512 ]
Facharbeiter/in Forstgarten- und Forstpflegewirtschaft
[ 19502, 19503, 19504, 19505, 19506, 19507, 19508, 19509, 19510, 19511, 19512 ]
Forstgehilf(e)in
[ 10118, 10119, 10120, 10121, 10122, 10123, 10124, 10125, 10126, 10127, 10128, 10129, 10130, 10131, 10132, 10133, 10134, 10135, 10136, 10137, 10138, 10139, 10140 ]
Facharbeiter/in Forstwirtschaft
[ 19502, 19503, 19504, 19505, 19506, 19507, 19508, 19509, 19510, 19511, 19512 ]
Facharbeiter/in Forstgarten- und Forstpflegewirtschaft
[ 19502, 19503, 19504, 19505, 19506, 19507, 19508, 19509, 19510, 19511, 19512 ]
Forsttechniker/in
[ 19502, 19503, 19504, 19505, 19506, 19507, 19508, 19509, 19510, 19511, 19512 ]
Jäger/in
[ 16215, 16216, 16217 ]
Bergmann/-frau
[ 18648, 18649, 18650, 18651, 18652, 18653, 18654 ]
Sprengmeister/in
[ 4123, 4124, 4125, 4126, 4127, 4128, 4129, 4130, 4131 ]
Steinbrucharbeiter/in
[ 13195, 13196, 13197, 13198, 13199, 13200, 13201, 13202, 13203, 13204, 13205, 13206, 13207, 13208, 13209, 13210, 13211, 13212, 13213, 13214, 13215, 13216, 13217, 13218, 13219, 13220, 13221, 13222, 13223 ]
Steinmetz/in
[ 18870, 18871, 18872, 18873, 18874, 18875, 18876 ]
Steinmetz/in
[ 18870, 18871, 18872, 18873, 18874, 18875, 18876 ]
Steinschleifer/in
[ 15902, 15903, 15904, 15905, 15906, 15907, 15908, 15909, 15910, 15911, 15912, 15913, 15914, 15915, 15916, 15917, 15918, 15919, 15920, 15921, 15922, 15923 ]
Betonwerker/in
[ 11341, 11342, 11343, 11344, 11345, 11346, 11347, 11348, 11349, 11350, 11351, 11352, 11353, 11354, 11355, 11356, 11357, 11358, 11359, 11360, 11361, 11362, 11363, 11364, 11365, 11366, 11367, 11368, 11369, 11370, 11371 ]
Ziegelmacher/in
[ 14824, 14825, 14826, 14827, 14828, 14829, 14830, 14831, 14832 ]
Keramiker/in
[ 18407, 18408, 18409, 18410, 18411, 18412, 18413, 18414, 18415 ]
Töpfereigehilf(e)in
[ 12572, 12573, 12574, 12575, 12576 ]
Keramiker/in - Gebrauchskeramik
[ 12572, 12573, 12574, 12575, 12576 ]
Keramiker/in - Industriekeramik
[ 920, 921, 922, 923 ]
Keramiker/in - Gebrauchskeramik
[ 12572, 12573, 12574, 12575, 12576 ]
Keramiker/in - Industriekeramik
[ 920, 921, 922, 923 ]
Porzellanmaler/in
[ 17412, 17413, 17414, 17415, 17416 ]
Porzellanschleifer/in
[ 12572, 12573, 12574, 12575, 12576 ]
Glasmacher/in
[ 17786, 17787, 17788 ]
Glasmacher/in
[ 17786, 17787, 17788 ]
Feinoptiker/in
[ 5246, 5247, 5248, 5249, 5250 ]
Feinoptiker/in
[ 5246, 5247, 5248, 5249, 5250 ]
Glasmaler/in
[ 9263, 9264, 9265 ]
Glasgestalter/in
[ 5766, 5767, 5768 ]
Maurer/in
[ 1139, 1140, 1141 ]
Maurerpolier/in
[ 1050, 1051, 1052, 1053, 1054 ]
Maurer/in
[ 1139, 1140, 1141 ]
Stukkateur/in
[ 18909, 18910, 18911, 18912, 18913, 18914, 18915, 18916, 18917, 18918, 18919 ]
Stuckateur/in und Trockenausbauer/in
[ 5769, 5770, 5771, 5772, 5773 ]
Stuckateur/in und Trockenausbauer/in
[ 5769, 5770, 5771, 5772, 5773 ]
Zimmer(er)in
[ 4068, 4069, 4070, 4071, 4072, 4073, 4074, 4075, 4076, 4077, 4078, 4079, 4080, 4081, 4082 ]
Zimmerer-Polier/in
[ 11408, 11409, 11410, 11411, 11412 ]
Zimmer(er)in
[ 4068, 4069, 4070, 4071, 4072, 4073, 4074, 4075, 4076, 4077, 4078, 4079, 4080, 4081, 4082 ]
Betonbauer/in
[ 12698, 12699, 12700, 12701, 12702, 12703, 12704, 12705, 12706, 12707, 12708, 12709, 12710, 12711, 12712, 12713, 12714, 12715, 12716, 12717, 12718, 12719, 12720, 12721, 12722, 12723, 12724, 12725, 12726, 12727, 12728, 12729, 12730 ]
Polier/in (Betonbau)
[ 3343, 3344, 3345, 3346, 3347 ]
Betonbauer/in
[ 12698, 12699, 12700, 12701, 12702, 12703, 12704, 12705, 12706, 12707, 12708, 12709, 12710, 12711, 12712, 12713, 12714, 12715, 12716, 12717, 12718, 12719, 12720, 12721, 12722, 12723, 12724, 12725, 12726, 12727, 12728, 12729, 12730 ]
Brunnenmacher/in
[ 6077, 6078, 6079, 6080, 6081, 6082, 6083, 6084, 6085, 6086, 6087, 6088, 6089, 6090, 6091, 6092, 6093 ]
Gleisbautechniker/in
[ 4065, 4066, 4067 ]
Gleisbautechniker/in
[ 4065, 4066, 4067 ]
Straßenbaupolier/in
[ 10449, 10450, 10451, 10452 ]
Kabelleger/in
[ 16428, 16429, 16430, 16431, 16432 ]
Tiefbauer/in
[ 2049, 2050, 2051, 2052, 2053 ]
Dachdecker/in
[ 14662, 14663, 14664, 14665, 14666, 14667, 14668, 14669, 14670, 14671, 14672, 14673, 14674, 14675, 14676 ]
Dachdecker/in
[ 14662, 14663, 14664, 14665, 14666, 14667, 14668, 14669, 14670, 14671, 14672, 14673, 14674, 14675, 14676 ]
Platten- und Fliesenleger/in
[ 822, 823, 824, 825, 826, 827, 828, 829, 830 ]
Platten- und Fliesenleger/in
[ 822, 823, 824, 825, 826, 827, 828, 829, 830 ]
Terrazzomacher/in
[ 13627, 13628, 13629, 13630, 13631, 13632, 13633 ]
Bodenlegerhelfer/in
[ 17932, 17933, 17934, 17935, 17936, 17937, 17938 ]
Belagsverleger/in
[ 17932, 17933, 17934, 17935, 17936, 17937, 17938 ]
Bodenleger/in
[ 17932, 17933, 17934, 17935, 17936, 17937, 17938 ]
Lackierer/in und Maler/in
[ 9861, 9862, 9863, 9864, 9865 ]
Schilderhersteller/in
[ 13362, 13363, 13364 ]
Spritzlackierer/in
[ 9861, 9862, 9863, 9864, 9865 ]
Lackiertechniker/in
[ 9861, 9862, 9863, 9864, 9865 ]
Lackiertechniker/in
[ 9861, 9862, 9863, 9864, 9865 ]
Glasermeister/in
[ 8163, 8164, 8165, 8166, 8167 ]
Friedhofsarbeiter/in
[ 13961, 13962, 13963, 13964, 13965, 13966, 13967 ]
Glaserhelfer/in
[ 8163, 8164, 8165, 8166, 8167 ]
Lackiererhelfer/in
[ 9861, 9862, 9863, 9864, 9865 ]
Zeltarbeiter/in
[ 11787, 11788, 11789 ]
Hüttenarbeiter/in
[ 3717, 3718, 3719, 3720, 3721, 3722, 3723, 3724, 3725, 3726, 3727, 3728, 3729, 3730, 3731, 3732, 3733, 3734, 3735, 3736, 3737, 3738, 3739, 3740, 3741 ]
Hüttenmeister/in
[ 3717, 3718, 3719, 3720, 3721, 3722, 3723, 3724, 3725, 3726, 3727, 3728, 3729, 3730, 3731, 3732, 3733, 3734, 3735, 3736, 3737, 3738, 3739, 3740, 3741 ]
Drahtzieher/in
[ 9999, 10000, 10001, 10002, 10003, 10004, 10005, 10006, 10007, 10008, 10009, 10010, 10011, 10012, 10013, 10014, 10015, 10016, 10017, 10018, 10019, 10020, 10021 ]
Schmelzer/in
[ 7170, 7171, 7172, 7173, 7174, 7175, 7176 ]
Former/in und Gießer/in (Metall und Eisen)
[ 7248, 7249, 7250, 7251, 7252, 7253, 7254, 7255, 7256, 7257, 7258 ]
Gießer/in
[ 17447, 17448, 17449, 17450, 17451 ]
Metallgießer/in
[ 3717, 3718, 3719, 3720, 3721, 3722, 3723, 3724, 3725, 3726, 3727, 3728, 3729, 3730, 3731, 3732, 3733, 3734, 3735, 3736, 3737, 3738, 3739, 3740, 3741 ]
End of preview. Expand in Data Studio

MELO Benchmark

This dataset contains the Multilingual Entity Linking of Occupations (MELO) Benchmark for easy loading with the HuggingFace datasets library.

Dataset Description

MELO is a collection of 48 datasets for evaluating the linking of entity mentions in 21 languages to the ESCO Occupations multilingual taxonomy. It was built using high-quality, pre-existent human annotations.

Original paper abstract:

We present the Multilingual Entity Linking of Occupations (MELO) Benchmark, a new collection of 48 datasets for evaluating the linking of entity mentions in 21 languages to the ESCO Occupations multilingual taxonomy. MELO was built using high-quality, pre-existent human annotations. We conduct experiments with simple lexical models and general-purpose sentence encoders, evaluated as bi-encoders in a zero-shot setup, to establish baselines for future research.

Original repository: https://github.com/Avature/melo-benchmark

Dataset Structure

Each subset (configuration) contains two splits:

  • queries: Query texts with their relevant corpus element indices
  • corpus: Corpus element texts

Schema

queries split:

Column Type Description
text string The query surface form
labels list[int] Indices of relevant corpus elements

corpus split:

Column Type Description
text string The corpus element surface form

Usage 

from datasets import load_dataset

# Load a specific subset
ds = load_dataset("federetyk/MELO-Benchmark", "ita_q_it_c_it")

# Access the data
query_surface_forms = ds["queries"]["text"]
corpus_surface_forms = ds["corpus"]["text"]
label_lists = ds["queries"]["labels"]

# Example: Get relevant corpus texts for the first query
query_idx = 0
print(f"Query: {query_surface_forms[query_idx]}")
print(f"Relevant corpus elements:")
for corpus_idx in label_lists[query_idx]:
    print(f"  - {corpus_surface_forms[corpus_idx]}")

Available Subsets

The subset names follow the pattern: {country}_q_{query_lang}_c_{corpus_lang(s)}

Subset ID Task Name Source Taxonomy Query Lang # Queries Target Taxonomy Corpus Lang # Corpus
usa_q_en_c_en USA-en-en O*NET en 633 ESCO v1.1.0 en 33,813
usa_q_en_c_de_en_es_fr_it_nl_pl_pt USA-en-xx † O*NET en 633 ESCO v1.1.0 xx 150,140
aut_q_de_c_de AUT-de-de Austria de 1,120 ESCO v1.1.0 de 19,782
aut_q_de_c_en AUT-de-en Austria de 1,120 ESCO v1.1.0 en 33,813
bel_q_fr_c_fr BEL-fr-fr Belgium fr 328 ESCO v1.0.3 fr 15,227
bel_q_fr_c_en BEL-fr-en Belgium fr 328 ESCO v1.0.3 en 33,609
bel_q_nl_c_nl BEL-nl-nl Belgium nl 328 ESCO v1.0.3 nl 24,070
bel_q_nl_c_en BEL-nl-en Belgium nl 328 ESCO v1.0.3 en 33,609
bgr_q_bg_c_bg BGR-bg-bg Bulgaria bg 4,438 ESCO v1.0.3 bg 21,082
bgr_q_bg_c_en BGR-bg-en Bulgaria bg 4,438 ESCO v1.0.3 en 33,609
cze_q_cs_c_cs CZE-cs-cs Czechia cs 988 ESCO v1.0.9 cs 13,333
cze_q_cs_c_en CZE-cs-en Czechia cs 988 ESCO v1.0.9 en 33,583
deu_q_de_c_de DEU-de-de Germany de 1,779 ESCO v1.0.3 de 19,135
deu_q_de_c_en DEU-de-en Germany de 1,779 ESCO v1.0.3 en 33,609
dnk_q_da_c_da DNK-da-da Denmark da 734 ESCO v1.0.8 da 10,410
dnk_q_da_c_en DNK-da-en Denmark da 734 ESCO v1.0.8 en 33,583
esp_q_es_c_es ESP-es-es Spain es 1,580 ESCO v1.0.8 es 16,502
esp_q_es_c_en ESP-es-en Spain es 1,580 ESCO v1.0.8 en 33,583
est_q_et_c_et EST-et-et Estonia et 1,068 ESCO v1.0.8 et 4,956
est_q_et_c_en EST-et-en Estonia et 1,068 ESCO v1.0.8 en 33,583
fra_q_fr_c_fr FRA-fr-fr France fr 1,435 ESCO v1.0.9 fr 15,217
fra_q_fr_c_en FRA-fr-en France fr 1,435 ESCO v1.0.9 en 33,583
hrv_q_hr_c_hr HRV-hr-hr Croatia hr 2,347 ESCO v1.0.3 hr 17,390
hrv_q_hr_c_en HRV-hr-en Croatia hr 2,347 ESCO v1.0.3 en 33,609
hun_q_hu_c_hu HUN-hu-hu Hungary hu 362 ESCO v1.0.8 hu 16,923
hun_q_hu_c_en HUN-hu-en Hungary hu 362 ESCO v1.0.8 en 33,583
ita_q_it_c_it ITA-it-it Italy it 362 ESCO v1.0.8 it 16,199
ita_q_it_c_en ITA-it-en Italy it 362 ESCO v1.0.8 en 33,583
ltu_q_lt_c_lt LTU-lt-lt Lithuania lt 3,849 ESCO v1.0.8 lt 17,824
ltu_q_lt_c_en LTU-lt-en Lithuania lt 3,849 ESCO v1.0.8 en 33,583
lva_q_lv_c_lv LVA-lv-lv Latvia lv 3,251 ESCO v1.0.8 lv 9,733
lva_q_lv_c_en LVA-lv-en Latvia lv 3,251 ESCO v1.0.8 en 33,583
nld_q_nl_c_nl NLD-nl-nl Netherlands nl 2,605 ESCO v1.0.3 nl 24,070
nld_q_nl_c_en NLD-nl-en Netherlands nl 2,605 ESCO v1.0.3 en 33,609
nor_q_no_c_no NOR-no-no Norway no 96 ESCO v1.0.8 no 7,821
nor_q_no_c_en NOR-no-en Norway no 96 ESCO v1.0.8 en 33,583
pol_q_pl_c_pl POL-pl-pl Poland pl 1,937 ESCO v1.0.3 pl 8,879
pol_q_pl_c_en POL-pl-en Poland pl 1,937 ESCO v1.0.3 en 33,609
prt_q_pt_c_pt PRT-pt-pt Portugal pt 379 ESCO v1.0.3 pt 11,671
prt_q_pt_c_en PRT-pt-en Portugal pt 379 ESCO v1.0.3 en 33,609
rou_q_ro_c_ro ROU-ro-ro Romania ro 3,273 ESCO v1.0.8 ro 14,833
rou_q_ro_c_en ROU-ro-en Romania ro 3,273 ESCO v1.0.8 en 33,583
svk_q_sk_c_sk SVK-sk-sk Slovakia sk 2,040 ESCO v1.0.8 sk 12,899
svk_q_sk_c_en SVK-sk-en Slovakia sk 2,040 ESCO v1.0.8 en 33,583
svn_q_sl_c_sl SVN-sl-sl Slovenia sl 3,222 ESCO v1.0.8 sl 15,487
svn_q_sl_c_en SVN-sl-en Slovenia sl 3,222 ESCO v1.0.8 en 33,583
swe_q_sv_c_sv SWE-sv-sv Sweden sv 2,883 ESCO v1.1.1 sv 7,506
swe_q_sv_c_en SWE-sv-en Sweden sv 2,883 ESCO v1.1.1 en 33,802

† The usa_q_en_c_de_en_es_fr_it_nl_pl_pt subset contains a multilingual corpus with texts in German, English, Spanish, French, Italian, Dutch, Polish, and Portuguese.

Subset Naming Convention

  • {country}: ISO 3166-1 alpha-3 country code (e.g., ita for Italy, usa for USA)
  • q_{lang}: Query language (ISO 639-1 code)
  • c_{lang(s)}: Corpus language(s) (one or more ISO 639-1 codes)

Examples:

  • ita_q_it_c_it: Italian queries, Italian corpus (Italy)
  • ita_q_it_c_en: Italian queries, English corpus (Italy)
  • usa_q_en_c_de_en_es_fr_it_nl_pl_pt: English queries, multilingual corpus (USA)

Citation

If you use this dataset, please cite the original MELO Benchmark paper:

@inproceedings{retyk2024melo,
  title        = {{MELO: An Evaluation Benchmark for Multilingual Entity Linking of Occupations}},
  author       = {Federico Retyk and Luis Gasco and Casimiro Pio Carrino and Daniel Deniz and Rabih Zbib},
  booktitle    = {Proceedings of the 4th Workshop on Recommender Systems for Human Resources
                  (RecSys in {HR} 2024), in conjunction with the 18th {ACM} Conference on
                  Recommender Systems},
  year         = {2024},
  url          = {https://recsyshr.aau.dk/wp-content/uploads/2024/10/RecSysHR2024-paper_2.pdf},
}

License

This dataset is licensed under the MIT License. See the LICENSE file for more information.

Downloads last month
33
Size of downloaded dataset files:
21.2 MB
Size of the auto-converted Parquet files:
21.2 MB
Number of rows:
1,379,949