{"id":"https://openalex.org/W7130359175","doi":"https://doi.org/10.48550/arxiv.2602.15547","title":"jina-embeddings-v5-text: Task-Targeted Embedding Distillation","display_name":"jina-embeddings-v5-text: Task-Targeted Embedding Distillation","publication_year":2026,"publication_date":"2026-02-17","ids":{"openalex":"https://openalex.org/W7130359175","doi":"https://doi.org/10.48550/arxiv.2602.15547"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2602.15547","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.15547","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2602.15547","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5079669173","display_name":"Mohammad Kalim Akram","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Akram, Mohammad Kalim","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5094027921","display_name":"Saba Sturua","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sturua, Saba","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126306999","display_name":"Nastia Havriushenko","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Havriushenko, Nastia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023428886","display_name":"Q. Herreros","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Herreros, Quentin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053195307","display_name":"Michael G\u00fcnther","orcid":"https://orcid.org/0000-0002-8158-935X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"G\u00fcnther, Michael","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5094027920","display_name":"Maximilian Werk","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Werk, Maximilian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5126318857","display_name":"Han Xiao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiao, Han","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5079669173"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.5982000231742859,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.5982000231742859,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.0868000015616417,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.041099999099969864,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.7303000092506409},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5741999745368958},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5170999765396118},{"id":"https://openalex.org/keywords/truncation","display_name":"Truncation (statistics)","score":0.45890000462532043},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.45419999957084656},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4104999899864197},{"id":"https://openalex.org/keywords/distillation","display_name":"Distillation","score":0.37229999899864197},{"id":"https://openalex.org/keywords/semantic-similarity","display_name":"Semantic similarity","score":0.3492000102996826}],"concepts":[{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.7303000092506409},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6807000041007996},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5871999859809875},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5741999745368958},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5170999765396118},{"id":"https://openalex.org/C106195933","wikidata":"https://www.wikidata.org/wiki/Q7847935","display_name":"Truncation (statistics)","level":2,"score":0.45890000462532043},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.45419999957084656},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4104999899864197},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4034999907016754},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.37229999899864197},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.3492000102996826},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.3328000009059906},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3246999979019165},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.31859999895095825},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.3068999946117401},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.30390000343322754},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2985999882221222},{"id":"https://openalex.org/C2779714256","wikidata":"https://www.wikidata.org/wiki/Q25305062","display_name":"Multiple Models","level":2,"score":0.28110000491142273},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.27300000190734863},{"id":"https://openalex.org/C124304363","wikidata":"https://www.wikidata.org/wiki/Q673661","display_name":"Abstraction","level":2,"score":0.26919999718666077},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.2669000029563904},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.26429998874664307},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.25769999623298645},{"id":"https://openalex.org/C31170391","wikidata":"https://www.wikidata.org/wiki/Q188619","display_name":"Hierarchy","level":2,"score":0.2565000057220459}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2602.15547","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.15547","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2602.15547","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.15547","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"display_name":"Quality Education","score":0.5137147307395935,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Text":[0],"embedding":[1,49,125],"models":[2,17,63,87,92],"are":[3,18,117],"widely":[4],"used":[5],"for":[6,60,74,86],"semantic":[7],"similarity":[8],"tasks,":[9],"including":[10],"information":[11],"retrieval,":[12],"clustering,":[13],"and":[14,79,104,112],"classification.":[15],"General-purpose":[16],"typically":[19],"trained":[20],"with":[21,41],"single-":[22],"or":[23,67,82],"multi-stage":[24],"processes":[25],"using":[26],"contrastive":[27,43,66],"loss":[28,44],"functions.":[29],"We":[30],"introduce":[31],"a":[32],"novel":[33],"training":[34,61,69],"regimen":[35],"that":[36,54,107],"combines":[37],"model":[38,126],"distillation":[39],"techniques":[40],"task-specific":[42],"to":[45,98],"produce":[46],"compact,":[47],"high-performance":[48],"models.":[50],"Our":[51],"findings":[52],"suggest":[53],"this":[55],"approach":[56],"is":[57],"more":[58],"effective":[59],"small":[62],"than":[64],"purely":[65],"distillation-based":[68],"paradigms":[70],"alone.":[71],"Benchmark":[72],"scores":[73],"the":[75,84],"resulting":[76],"models,":[77],"jina-embeddings-v5-text-small":[78],"jina-embeddings-v5-text-nano,":[80],"exceed":[81],"match":[83],"state-of-the-art":[85],"of":[88],"similar":[89],"size.":[90],"jina-embeddings-v5-text":[91],"additionally":[93],"support":[94],"long":[95],"texts":[96],"(up":[97],"32k":[99],"tokens)":[100],"in":[101,124],"many":[102],"languages,":[103],"generate":[105],"embeddings":[106],"remain":[108],"robust":[109],"under":[110],"truncation":[111],"binary":[113],"quantization.":[114],"Model":[115],"weights":[116],"publicly":[118],"available,":[119],"hopefully":[120],"inspiring":[121],"further":[122],"advances":[123],"development.":[127]},"counts_by_year":[],"updated_date":"2026-04-30T09:15:22.047038","created_date":"2026-02-19T00:00:00"}
