{"id":"https://openalex.org/W7117696150","doi":"https://doi.org/10.48550/arxiv.2512.23065","title":"TabiBERT: A Large-Scale ModernBERT Foundation Model and A Unified Benchmark for Turkish","display_name":"TabiBERT: A Large-Scale ModernBERT Foundation Model and A Unified Benchmark for Turkish","publication_year":2025,"publication_date":"2025-12-28","ids":{"openalex":"https://openalex.org/W7117696150","doi":"https://doi.org/10.48550/arxiv.2512.23065"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2512.23065","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.23065","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2512.23065","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5121666106","display_name":"Melik\u015fah T\u00fcrker","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"T\u00fcrker, Melik\u015fah","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121660826","display_name":"A. Ebrar K\u0131z\u0131lo\u011flu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"K\u0131z\u0131lo\u011flu, A. Ebrar","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089046382","display_name":"Onur G\u00fcng\u00f6r","orcid":"https://orcid.org/0000-0002-7843-1439"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"G\u00fcng\u00f6r, Onur","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5121631598","display_name":"Susan \u00dcsk\u00fcdarl\u0131","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"\u00dcsk\u00fcdarl\u0131, Susan","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5121666106"],"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":0.3483999967575073,"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":0.3483999967575073,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.16930000483989716,"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/T13523","display_name":"Mathematics, Computing, and Information Processing","score":0.094200000166893,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/turkish","display_name":"Turkish","score":0.7530999779701233},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.6805999875068665},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.6577000021934509},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6324999928474426},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5479999780654907},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5031999945640564},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.4950999915599823},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.47369998693466187}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7684000134468079},{"id":"https://openalex.org/C2781121862","wikidata":"https://www.wikidata.org/wiki/Q256","display_name":"Turkish","level":2,"score":0.7530999779701233},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.6805999875068665},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.6577000021934509},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6324999928474426},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5479999780654907},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5202000141143799},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5031999945640564},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.4950999915599823},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.47369998693466187},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4235999882221222},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4113999903202057},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4081000089645386},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.4032000005245209},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.3756999969482422},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3400000035762787},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.33399999141693115},{"id":"https://openalex.org/C46743427","wikidata":"https://www.wikidata.org/wiki/Q1341685","display_name":"Inference engine","level":3,"score":0.328900009393692},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2948000133037567},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.28760001063346863},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.28189998865127563},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.2770000100135803},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.26669999957084656},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.26030001044273376},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.257099986076355}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2512.23065","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.23065","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2512.23065","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.23065","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Since":[0],"the":[1],"inception":[2],"of":[3,153],"BERT,":[4],"encoder-only":[5],"Transformers":[6],"have":[7],"evolved":[8],"significantly":[9],"in":[10],"computational":[11],"efficiency,":[12],"training":[13,198],"stability,":[14],"and":[15,29,92,110,132,148,169,200],"long-context":[16],"modeling.":[17],"ModernBERT":[18,59],"consolidates":[19],"these":[20,33],"advances":[21],"by":[22,145],"integrating":[23],"Rotary":[24],"Positional":[25],"Embeddings":[26],"(RoPE),":[27],"FlashAttention,":[28],"refined":[30],"normalization.":[31],"Despite":[32],"developments,":[34],"Turkish":[35,55,206],"NLP":[36],"lacks":[37],"a":[38,53,65],"monolingual":[39,54],"encoder":[40,56,207],"trained":[41,61],"from":[42,62,77],"scratch,":[43],"incorporating":[44],"such":[45],"modern":[46],"architectural":[47],"paradigms.":[48],"This":[49],"work":[50],"introduces":[51],"TabiBERT,":[52],"based":[57],"on":[58,64,72,141,151,160],"architecture":[60],"scratch":[63],"large,":[66],"curated":[67],"corpus.":[68],"TabiBERT":[69,138,185],"is":[70],"pre-trained":[71],"one":[73],"trillion":[74],"tokens":[75],"sampled":[76],"an":[78],"84.88B":[79],"token":[80],"multi-domain":[81],"corpus:":[82],"web":[83],"text":[84],"(73%),":[85],"scientific":[86],"publications":[87],"(20%),":[88],"source":[89],"code":[90,165,202],"(6%),":[91],"mathematical":[93],"content":[94],"(0.3%).":[95],"It":[96],"supports":[97],"8,192-token":[98],"context":[99],"length":[100],"(16x":[101],"original":[102],"BERT),":[103],"achieves":[104,186],"up":[105],"to":[106],"2.65x":[107],"inference":[108],"speedup,":[109],"reduces":[111],"GPU":[112],"memory":[113],"consumption,":[114],"enabling":[115],"larger":[116],"batch":[117],"sizes.":[118],"We":[119,194],"introduce":[120],"TabiBench":[121],"with":[122,129,156,175],"28":[123],"datasets":[124],"across":[125],"eight":[126,154],"task":[127],"categories":[128],"standardized":[130],"splits":[131],"protocols,":[133],"evaluated":[134],"using":[135],"GLUE-style":[136],"macro-averaging.":[137],"attains":[139],"77.58":[140],"TabiBench,":[142],"outperforming":[143],"BERTurk":[144],"1.62":[146],"points":[147],"establishing":[149],"state-of-the-art":[150],"five":[152],"categories,":[155],"particularly":[157],"strong":[158],"gains":[159],"question":[161],"answering":[162],"(+9.55":[163],"points),":[164,168],"retrieval":[166],"(+2.41":[167],"academic":[170],"understanding":[171],"(+0.66":[172],"points).":[173],"Compared":[174],"task-specific":[176],"prior":[177],"best":[178],"results,":[179],"including":[180],"specialized":[181],"models":[182],"like":[183],"TurkishBERTweet,":[184],"+1.47":[187],"average":[188],"improvement,":[189],"indicating":[190],"robust":[191],"cross-domain":[192],"generalization.":[193],"release":[195],"model":[196],"weights,":[197],"configurations,":[199],"evaluation":[201],"for":[203],"transparent,":[204],"reproducible":[205],"research.":[208]},"counts_by_year":[],"updated_date":"2026-01-08T20:05:33.558190","created_date":"2025-12-31T00:00:00"}
