Add new SentenceTransformer model

b151c48 verified 6 days ago

93.8 kB

	---
	language:
	- uz
	license: apache-2.0
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:4737
	- loss:MatryoshkaLoss
	- loss:MultipleNegativesRankingLoss
	base_model: BAAI/bge-m3
	widget:
	- source_sentence: Dam olish va ovqatlanish uchun tanaffusning davomiyligi qanday
	belgilangan?
	sentences:
	- "Ish kuni (smena) davomida xodimga dam olish va ovqatlanish uchun davomiyligi\
	\ ko‘pi bilan \nikki soat va kamida o‘ttiz daqiqa bo‘lgan, ish vaqtiga kiritilmaydigan\
	\ tanaffus berilishi kerak. Ichki \nmehnat tartibi qoidalarida yoki mehnat shartnomasida,\
	\ agar xodim uchun belgilangan kunlik ishning \n(smenaning) davomiyligi to‘rt\
	\ soatdan oshmasa, unga mazkur tanaffus berilmasligi nazarda tutilishi \nmumkin.\
	\ \nDam olish va ovqatlanish uchun tanaffus berish vaqti va uning aniq davomiyligi\
	\ ichki \nmehnat tartibi qoidalarida yoki xodim va ish beruvchi o‘rtasidagi kelishuvga\
	\ ko‘ra belgilanadi. Dam olish va ovqatlanish uchun tanaffus vaqti umuman barcha\
	\ xodimlar uchun yoki tarkibiy \nbo‘linmalar, brigadalar va xodimlarning ayrim\
	\ guruhlari uchun alohida belgilanishi mumkin. \nXodimlar dam olish va ovqatlanish\
	\ uchun tanaffusdan o‘z ixtiyor iga ko‘ra foydalanadi. Bu \nvaqtda ular ish joyidan\
	\ chiqib ketishi mumkin. \nAgar ish kunining (smenaning) davomiyligi sakkiz soatdan\
	\ oshgan hollarda xodimlar uchun \nish vaqtini jamlab hisobga olish belgilangan\
	\ bo‘lsa, xodimga dam olish va ovqatlanish uchun ikk ita \ntanaffus berilishi\
	\ kerak. \nIshlab chiqarish (ish) sharoitlariga ko‘ra dam olish va ovqatlanish\
	\ uchun tanaffus berish \nimkoni bo‘lmagan ishlarda ish beruvchi xodimga ish vaqtida\
	\ dam olish va ovqat yeyish imkoniyatini \nta’minlashi shart. Bunday ishlarning\
	\ r o‘yxati, shuningdek dam olish va ovqat yeyish uchun joylar \nichki mehnat\
	\ tartib qoidalari bilan belgilanadi. \nQonunchilikda, sanitariya normalari va\
	\ qoidalarida ayrim toifadagi xodimlarga dam olish va \novqatlanish uchun tanaffus\
	\ berishning o‘ziga xos xususiyatlari nazarda tutilishi mumkin."
	- "taqdirda, mehnat shartnom asining barcha nusxalarida mansabdor shaxsning imzosi\
	\ muhr bilan \ntasdiqlanadi. Qo‘shimcha kelishuvning bir nusxasi xodimga beriladi,\
	\ boshqasi (boshqalari) ish \nberuvchida mehnat shartnomasi bilan birga saqlanadi.\
	\ Xodim tomonidan qo‘shimcha kelishuvning \nnusxasi olinganligi xodimning ish\
	\ beruvchida saqlanadigan qo‘shimcha kelishuvning nusxasiga \nqo‘yilgan qo‘shimcha\
	\ imzosi bilan tasdiqlanadi. \nXodimni doimiy boshqa ishga o‘tkazish, mehnat shartnomasida\
	\ nazarda tutilgan mehnat \nshartlarini o‘zgartirish, shuningdek mehnat shartnomasida\
	\ shart qilib ko‘rsatilgan ish joyini \no‘zgartirish to‘g‘risidagi buyruqlar mehnat\
	\ shartnomasi taraflari tomonidan qo‘shimcha kelishuv \ntuzish orqali ushbu shartnomaga\
	\ kiritilgan o‘zgartishlarning mazmuniga aynan muvofiq ravishda \nchiqariladi\
	\ va xodimga imzo qo‘ydirib e’lon qilinadi. \nXodimni vaqtincha boshqa ishga\
	\ o‘tkazish o‘tkazishning muddati ko‘rsatilgan holda buyruq \nbilan rasmiylashtiriladi.\
	\ \nMehnat shartnomasi taraflarining kelishuviga ko‘ra va xodimning tashabbusi\
	\ bilan xodimni \nvaqtincha boshqa ishga o‘tkazish to‘g‘risida buyruq chiqarish\
	\ uchun xodimning yozma arizasi asos \nbo‘ladi. \nXodim sog‘lig‘ining holatiga\
	\ ko‘ra xodimni vaqtincha boshqa ishga o‘tkazish, homilador \nayolni, shuningdek\
	\ ikki yoshga to‘lmagan bolasini parvarishlayotgan ota-onadan birini (vasiyni)\
	\ \nularning avvalgi ishni bajarish imkoniyati bo‘lmagan taqdirda vaqtincha boshqa\
	\ ishga o‘tkazish \nhaqida buyruq chiqarish uchun ularning arizasi va tibbiy xulosa\
	\ asos bo‘ladi. \nIsh beruvchining tashabbusiga ko‘ra xodimni vaqtincha boshqa\
	\ ishga o‘tkazish to‘g‘risida \nbuyruq chiqarish uchun ishlab chiqarish zaruriyati\
	\ yoki bekor turib qolish faktlarining mavjudligi \nasos bo‘ladi. \nXodimni vaqtincha\
	\ boshqa ishga o‘tkazish mehnat shartnomasida aks ettirilmaydi."
	- "Ishlanmaydigan bayram kunlari arafasida har kunlik ishning (smenaning) davomiyligi\
	\ \nbarcha xodimlar uchun kamida bir soatga qisqartiriladi. \nBayramdan oldingi\
	\ kuni ishning (smenaning) davomiyligini qisqartirish imkoni bo‘lmagan \nuzluksiz\
	\ ishlaydigan tashkilotlarda va ayrim turdagi ishlarda ortiqcha ishlaganlik xodimga\
	\ \nqo‘shimcha dam olish vaqti berish yoki xodimning roziligi bilan ish vaqtidan\
	\ tashqari ish uchun \nbelgilangan normalar bo‘yicha haq to‘lash orqali kompensatsiya\
	\ qilinadi."
	- source_sentence: Jamoaviy muzokaralar ishtirokchilari olingan ma’lumotlarni oshkor
	qilmasligi lozimligi haqida {chapter} va {section}da qanday ko‘rsatmalar berilgan?
	sentences:
	- "Vaqtincha mehnatga qobiliyatsizlik davri va xodim haqiqatda ishda bo‘lmagan \
	\ boshqa \ndavrlar dastlabki sinov muddatiga qo‘shilmaydi."
	- "Tashkilot rahbari, uning o‘rinbosarlari, tashkilot bosh buxgalteri va tashkilot\
	\ alohida \nbo‘linmasi rahbari tashkilotga o‘zi bevosita yetkazgan haqiqiy zarar\
	\ uchun to ‘liq moddiy javobgar \nbo‘ladi. \nUshbu moddaning birinchi qismida\
	\ ko‘rsatilgan shaxslar aybli harakatlari (harakatsizligi) \ntufayli yetkazilgan\
	\ zararning o‘rnini tashkilot mulkdorining (aksiyadorlar, ishtirokchilar, muassislar\
	\ \numumiy yig‘ilishining) yoxud kuzatuv kengashining yoki mulkdor vakolat bergan\
	\ boshqa organning \ntalabiga binoan qoplaydi. Bunda zararlarni hisob -kitob qilish\
	\ fuqarolik to‘g‘risidagi qonunchilikda \nnazarda tutilgan normalarga muvofiq\
	\ amalga oshiriladi."
	- "Ijtimoiy sheriklikning har qanday tarafi jamoaviy muzokaralar tashabbuskori bo‘lishi\
	\ \nmumkin. \nAvvalgi jamoa kelishuvining, jamoa shartnomasining amal qilish muddati\
	\ tugaguniga qadar \nuch oy ichida yoki ushbu hujjatlarda belgilangan muddatlarda\
	\ ijtimoiy sheriklikning har qanday tarafi \nboshqa tarafga yangi jamoa kelishuvini,\
	\ jamoa shartnomasini tuzish yuzasidan muzokaralar boshlash \nto‘g‘risida yozma\
	\ xabar yuborishga haqlidir. \nIsh beruvchilarning manfaatlarini ifoda etuvchi\
	\ shaxslar, shuningdek ish beruvchilar, \nmahalliy ijro etuvchi hokimiyat organlari,\
	\ davlat boshqaruvi organlari, siyosiy partiyalar tashkil etgan \nyoki moliyalashtiradigan\
	\ tashkilotlar yoxud organlar tomonidan xo dimlar nomidan jamoaviy muzokaralar\
	\ olib borilishiga hamda jamoa kelishuvlari va jamoa shartnomasi tuzilishiga yo‘l\
	\ \nqo‘yilmaydi. \nIjtimoiy sheriklik taraflari tegishli so‘rov olingan kundan\
	\ e’tiboran ikki haftadan \nkechiktirmay jamoaviy muzokaralar olib borish uchun\
	\ zarur bo‘lgan o‘zidagi mavjud axborotni bir -\nbiriga taqdim etishi kerak. \n\
	Jamoaviy muzokaralar ishtirokchilari, jamoaviy muzokaralar olib borish bilan bog‘liq\
	\ \nbo‘lgan boshqa shaxslar olingan ma’lumotlarni, agar ushbu ma’lumotlar davlat\
	\ sirlariga yok i qonun \nbilan qo‘riqlanadigan boshqa sirga taalluqli bo‘lsa,\
	\ oshkor qilmasligi lozim."
	- source_sentence: Mehnat shartnomasi bekor qilinganda xodimga berilishi kerak bo‘lgan
	summalar qanday muddatda to‘lanishi kerak?
	sentences:
	- "nafaqasi to‘lanadi. Agar ish beruvchi mehnatga qobiliyatsizlik varaqasida ko‘rsatilgan\
	\ muddatda \nboshqa ish topib berolmagan bo‘lsa, buning oqibatida bekor o‘tgan\
	\ kunlar uchun mazkur nafaqa \numumiy asoslarda to‘lanadi. \nMehnatda mayib bo‘lganligi\
	\ yoki i sh bilan bog‘liq holda sog‘lig‘iga boshqacha tarzda \nshikast yetkazilganligi\
	\ munosabati bilan vaqtincha kamroq haq to‘lanadigan ishga o‘tkazilgan \nxodimlarga\
	\ ularning sog‘lig‘i shikastlanganligi uchun javobgar bo‘lgan ish beruvchi avvalgi\
	\ ish haqi \nbilan yang i ishda oladigan ish haqi o‘rtasidagi farqni to‘laydi.\
	\ Bunday farq mehnat qobiliyati \ntiklanguniga qadar yoki nogironlik belgilanguniga\
	\ qadar to‘lanadi. \nQonunchilikda sog‘lig‘ining holatiga ko‘ra yengilroq yoki\
	\ noqulay ishlab chiqarish \nomillarining ta’siridan xoli bo‘lgan, kamroq haq\
	\ to‘lanadigan ishga o‘tkazilganda avvalgi o‘rtacha \nish haqini saqlab qolishning\
	\ yoki davlat ijtimoiy sug‘urtasi bo‘yicha nafaqa to‘lashning boshqa hollari \n\
	ham nazarda tutilishi mumkin."
	- "Mehnat shartnomasi bekor qilinganda ish beruvchidan xodimga berilishi kerak bo‘lgan\
	\ \nbarcha summalarni to‘lash xodim bilan tuzilgan mehnat shartnomasi bekor qilingan\
	\ kuni amalga \noshiriladi. Agar xodim mehnat shartnomasi bekor qilingan kuni\
	\ ishlamagan bo‘lsa, tegishli summalar \nushbu xodim tomonidan hisob -kitob qilish\
	\ to‘g‘risidagi talab taqdim etilganidan keyin uch kundan \nkechiktirmay to‘lanishi\
	\ kerak. \nMehnat shartnomasi bekor qilinganda xodimga tegishli bo‘lgan summalar\
	\ miqdorlari \nto‘g‘risida nizo chiqqan ta qdirda, ish beruvchi xodimga shak -shubhasiz\
	\ tegadigan summani ushbu \nmoddaning birinchi qismida ko‘rsatilgan muddatda to‘lashi\
	\ shart. \nIchki hujjatlarda nazarda tutilgan hollarda xodim, agar u hatto mukofot\
	\ to‘lanayotgan paytda \nyakka tartibdagi mehnatga oid munosabatlarda bo‘lmasa\
	\ ham, bir yildagi ish yakunlariga ko‘ra \nmukofot olish huquqiga ega bo‘ladi."
	- "Tashkilot rahbari, uning o‘rinbosarlari, tashkilot b osh buxgalteri va tashkilot\
	\ alohida \nbo‘linmasining rahbari bilan tashkilotning ta’sis hujjatlarida yoki\
	\ taraflarning kelishuvida \nbelgilangan muddatga muddatli mehnat shartnomasi\
	\ tuzilishi mumkin. \nAksiyadorlik jamiyatining rahbari bilan qonunda belgilangan\
	\ muddatga muddatli mehnat \nshartnomasi tuziladi. \nQonunda va boshqa normativ-huquqiy\
	\ hujjatlarda, tashkilotning ta’sis hujjatlarida tashkilot \nrahbari bilan mehnat\
	\ shartnomasi tuzilishidan oldingi tartib -taomillar (tanlov o‘tkazish, lavozimga\
	\ \nsaylash yoki tayinlash va hokazo) belgilanishi mumkin. \nTashkilot rahbarini,\
	\ uning o‘rinbosarlarini, tashkilot bosh buxgalterini va tashkilotning \nalohida\
	\ bo‘linmasi rahbarini ishga qabul qilish chog‘ida olti oygacha muddat bilan dastlabki\
	\ sinov \nbelgilanishi mumkin."
	- source_sentence: Mehnat nizolarini hal etish jarayonida kimlar ishtirok etadi?
	sentences:
	- "Vaxta usulida ishlovchi shaxslarga yillik mehnat ta’tili ular vaxtalar oralig‘idagi\
	\ dam olish \nkunlaridan foydalanganidan keyin berilishi kerak. \nUshbu moddaning\
	\ birinchi qismidagi talab vaxta us ulida ishlovchi shaxslarning ta’tillar \n\
	jadvalini tuzish chog‘ida hisobga olinishi kerak. \nAgar vaxta usulida ishlovchi\
	\ shaxsning yillik mehnat ta’tilining tugashi vaxtalar oralig‘idagi \ndam olish\
	\ kunlariga to‘g‘ri kelsa, unda ish beruvchi xodimning roziligi bilan: \nvaxta\
	\ boshlanguniga qadar xodimni vaqtincha boshqa ishga o‘tkazishi; \nvaxta boshlanguniga\
	\ qadar xodimga ish haqi saqlanmagan holda ta’til berishi; \nxodimni vaxtaning\
	\ boshqa smenasiga o‘tkazishi mumkin."
	- "Mehnat to‘g‘risidagi qonunchilikni va mehnat haqidagi boshqa huquqiy hujjatlarni,\
	\ mehnat \nshartnomasini qo‘llash masalalari bo‘yicha yakka tartibdagi mehnat\
	\ nizolarini (da’vo xususiyatiga \nega yakka tartibdagi me hnat nizolari) ko‘rib\
	\ chiqish tartibi ushbu Kodeksda belgilanadi, sudlarda \nmehnat nizolari bo‘yicha\
	\ ishlarni ko‘rish tartibi esa bundan tashqari O‘zbekiston Respublikasining \n\
	Fuqarolik protsessual kodeksida belgilanadi. Xodim uchun yangi mehnat shartlarini\
	\ belgilash yoki mavjud mehnat shartlarini o‘zgartirish \nto‘g‘risidagi yakka\
	\ tartibdagi mehnat nizolari (da’vosiz xususiyatga ega bo‘lgan yakka tartibdagi\
	\ \nmehnat nizolari) ish beruvchi va kasaba uyushmasi qo‘mitasi tomonidan hal\
	\ etiladi."
	- "Xodim ish jarayonida o‘z hayotiga va sog‘lig‘iga tahdid soladigan holatlar yuzaga\
	\ kelganligi \nto‘g‘risida ish beruvchini darhol xabardor qilib, o‘z hayotiga\
	\ va sog‘lig‘iga tahdid soluvchi holatlar \nbartaraf etilguniga qadar tegishli\
	\ ishni bajarishni rad etishga haqli. Ana shu davr mobaynida \nxodimning o‘rtacha\
	\ ish haqi saqlanadi. Agar xodimning hayotiga va sog‘lig‘iga xavf soladigan holatlar\
	\ yuzaga kelmaganligi \naniqlansa, ish beruvchi ushbu Kodeksning 302 — 311-moddalarida\
	\ belgilangan tartibda xodimga \nnisbatan xizmat tekshiruvi o‘tkazish tashabbusi\
	\ bilan chiqishga haqli."
	- source_sentence: O‘n olti yoshga to‘lguniga qadar nogironligi bo‘lgan bolani tarbiyalayotgan
	ota-onaga qanday qo‘shimcha kunlar beriladi, {chapter} va {section}da bu haqida
	nima yozilgan?
	sentences:
	- "Ish beruvchi bilan: \nmehnat shartnomasida shart qilib ko‘rsatilgan ishni bajarishning\
	\ butun vaqti davomida \nmasofadan turib ishlash to‘g‘risida nomuayyan muddatga\
	\ yoki muddatli mehnat shartnomasi; \nish beruvchining nazorati ostida bo‘lgan\
	\ statsionar ish joyidan tashqarida doimiy asosda \nishlash haqidagi shartni o‘z\
	\ ichiga olgan mehnat shartnomasiga doir qo‘shimcha kelishuv tuzgan \nshaxslarning\
	\ ishi doimiy asosda masofadan turib ishlashdir. \nVaqtincha masofadan turib ishlash\
	\ xodim tomonidan mehnat vazifasini uning roziligi bilan \nish beruvchining nazorati\
	\ ostida bo‘lgan statsionar ish joyidan tashqarida vaqtincha bajarilishini \n\
	nazarda tutuvchi ish rejimidir. Vaqtincha masofadan turib ishlashda mehnat shartnomasi\
	\ taraflarining \nroziligi bilan masofadan turib ishlash rejimining muddati shart\
	\ qilib ko‘rsatilgan bo‘lishi kerak. \nMasofadan turib ishlash rejimining muddati\
	\ quyidagilar vositasida aniqlanishi mumkin: kun, oy va boshqa muddatlarda masofadan\
	\ turib ishlashning umumiy muddati davomiyligini \nko‘rsatish; \nmasofadan turib\
	\ ishlash boshlanadigan va tugallanadigan kalendar sanani belgilash; \nyuz berishi\
	\ bilan masofadan turib ishlash rejimi muddati tugashiga olib keladigan hodisani\
	\ \naniqlash (epidemiya munosabati bilan joriy etilgan karantin choralarining\
	\ bekor qilinishi, tabiiy yoki \ntexnogen xususiyatga ega halokatlar, ishlab chiqarish\
	\ avariyasi oqibatlarining bartaraf etilishi va \nboshqalar). \nVaqtincha masofadan\
	\ turib ishlashga o‘tishning eng ko‘p muddati bir yildan oshmasligi \nkerak. \n\
	Vaqtincha masofadan turib ishlash mud dati tugagach, ish beruvchi xodim uchun\
	\ u \nmasofadan turib ishlash rejimiga o‘tguniga qadar ishlagan avvalgi ish rejimini\
	\ belgilashi shart. Agar \nmasofadan turib ishlashga o‘tish vaqtincha bo‘lgan\
	\ bo‘lsa, ish beruvchi xodimning o‘tkazilish \nmuddati tugashi bilan unga avvalgi\
	\ mehnat vazifasi bo‘yicha ishini ham berishi shart."
	- "Masofadan turib ishlovchi xodim bilan tuzilgan mehnat shartnomasi ushbu Kodeksda\
	\ \nbelgilangan asoslarga ko‘ra bekor qilinishi mumkin. \nAgar masofadan turib\
	\ ishlovchi xodimning ish beruvchining masofadan turib ishlash \nto‘g‘risidagi\
	\ mehnat shartnomasini bekor qilish to‘g‘risidagi buyrug‘i bilan tanishib chiqishi\
	\ elektron \nhujjat tarzida amalga oshirilsa, ish beruvchi masofadan turib ishlovchi\
	\ xodimga mazkur mehnat \nshartnomasi bekor qilingan kuni lozim darajada rasmiylashtirilgan\
	\ mehnat shartnomasini bekor qilish \nto‘g‘risidagi buyruqning ko‘chirma nusxasini\
	\ ma’lum qilinadigan buyurtma xat bilan pochta orqali \nqog‘ozda yuborishi shart.\
	\ \n4-§. Vaxta usulida ishlovchi shaxslarning mehnatini huquqiy jihatdan tartibga\
	\ solishning \no‘ziga xos xususiyatlari"
	- "Xodimga dam olish uchun emas, balki boshqa maqsadlarda beriladigan, xodimni mehnat\
	\ \nmajburiyatlarini bajarishdan ozod etish davrlari dam olish vaqtiga kirmaydi.\
	\ Bunday davrlar \njumlasiga quyidagilar kiradi: \nmehnat shartnomasi ish beruvchining\
	\ tashabbusiga k o‘ra bekor qilinishi to‘g‘risidagi \nogohlantirish muddati davrida\
	\ xodimga ishga joylashish uchun beriladigan ishdan bo‘sh bo‘linadigan \nqo‘shimcha\
	\ kunlar; \no‘n olti yoshga to‘lguniga qadar nogironligi bo‘lgan bolani tarbiyalayotgan\
	\ ota -onadan \nbiriga (ota -onaning o‘rnini bosuvchi shaxsga) beriladigan ishdan\
	\ bo‘sh bo‘linadigan qo‘shimcha \nkunlar; \nhomilador ayollarga beriladigan ishdan\
	\ bo‘sh bo‘linadigan kunlar; \ndonorlarning tibbiy tekshiruv kunida hamda qon\
	\ va uning tarkibiy qismlari topshiriladigan \nkunda ishdan ozod etilishi; \n\
	ijtimoiy ta’tillar: homiladorlik va tug‘ish ta’tillari, bolani parvarishlash ta’tillari,\
	\ o‘quv \nta’tillari va ijodiy ta’tillar; \nxodim tomonidan davlat yoki jamoat\
	\ majburiyatlari bajariladigan davrlar; \nish beruvchining va mehnat jamoasining\
	\ m anfaatlarini ko‘zlab majburiyatlar bajariladigan \ndavrlar; \nxodimning vaqtincha\
	\ mehnatga qobiliyatsizlik davrlari; \nxodimga dam olish uchun emas, balki mehnat\
	\ to‘g‘risidagi qonunchilikda va mehnat \nhaqidagi boshqa huquqiy hujjatlarda\
	\ belgilangan o‘zga maqsadla rda beriladigan, xodimni mehnat \nmajburiyatlarini\
	\ bajarishdan ozod etishning boshqa davrlari."
	pipeline_tag: sentence-similarity
	library_name: sentence-transformers
	metrics:
	- cosine_accuracy@1
	- cosine_accuracy@3
	- cosine_accuracy@5
	- cosine_accuracy@10
	- cosine_precision@1
	- cosine_precision@3
	- cosine_precision@5
	- cosine_precision@10
	- cosine_recall@1
	- cosine_recall@3
	- cosine_recall@5
	- cosine_recall@10
	- cosine_ndcg@10
	- cosine_mrr@10
	- cosine_map@100
	model-index:
	- name: BGE m3 Uzbek Legal Matryoshka
	results:
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: dim 1024
	type: dim_1024
	metrics:
	- type: cosine_accuracy@1
	value: 0.6470588235294118
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.8349146110056926
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.8918406072106262
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.9354838709677419
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.6470588235294118
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.27830487033523077
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.17836812144212524
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.09354838709677418
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.6470588235294118
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.8349146110056926
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.8918406072106262
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.9354838709677419
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.7946291757471942
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.7489232252040601
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.7528153336142288
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: dim 768
	type: dim_768
	metrics:
	- type: cosine_accuracy@1
	value: 0.6432637571157496
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.8368121442125237
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.8956356736242884
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.9335863377609108
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.6432637571157496
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.2789373814041745
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.1791271347248577
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.09335863377609109
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.6432637571157496
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.8368121442125237
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.8956356736242884
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.9335863377609108
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.7932547875342137
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.7475678443420375
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.7515302024634125
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: dim 512
	type: dim_512
	metrics:
	- type: cosine_accuracy@1
	value: 0.6413662239089184
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.8330170777988615
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.8937381404174574
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.9316888045540797
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.6413662239089184
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.27767235926628714
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.17874762808349146
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.09316888045540797
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.6413662239089184
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.8330170777988615
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.8937381404174574
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.9316888045540797
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.7914538299798937
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.7458096141682476
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.7496769641433116
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: dim 256
	type: dim_256
	metrics:
	- type: cosine_accuracy@1
	value: 0.6204933586337761
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.8216318785578748
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.8842504743833017
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.9259962049335864
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.6204933586337761
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.2738772928526249
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.1768500948766603
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.09259962049335864
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.6204933586337761
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.8216318785578748
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.8842504743833017
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.9259962049335864
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.7779399930379503
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.7296602511972529
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.7337697408521
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: dim 128
	type: dim_128
	metrics:
	- type: cosine_accuracy@1
	value: 0.6223908918406073
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.8178368121442126
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.8671726755218216
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.9184060721062619
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.6223908918406073
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.2726122707147375
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.17343453510436432
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.09184060721062619
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.6223908918406073
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.8178368121442126
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.8671726755218216
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.9184060721062619
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.7727418198937503
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.7256528417818741
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.7303101255877268
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: dim 64
	type: dim_64
	metrics:
	- type: cosine_accuracy@1
	value: 0.5939278937381404
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.7950664136622391
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.857685009487666
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.9146110056925996
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.5939278937381404
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.265022137887413
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.1715370018975332
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.09146110056925996
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.5939278937381404
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.7950664136622391
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.857685009487666
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.9146110056925996
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.7555340391985981
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.7042927923857711
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.7088145188830932
	name: Cosine Map@100
	---

	# BGE m3 Uzbek Legal Matryoshka

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) on the json dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

	## Model Details

	### Model Description
	- Model Type: Sentence Transformer
	- Base model: [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) <!-- at revision 5617a9f61b028005a4858fdac845db406aefb181 -->
	- Maximum Sequence Length: 8192 tokens
	- Output Dimensionality: 1024 dimensions
	- Similarity Function: Cosine Similarity
	- Training Dataset:
	- json
	- Language: uz
	- License: apache-2.0

	### Model Sources

	- Documentation: [Sentence Transformers Documentation](https://sbert.net)
	- Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
	- Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

	### Full Model Architecture

	```
	SentenceTransformer(
	(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
	(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
	(2): Normalize()
	)
	```

	## Usage

	### Direct Usage (Sentence Transformers)

	First install the Sentence Transformers library:

	```bash
	pip install -U sentence-transformers
	```

	Then you can load this model and run inference.
	```python
	from sentence_transformers import SentenceTransformer

	# Download from the 🤗 Hub
	model = SentenceTransformer("fitlemon/bge-m3-uz-legal-matryoshka")
	# Run inference
	sentences = [
	'O‘n olti yoshga to‘lguniga qadar nogironligi bo‘lgan bolani tarbiyalayotgan ota-onaga qanday qo‘shimcha kunlar beriladi, {chapter} va {section}da bu haqida nima yozilgan?',
	'Xodimga dam olish uchun emas, balki boshqa maqsadlarda beriladigan, xodimni mehnat \nmajburiyatlarini bajarishdan ozod etish davrlari dam olish vaqtiga kirmaydi. Bunday davrlar \njumlasiga quyidagilar kiradi: \nmehnat shartnomasi ish beruvchining tashabbusiga k o‘ra bekor qilinishi to‘g‘risidagi \nogohlantirish muddati davrida xodimga ishga joylashish uchun beriladigan ishdan bo‘sh bo‘linadigan \nqo‘shimcha kunlar; \no‘n olti yoshga to‘lguniga qadar nogironligi bo‘lgan bolani tarbiyalayotgan ota -onadan \nbiriga (ota -onaning o‘rnini bosuvchi shaxsga) beriladigan ishdan bo‘sh bo‘linadigan qo‘shimcha \nkunlar; \nhomilador ayollarga beriladigan ishdan bo‘sh bo‘linadigan kunlar; \ndonorlarning tibbiy tekshiruv kunida hamda qon va uning tarkibiy qismlari topshiriladigan \nkunda ishdan ozod etilishi; \nijtimoiy ta’tillar: homiladorlik va tug‘ish ta’tillari, bolani parvarishlash ta’tillari, o‘quv \nta’tillari va ijodiy ta’tillar; \nxodim tomonidan davlat yoki jamoat majburiyatlari bajariladigan davrlar; \nish beruvchining va mehnat jamoasining m anfaatlarini ko‘zlab majburiyatlar bajariladigan \ndavrlar; \nxodimning vaqtincha mehnatga qobiliyatsizlik davrlari; \nxodimga dam olish uchun emas, balki mehnat to‘g‘risidagi qonunchilikda va mehnat \nhaqidagi boshqa huquqiy hujjatlarda belgilangan o‘zga maqsadla rda beriladigan, xodimni mehnat \nmajburiyatlarini bajarishdan ozod etishning boshqa davrlari.',
	'Masofadan turib ishlovchi xodim bilan tuzilgan mehnat shartnomasi ushbu Kodeksda \nbelgilangan asoslarga ko‘ra bekor qilinishi mumkin. \nAgar masofadan turib ishlovchi xodimning ish beruvchining masofadan turib ishlash \nto‘g‘risidagi mehnat shartnomasini bekor qilish to‘g‘risidagi buyrug‘i bilan tanishib chiqishi elektron \nhujjat tarzida amalga oshirilsa, ish beruvchi masofadan turib ishlovchi xodimga mazkur mehnat \nshartnomasi bekor qilingan kuni lozim darajada rasmiylashtirilgan mehnat shartnomasini bekor qilish \nto‘g‘risidagi buyruqning ko‘chirma nusxasini ma’lum qilinadigan buyurtma xat bilan pochta orqali \nqog‘ozda yuborishi shart. \n4-§. Vaxta usulida ishlovchi shaxslarning mehnatini huquqiy jihatdan tartibga solishning \no‘ziga xos xususiyatlari',
	]
	embeddings = model.encode(sentences)
	print(embeddings.shape)
	# [3, 1024]

	# Get the similarity scores for the embeddings
	similarities = model.similarity(embeddings, embeddings)
	print(similarities.shape)
	# [3, 3]
	```

	<!--
	### Direct Usage (Transformers)

	<details><summary>Click to see the direct usage in Transformers</summary>

	</details>
	-->

	<!--
	### Downstream Usage (Sentence Transformers)

	You can finetune this model on your own dataset.

	<details><summary>Click to expand</summary>

	</details>
	-->

	<!--
	### Out-of-Scope Use

	List how the model may foreseeably be misused and address what users ought not to do with the model.
	-->

	## Evaluation

	### Metrics

	#### Information Retrieval

	* Datasets: `dim_1024`, `dim_768`, `dim_512`, `dim_256`, `dim_128` and `dim_64`
	* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

	\| Metric \| dim_1024 \| dim_768 \| dim_512 \| dim_256 \| dim_128 \| dim_64 \|
	\|:--------------------\|:-----------\|:-----------\|:-----------\|:-----------\|:-----------\|:-----------\|
	\| cosine_accuracy@1 \| 0.6471 \| 0.6433 \| 0.6414 \| 0.6205 \| 0.6224 \| 0.5939 \|
	\| cosine_accuracy@3 \| 0.8349 \| 0.8368 \| 0.833 \| 0.8216 \| 0.8178 \| 0.7951 \|
	\| cosine_accuracy@5 \| 0.8918 \| 0.8956 \| 0.8937 \| 0.8843 \| 0.8672 \| 0.8577 \|
	\| cosine_accuracy@10 \| 0.9355 \| 0.9336 \| 0.9317 \| 0.926 \| 0.9184 \| 0.9146 \|
	\| cosine_precision@1 \| 0.6471 \| 0.6433 \| 0.6414 \| 0.6205 \| 0.6224 \| 0.5939 \|
	\| cosine_precision@3 \| 0.2783 \| 0.2789 \| 0.2777 \| 0.2739 \| 0.2726 \| 0.265 \|
	\| cosine_precision@5 \| 0.1784 \| 0.1791 \| 0.1787 \| 0.1769 \| 0.1734 \| 0.1715 \|
	\| cosine_precision@10 \| 0.0935 \| 0.0934 \| 0.0932 \| 0.0926 \| 0.0918 \| 0.0915 \|
	\| cosine_recall@1 \| 0.6471 \| 0.6433 \| 0.6414 \| 0.6205 \| 0.6224 \| 0.5939 \|
	\| cosine_recall@3 \| 0.8349 \| 0.8368 \| 0.833 \| 0.8216 \| 0.8178 \| 0.7951 \|
	\| cosine_recall@5 \| 0.8918 \| 0.8956 \| 0.8937 \| 0.8843 \| 0.8672 \| 0.8577 \|
	\| cosine_recall@10 \| 0.9355 \| 0.9336 \| 0.9317 \| 0.926 \| 0.9184 \| 0.9146 \|
	\| cosine_ndcg@10 \| 0.7946 \| 0.7933 \| 0.7915 \| 0.7779 \| 0.7727 \| 0.7555 \|
	\| cosine_mrr@10 \| 0.7489 \| 0.7476 \| 0.7458 \| 0.7297 \| 0.7257 \| 0.7043 \|
	\| cosine_map@100 \| 0.7528 \| 0.7515 \| 0.7497 \| 0.7338 \| 0.7303 \| 0.7088 \|

	<!--
	## Bias, Risks and Limitations

	What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.
	-->

	<!--
	### Recommendations

	What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.
	-->

	## Training Details

	### Training Dataset

	#### json

	* Dataset: json
	* Size: 4,737 training samples
	* Columns: <code>question</code> and <code>chunk</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| question \| chunk \|
	\|:--------\|:----------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------\|
	\| type \| string \| string \|
	\| details \| <ul><li>min: 9 tokens</li><li>mean: 22.45 tokens</li><li>max: 53 tokens</li></ul> \| <ul><li>min: 26 tokens</li><li>mean: 268.97 tokens</li><li>max: 520 tokens</li></ul> \|
	* Samples:
	\| question \| chunk \|
	\|:------------------------------------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>Ish beruvchi o‘rindoshlik asosida ishga qabul qilishda qanday hujjatlarni talab qilishga haqli emas?</code> \| <code>Boshqa ish beruvchiga (asosiy ish joyidan tashqari) o‘rindoshlik asosida ishga kirayotgan <br>shaxslar quyidagilarni taqdim etishi shart: <br>pasportni yoxud uning o‘rnini bosuvchi hujjatni yoki identifikatsiya ID-kartasini; <br>asosiy ish joyidan O ‘zbekiston Respublikasi Bandlik va mehnat munosabatlari vazirligi <br>tomonidan tasdiqlanadigan shakl bo‘yicha ma’lumotnomani; <br>bajarilishi uchun qonunchilikka muvofiq faqat muayyan ish stajiga ega bo‘lgan shaxslar <br>qo‘yilishi mumkin bo‘lgan ishga o‘rindoshlik a sosida qabul qilishda asosiy ish joyidagi mehnat <br>daftarchasining tasdiqlangan ko‘chirma nusxasini yoki elektron mehnat daftarchasidan ko‘chirmani; <br>diplomni, guvohnomani (sertifikatni) yoki ta’lim to‘g‘risidagi yoki kasbiy tayyorgarlik <br>haqidagi boshqa hujjatni, agar bunday ish maxsus bilimlarni yoxud maxsus tayyorgarlikni talab qilsa; <br>mehnat sharoitlari zararli va (yoki) xavfli bo‘lgan ishga qabul qilish chog‘ida asosiy ish <br>joyidan mehnatning xususiyati va shartlari to‘g‘risidagi olingan m...</code> \|
	\| <code>Yakka tartibdagi mehnatga oid munosabatlarni tartibga solishning asosiy jihatlari nimalardan iborat?</code> \| <code>Yakka tartibdagi mehnatga oid munosabatlarni va ular bilan bevosita bog‘liq bo‘lgan <br>ijtimoiy munosabatlarni huquqiy jihatdan tartibga solishning asosiy prinsiplari quyidagilardan iborat: <br>mehnat huquqlarining tengligi, mehnat va mashg‘ulotlar sohasida kamsitishni taqiqlash; <br>mehnat erkinligi va majburiy mehnatni taqiqlash; <br>mehnat sohasidagi ijtimoiy sheriklik; <br>mehnat huquqlari ta’minlanishining va mehnat majburiyatlari bajarilishining <br>kafolatlanganligi; <br>xodimning huquqiy holati yomonlashishiga yo‘l qo‘yilmasligi.</code> \|
	\| <code>Tashkilotning ta’sis hujjatlari ish beruvchining huquqlarini qanday ta'sir qiladi?</code> \| <code>Ish beruvchi moddiy zarar yetkazilgan aniq sharoitlarni hisobga olgan holda zararni aybdor <br>xodimdan to‘liq yoki qisman undirishdan voz kechish huquq iga ega. Tashkilot mulkdori ish <br>beruvchining mazkur huquqini qonunchilikda, shuningdek tashkilotning ta’sis hujjatlarida nazarda <br>tutilgan hollarda cheklashi mumkin.</code> \|
	* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
	```json
	{
	"loss": "MultipleNegativesRankingLoss",
	"matryoshka_dims": [
	1024,
	768,
	512,
	256,
	128,
	64
	],
	"matryoshka_weights": [
	1,
	1,
	1,
	1,
	1,
	1
	],
	"n_dims_per_step": -1
	}
	```

	### Training Hyperparameters
	#### Non-Default Hyperparameters

	- `eval_strategy`: epoch
	- `learning_rate`: 2e-05
	- `num_train_epochs`: 4
	- `lr_scheduler_type`: cosine
	- `warmup_ratio`: 0.1
	- `fp16`: True
	- `tf32`: False
	- `load_best_model_at_end`: True
	- `optim`: adamw_torch_fused
	- `batch_sampler`: no_duplicates

	#### All Hyperparameters
	<details><summary>Click to expand</summary>

	- `overwrite_output_dir`: False
	- `do_predict`: False
	- `eval_strategy`: epoch
	- `prediction_loss_only`: True
	- `per_device_train_batch_size`: 8
	- `per_device_eval_batch_size`: 8
	- `per_gpu_train_batch_size`: None
	- `per_gpu_eval_batch_size`: None
	- `gradient_accumulation_steps`: 1
	- `eval_accumulation_steps`: None
	- `torch_empty_cache_steps`: None
	- `learning_rate`: 2e-05
	- `weight_decay`: 0.0
	- `adam_beta1`: 0.9
	- `adam_beta2`: 0.999
	- `adam_epsilon`: 1e-08
	- `max_grad_norm`: 1.0
	- `num_train_epochs`: 4
	- `max_steps`: -1
	- `lr_scheduler_type`: cosine
	- `lr_scheduler_kwargs`: {}
	- `warmup_ratio`: 0.1
	- `warmup_steps`: 0
	- `log_level`: passive
	- `log_level_replica`: warning
	- `log_on_each_node`: True
	- `logging_nan_inf_filter`: True
	- `save_safetensors`: True
	- `save_on_each_node`: False
	- `save_only_model`: False
	- `restore_callback_states_from_checkpoint`: False
	- `no_cuda`: False
	- `use_cpu`: False
	- `use_mps_device`: False
	- `seed`: 42
	- `data_seed`: None
	- `jit_mode_eval`: False
	- `use_ipex`: False
	- `bf16`: False
	- `fp16`: True
	- `fp16_opt_level`: O1
	- `half_precision_backend`: auto
	- `bf16_full_eval`: False
	- `fp16_full_eval`: False
	- `tf32`: False
	- `local_rank`: 0
	- `ddp_backend`: None
	- `tpu_num_cores`: None
	- `tpu_metrics_debug`: False
	- `debug`: []
	- `dataloader_drop_last`: False
	- `dataloader_num_workers`: 0
	- `dataloader_prefetch_factor`: None
	- `past_index`: -1
	- `disable_tqdm`: False
	- `remove_unused_columns`: True
	- `label_names`: None
	- `load_best_model_at_end`: True
	- `ignore_data_skip`: False
	- `fsdp`: []
	- `fsdp_min_num_params`: 0
	- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
	- `fsdp_transformer_layer_cls_to_wrap`: None
	- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
	- `deepspeed`: None
	- `label_smoothing_factor`: 0.0
	- `optim`: adamw_torch_fused
	- `optim_args`: None
	- `adafactor`: False
	- `group_by_length`: False
	- `length_column_name`: length
	- `ddp_find_unused_parameters`: None
	- `ddp_bucket_cap_mb`: None
	- `ddp_broadcast_buffers`: False
	- `dataloader_pin_memory`: True
	- `dataloader_persistent_workers`: False
	- `skip_memory_metrics`: True
	- `use_legacy_prediction_loop`: False
	- `push_to_hub`: False
	- `resume_from_checkpoint`: None
	- `hub_model_id`: None
	- `hub_strategy`: every_save
	- `hub_private_repo`: None
	- `hub_always_push`: False
	- `gradient_checkpointing`: False
	- `gradient_checkpointing_kwargs`: None
	- `include_inputs_for_metrics`: False
	- `include_for_metrics`: []
	- `eval_do_concat_batches`: True
	- `fp16_backend`: auto
	- `push_to_hub_model_id`: None
	- `push_to_hub_organization`: None
	- `mp_parameters`:
	- `auto_find_batch_size`: False
	- `full_determinism`: False
	- `torchdynamo`: None
	- `ray_scope`: last
	- `ddp_timeout`: 1800
	- `torch_compile`: False
	- `torch_compile_backend`: None
	- `torch_compile_mode`: None
	- `dispatch_batches`: None
	- `split_batches`: None
	- `include_tokens_per_second`: False
	- `include_num_input_tokens_seen`: False
	- `neftune_noise_alpha`: None
	- `optim_target_modules`: None
	- `batch_eval_metrics`: False
	- `eval_on_start`: False
	- `use_liger_kernel`: False
	- `eval_use_gather_object`: False
	- `average_tokens_across_devices`: False
	- `prompts`: None
	- `batch_sampler`: no_duplicates
	- `multi_dataset_batch_sampler`: proportional

	</details>

	### Training Logs
	<details><summary>Click to expand</summary>

	\| Epoch \| Step \| Training Loss \| dim_1024_cosine_ndcg@10 \| dim_768_cosine_ndcg@10 \| dim_512_cosine_ndcg@10 \| dim_256_cosine_ndcg@10 \| dim_128_cosine_ndcg@10 \| dim_64_cosine_ndcg@10 \|
	\|:-------:\|:--------:\|:-------------:\|:-----------------------:\|:----------------------:\|:----------------------:\|:----------------------:\|:----------------------:\|:---------------------:\|
	\| 0.0169 \| 10 \| 2.9869 \| - \| - \| - \| - \| - \| - \|
	\| 0.0337 \| 20 \| 2.7979 \| - \| - \| - \| - \| - \| - \|
	\| 0.0506 \| 30 \| 2.7458 \| - \| - \| - \| - \| - \| - \|
	\| 0.0675 \| 40 \| 1.9948 \| - \| - \| - \| - \| - \| - \|
	\| 0.0843 \| 50 \| 1.8067 \| - \| - \| - \| - \| - \| - \|
	\| 0.1012 \| 60 \| 1.6556 \| - \| - \| - \| - \| - \| - \|
	\| 0.1180 \| 70 \| 1.3729 \| - \| - \| - \| - \| - \| - \|
	\| 0.1349 \| 80 \| 1.9454 \| - \| - \| - \| - \| - \| - \|
	\| 0.1518 \| 90 \| 0.7781 \| - \| - \| - \| - \| - \| - \|
	\| 0.1686 \| 100 \| 1.5047 \| - \| - \| - \| - \| - \| - \|
	\| 0.1855 \| 110 \| 1.5764 \| - \| - \| - \| - \| - \| - \|
	\| 0.2024 \| 120 \| 2.0667 \| - \| - \| - \| - \| - \| - \|
	\| 0.2192 \| 130 \| 1.9632 \| - \| - \| - \| - \| - \| - \|
	\| 0.2361 \| 140 \| 0.6082 \| - \| - \| - \| - \| - \| - \|
	\| 0.2530 \| 150 \| 1.0892 \| - \| - \| - \| - \| - \| - \|
	\| 0.2698 \| 160 \| 1.4455 \| - \| - \| - \| - \| - \| - \|
	\| 0.2867 \| 170 \| 1.6741 \| - \| - \| - \| - \| - \| - \|
	\| 0.3035 \| 180 \| 1.3283 \| - \| - \| - \| - \| - \| - \|
	\| 0.3204 \| 190 \| 1.0791 \| - \| - \| - \| - \| - \| - \|
	\| 0.3373 \| 200 \| 1.0939 \| - \| - \| - \| - \| - \| - \|
	\| 0.3541 \| 210 \| 0.923 \| - \| - \| - \| - \| - \| - \|
	\| 0.3710 \| 220 \| 0.5855 \| - \| - \| - \| - \| - \| - \|
	\| 0.3879 \| 230 \| 0.8982 \| - \| - \| - \| - \| - \| - \|
	\| 0.4047 \| 240 \| 0.8841 \| - \| - \| - \| - \| - \| - \|
	\| 0.4216 \| 250 \| 0.9478 \| - \| - \| - \| - \| - \| - \|
	\| 0.4384 \| 260 \| 1.5893 \| - \| - \| - \| - \| - \| - \|
	\| 0.4553 \| 270 \| 1.2372 \| - \| - \| - \| - \| - \| - \|
	\| 0.4722 \| 280 \| 0.9174 \| - \| - \| - \| - \| - \| - \|
	\| 0.4890 \| 290 \| 0.6589 \| - \| - \| - \| - \| - \| - \|
	\| 0.5059 \| 300 \| 0.98 \| - \| - \| - \| - \| - \| - \|
	\| 0.5228 \| 310 \| 1.0765 \| - \| - \| - \| - \| - \| - \|
	\| 0.5396 \| 320 \| 1.0838 \| - \| - \| - \| - \| - \| - \|
	\| 0.5565 \| 330 \| 1.4062 \| - \| - \| - \| - \| - \| - \|
	\| 0.5734 \| 340 \| 1.0347 \| - \| - \| - \| - \| - \| - \|
	\| 0.5902 \| 350 \| 0.9098 \| - \| - \| - \| - \| - \| - \|
	\| 0.6071 \| 360 \| 1.8553 \| - \| - \| - \| - \| - \| - \|
	\| 0.6239 \| 370 \| 0.9615 \| - \| - \| - \| - \| - \| - \|
	\| 0.6408 \| 380 \| 1.6353 \| - \| - \| - \| - \| - \| - \|
	\| 0.6577 \| 390 \| 0.8521 \| - \| - \| - \| - \| - \| - \|
	\| 0.6745 \| 400 \| 1.3464 \| - \| - \| - \| - \| - \| - \|
	\| 0.6914 \| 410 \| 0.7428 \| - \| - \| - \| - \| - \| - \|
	\| 0.7083 \| 420 \| 1.5889 \| - \| - \| - \| - \| - \| - \|
	\| 0.7251 \| 430 \| 1.0916 \| - \| - \| - \| - \| - \| - \|
	\| 0.7420 \| 440 \| 0.7608 \| - \| - \| - \| - \| - \| - \|
	\| 0.7589 \| 450 \| 0.7987 \| - \| - \| - \| - \| - \| - \|
	\| 0.7757 \| 460 \| 0.6777 \| - \| - \| - \| - \| - \| - \|
	\| 0.7926 \| 470 \| 1.4708 \| - \| - \| - \| - \| - \| - \|
	\| 0.8094 \| 480 \| 0.5794 \| - \| - \| - \| - \| - \| - \|
	\| 0.8263 \| 490 \| 1.016 \| - \| - \| - \| - \| - \| - \|
	\| 0.8432 \| 500 \| 0.6064 \| - \| - \| - \| - \| - \| - \|
	\| 0.8600 \| 510 \| 0.828 \| - \| - \| - \| - \| - \| - \|
	\| 0.8769 \| 520 \| 0.3055 \| - \| - \| - \| - \| - \| - \|
	\| 0.8938 \| 530 \| 1.3419 \| - \| - \| - \| - \| - \| - \|
	\| 0.9106 \| 540 \| 1.9443 \| - \| - \| - \| - \| - \| - \|
	\| 0.9275 \| 550 \| 1.1958 \| - \| - \| - \| - \| - \| - \|
	\| 0.9444 \| 560 \| 1.0707 \| - \| - \| - \| - \| - \| - \|
	\| 0.9612 \| 570 \| 0.509 \| - \| - \| - \| - \| - \| - \|
	\| 0.9781 \| 580 \| 1.1698 \| - \| - \| - \| - \| - \| - \|
	\| 0.9949 \| 590 \| 0.58 \| - \| - \| - \| - \| - \| - \|
	\| 1.0 \| 593 \| - \| 0.7864 \| 0.7830 \| 0.7770 \| 0.7631 \| 0.7414 \| 0.7046 \|
	\| 1.0118 \| 600 \| 0.3053 \| - \| - \| - \| - \| - \| - \|
	\| 1.0287 \| 610 \| 0.6652 \| - \| - \| - \| - \| - \| - \|
	\| 1.0455 \| 620 \| 0.8645 \| - \| - \| - \| - \| - \| - \|
	\| 1.0624 \| 630 \| 0.4758 \| - \| - \| - \| - \| - \| - \|
	\| 1.0793 \| 640 \| 0.6793 \| - \| - \| - \| - \| - \| - \|
	\| 1.0961 \| 650 \| 0.5269 \| - \| - \| - \| - \| - \| - \|
	\| 1.1130 \| 660 \| 0.5493 \| - \| - \| - \| - \| - \| - \|
	\| 1.1298 \| 670 \| 0.8714 \| - \| - \| - \| - \| - \| - \|
	\| 1.1467 \| 680 \| 0.2095 \| - \| - \| - \| - \| - \| - \|
	\| 1.1636 \| 690 \| 0.5681 \| - \| - \| - \| - \| - \| - \|
	\| 1.1804 \| 700 \| 1.0656 \| - \| - \| - \| - \| - \| - \|
	\| 1.1973 \| 710 \| 0.3448 \| - \| - \| - \| - \| - \| - \|
	\| 1.2142 \| 720 \| 0.9805 \| - \| - \| - \| - \| - \| - \|
	\| 1.2310 \| 730 \| 0.9345 \| - \| - \| - \| - \| - \| - \|
	\| 1.2479 \| 740 \| 0.7285 \| - \| - \| - \| - \| - \| - \|
	\| 1.2648 \| 750 \| 0.5815 \| - \| - \| - \| - \| - \| - \|
	\| 1.2816 \| 760 \| 1.0547 \| - \| - \| - \| - \| - \| - \|
	\| 1.2985 \| 770 \| 0.759 \| - \| - \| - \| - \| - \| - \|
	\| 1.3153 \| 780 \| 0.9341 \| - \| - \| - \| - \| - \| - \|
	\| 1.3322 \| 790 \| 0.6537 \| - \| - \| - \| - \| - \| - \|
	\| 1.3491 \| 800 \| 0.7775 \| - \| - \| - \| - \| - \| - \|
	\| 1.3659 \| 810 \| 0.7652 \| - \| - \| - \| - \| - \| - \|
	\| 1.3828 \| 820 \| 0.3977 \| - \| - \| - \| - \| - \| - \|
	\| 1.3997 \| 830 \| 1.1133 \| - \| - \| - \| - \| - \| - \|
	\| 1.4165 \| 840 \| 0.5203 \| - \| - \| - \| - \| - \| - \|
	\| 1.4334 \| 850 \| 0.2669 \| - \| - \| - \| - \| - \| - \|
	\| 1.4503 \| 860 \| 0.9608 \| - \| - \| - \| - \| - \| - \|
	\| 1.4671 \| 870 \| 0.4095 \| - \| - \| - \| - \| - \| - \|
	\| 1.4840 \| 880 \| 0.8907 \| - \| - \| - \| - \| - \| - \|
	\| 1.5008 \| 890 \| 0.5912 \| - \| - \| - \| - \| - \| - \|
	\| 1.5177 \| 900 \| 0.6184 \| - \| - \| - \| - \| - \| - \|
	\| 1.5346 \| 910 \| 0.5476 \| - \| - \| - \| - \| - \| - \|
	\| 1.5514 \| 920 \| 0.4008 \| - \| - \| - \| - \| - \| - \|
	\| 1.5683 \| 930 \| 0.2897 \| - \| - \| - \| - \| - \| - \|
	\| 1.5852 \| 940 \| 0.4879 \| - \| - \| - \| - \| - \| - \|
	\| 1.6020 \| 950 \| 0.3882 \| - \| - \| - \| - \| - \| - \|
	\| 1.6189 \| 960 \| 0.6128 \| - \| - \| - \| - \| - \| - \|
	\| 1.6358 \| 970 \| 0.5498 \| - \| - \| - \| - \| - \| - \|
	\| 1.6526 \| 980 \| 0.4599 \| - \| - \| - \| - \| - \| - \|
	\| 1.6695 \| 990 \| 0.8448 \| - \| - \| - \| - \| - \| - \|
	\| 1.6863 \| 1000 \| 0.4084 \| - \| - \| - \| - \| - \| - \|
	\| 1.7032 \| 1010 \| 0.2107 \| - \| - \| - \| - \| - \| - \|
	\| 1.7201 \| 1020 \| 0.8027 \| - \| - \| - \| - \| - \| - \|
	\| 1.7369 \| 1030 \| 0.8358 \| - \| - \| - \| - \| - \| - \|
	\| 1.7538 \| 1040 \| 0.7824 \| - \| - \| - \| - \| - \| - \|
	\| 1.7707 \| 1050 \| 0.3526 \| - \| - \| - \| - \| - \| - \|
	\| 1.7875 \| 1060 \| 0.9841 \| - \| - \| - \| - \| - \| - \|
	\| 1.8044 \| 1070 \| 0.588 \| - \| - \| - \| - \| - \| - \|
	\| 1.8212 \| 1080 \| 0.551 \| - \| - \| - \| - \| - \| - \|
	\| 1.8381 \| 1090 \| 0.1695 \| - \| - \| - \| - \| - \| - \|
	\| 1.8550 \| 1100 \| 0.4445 \| - \| - \| - \| - \| - \| - \|
	\| 1.8718 \| 1110 \| 0.7868 \| - \| - \| - \| - \| - \| - \|
	\| 1.8887 \| 1120 \| 0.2798 \| - \| - \| - \| - \| - \| - \|
	\| 1.9056 \| 1130 \| 0.8559 \| - \| - \| - \| - \| - \| - \|
	\| 1.9224 \| 1140 \| 1.0843 \| - \| - \| - \| - \| - \| - \|
	\| 1.9393 \| 1150 \| 0.3561 \| - \| - \| - \| - \| - \| - \|
	\| 1.9562 \| 1160 \| 0.8827 \| - \| - \| - \| - \| - \| - \|
	\| 1.9730 \| 1170 \| 0.6912 \| - \| - \| - \| - \| - \| - \|
	\| 1.9899 \| 1180 \| 0.4215 \| - \| - \| - \| - \| - \| - \|
	\| 2.0 \| 1186 \| - \| 0.7821 \| 0.7791 \| 0.7753 \| 0.7610 \| 0.7562 \| 0.7326 \|
	\| 2.0067 \| 1190 \| 0.2097 \| - \| - \| - \| - \| - \| - \|
	\| 2.0236 \| 1200 \| 0.2441 \| - \| - \| - \| - \| - \| - \|
	\| 2.0405 \| 1210 \| 0.6279 \| - \| - \| - \| - \| - \| - \|
	\| 2.0573 \| 1220 \| 0.2016 \| - \| - \| - \| - \| - \| - \|
	\| 2.0742 \| 1230 \| 1.068 \| - \| - \| - \| - \| - \| - \|
	\| 2.0911 \| 1240 \| 0.6641 \| - \| - \| - \| - \| - \| - \|
	\| 2.1079 \| 1250 \| 0.0971 \| - \| - \| - \| - \| - \| - \|
	\| 2.1248 \| 1260 \| 0.5854 \| - \| - \| - \| - \| - \| - \|
	\| 2.1417 \| 1270 \| 1.0182 \| - \| - \| - \| - \| - \| - \|
	\| 2.1585 \| 1280 \| 0.3596 \| - \| - \| - \| - \| - \| - \|
	\| 2.1754 \| 1290 \| 0.6765 \| - \| - \| - \| - \| - \| - \|
	\| 2.1922 \| 1300 \| 0.1574 \| - \| - \| - \| - \| - \| - \|
	\| 2.2091 \| 1310 \| 0.2267 \| - \| - \| - \| - \| - \| - \|
	\| 2.2260 \| 1320 \| 0.7106 \| - \| - \| - \| - \| - \| - \|
	\| 2.2428 \| 1330 \| 0.2617 \| - \| - \| - \| - \| - \| - \|
	\| 2.2597 \| 1340 \| 0.3977 \| - \| - \| - \| - \| - \| - \|
	\| 2.2766 \| 1350 \| 1.0292 \| - \| - \| - \| - \| - \| - \|
	\| 2.2934 \| 1360 \| 0.3401 \| - \| - \| - \| - \| - \| - \|
	\| 2.3103 \| 1370 \| 0.3034 \| - \| - \| - \| - \| - \| - \|
	\| 2.3272 \| 1380 \| 0.3307 \| - \| - \| - \| - \| - \| - \|
	\| 2.3440 \| 1390 \| 0.6796 \| - \| - \| - \| - \| - \| - \|
	\| 2.3609 \| 1400 \| 0.3568 \| - \| - \| - \| - \| - \| - \|
	\| 2.3777 \| 1410 \| 0.0886 \| - \| - \| - \| - \| - \| - \|
	\| 2.3946 \| 1420 \| 0.3308 \| - \| - \| - \| - \| - \| - \|
	\| 2.4115 \| 1430 \| 0.5477 \| - \| - \| - \| - \| - \| - \|
	\| 2.4283 \| 1440 \| 0.035 \| - \| - \| - \| - \| - \| - \|
	\| 2.4452 \| 1450 \| 0.5458 \| - \| - \| - \| - \| - \| - \|
	\| 2.4621 \| 1460 \| 0.118 \| - \| - \| - \| - \| - \| - \|
	\| 2.4789 \| 1470 \| 0.6712 \| - \| - \| - \| - \| - \| - \|
	\| 2.4958 \| 1480 \| 0.4372 \| - \| - \| - \| - \| - \| - \|
	\| 2.5126 \| 1490 \| 0.1344 \| - \| - \| - \| - \| - \| - \|
	\| 2.5295 \| 1500 \| 0.2819 \| - \| - \| - \| - \| - \| - \|
	\| 2.5464 \| 1510 \| 0.1784 \| - \| - \| - \| - \| - \| - \|
	\| 2.5632 \| 1520 \| 0.1045 \| - \| - \| - \| - \| - \| - \|
	\| 2.5801 \| 1530 \| 0.3959 \| - \| - \| - \| - \| - \| - \|
	\| 2.5970 \| 1540 \| 0.0537 \| - \| - \| - \| - \| - \| - \|
	\| 2.6138 \| 1550 \| 0.2369 \| - \| - \| - \| - \| - \| - \|
	\| 2.6307 \| 1560 \| 0.8336 \| - \| - \| - \| - \| - \| - \|
	\| 2.6476 \| 1570 \| 0.2027 \| - \| - \| - \| - \| - \| - \|
	\| 2.6644 \| 1580 \| 0.3074 \| - \| - \| - \| - \| - \| - \|
	\| 2.6813 \| 1590 \| 0.1481 \| - \| - \| - \| - \| - \| - \|
	\| 2.6981 \| 1600 \| 0.1564 \| - \| - \| - \| - \| - \| - \|
	\| 2.7150 \| 1610 \| 0.5756 \| - \| - \| - \| - \| - \| - \|
	\| 2.7319 \| 1620 \| 0.5477 \| - \| - \| - \| - \| - \| - \|
	\| 2.7487 \| 1630 \| 0.1841 \| - \| - \| - \| - \| - \| - \|
	\| 2.7656 \| 1640 \| 0.6235 \| - \| - \| - \| - \| - \| - \|
	\| 2.7825 \| 1650 \| 0.0891 \| - \| - \| - \| - \| - \| - \|
	\| 2.7993 \| 1660 \| 0.2754 \| - \| - \| - \| - \| - \| - \|
	\| 2.8162 \| 1670 \| 0.2289 \| - \| - \| - \| - \| - \| - \|
	\| 2.8331 \| 1680 \| 0.0992 \| - \| - \| - \| - \| - \| - \|
	\| 2.8499 \| 1690 \| 0.3062 \| - \| - \| - \| - \| - \| - \|
	\| 2.8668 \| 1700 \| 0.094 \| - \| - \| - \| - \| - \| - \|
	\| 2.8836 \| 1710 \| 0.1212 \| - \| - \| - \| - \| - \| - \|
	\| 2.9005 \| 1720 \| 0.1117 \| - \| - \| - \| - \| - \| - \|
	\| 2.9174 \| 1730 \| 0.0695 \| - \| - \| - \| - \| - \| - \|
	\| 2.9342 \| 1740 \| 0.2113 \| - \| - \| - \| - \| - \| - \|
	\| 2.9511 \| 1750 \| 0.4381 \| - \| - \| - \| - \| - \| - \|
	\| 2.9680 \| 1760 \| 0.5537 \| - \| - \| - \| - \| - \| - \|
	\| 2.9848 \| 1770 \| 1.3753 \| - \| - \| - \| - \| - \| - \|
	\| 3.0 \| 1779 \| - \| 0.7922 \| 0.7886 \| 0.7856 \| 0.7752 \| 0.7656 \| 0.7511 \|
	\| 3.0017 \| 1780 \| 0.1847 \| - \| - \| - \| - \| - \| - \|
	\| 3.0185 \| 1790 \| 0.3758 \| - \| - \| - \| - \| - \| - \|
	\| 3.0354 \| 1800 \| 0.3809 \| - \| - \| - \| - \| - \| - \|
	\| 3.0523 \| 1810 \| 0.2109 \| - \| - \| - \| - \| - \| - \|
	\| 3.0691 \| 1820 \| 0.1206 \| - \| - \| - \| - \| - \| - \|
	\| 3.0860 \| 1830 \| 0.2972 \| - \| - \| - \| - \| - \| - \|
	\| 3.1029 \| 1840 \| 0.0778 \| - \| - \| - \| - \| - \| - \|
	\| 3.1197 \| 1850 \| 0.0589 \| - \| - \| - \| - \| - \| - \|
	\| 3.1366 \| 1860 \| 0.166 \| - \| - \| - \| - \| - \| - \|
	\| 3.1535 \| 1870 \| 0.1946 \| - \| - \| - \| - \| - \| - \|
	\| 3.1703 \| 1880 \| 0.2489 \| - \| - \| - \| - \| - \| - \|
	\| 3.1872 \| 1890 \| 0.1384 \| - \| - \| - \| - \| - \| - \|
	\| 3.2040 \| 1900 \| 0.07 \| - \| - \| - \| - \| - \| - \|
	\| 3.2209 \| 1910 \| 0.5017 \| - \| - \| - \| - \| - \| - \|
	\| 3.2378 \| 1920 \| 0.1851 \| - \| - \| - \| - \| - \| - \|
	\| 3.2546 \| 1930 \| 0.1793 \| - \| - \| - \| - \| - \| - \|
	\| 3.2715 \| 1940 \| 0.1809 \| - \| - \| - \| - \| - \| - \|
	\| 3.2884 \| 1950 \| 0.4634 \| - \| - \| - \| - \| - \| - \|
	\| 3.3052 \| 1960 \| 0.4031 \| - \| - \| - \| - \| - \| - \|
	\| 3.3221 \| 1970 \| 0.3377 \| - \| - \| - \| - \| - \| - \|
	\| 3.3390 \| 1980 \| 0.3894 \| - \| - \| - \| - \| - \| - \|
	\| 3.3558 \| 1990 \| 0.2699 \| - \| - \| - \| - \| - \| - \|
	\| 3.3727 \| 2000 \| 0.0361 \| - \| - \| - \| - \| - \| - \|
	\| 3.3895 \| 2010 \| 0.0887 \| - \| - \| - \| - \| - \| - \|
	\| 3.4064 \| 2020 \| 0.1028 \| - \| - \| - \| - \| - \| - \|
	\| 3.4233 \| 2030 \| 0.3571 \| - \| - \| - \| - \| - \| - \|
	\| 3.4401 \| 2040 \| 0.084 \| - \| - \| - \| - \| - \| - \|
	\| 3.4570 \| 2050 \| 0.2129 \| - \| - \| - \| - \| - \| - \|
	\| 3.4739 \| 2060 \| 0.3255 \| - \| - \| - \| - \| - \| - \|
	\| 3.4907 \| 2070 \| 0.097 \| - \| - \| - \| - \| - \| - \|
	\| 3.5076 \| 2080 \| 0.0376 \| - \| - \| - \| - \| - \| - \|
	\| 3.5245 \| 2090 \| 0.1035 \| - \| - \| - \| - \| - \| - \|
	\| 3.5413 \| 2100 \| 0.1985 \| - \| - \| - \| - \| - \| - \|
	\| 3.5582 \| 2110 \| 0.0757 \| - \| - \| - \| - \| - \| - \|
	\| 3.5750 \| 2120 \| 0.1875 \| - \| - \| - \| - \| - \| - \|
	\| 3.5919 \| 2130 \| 0.2383 \| - \| - \| - \| - \| - \| - \|
	\| 3.6088 \| 2140 \| 0.3408 \| - \| - \| - \| - \| - \| - \|
	\| 3.6256 \| 2150 \| 0.1063 \| - \| - \| - \| - \| - \| - \|
	\| 3.6425 \| 2160 \| 0.0859 \| - \| - \| - \| - \| - \| - \|
	\| 3.6594 \| 2170 \| 0.1128 \| - \| - \| - \| - \| - \| - \|
	\| 3.6762 \| 2180 \| 0.1582 \| - \| - \| - \| - \| - \| - \|
	\| 3.6931 \| 2190 \| 0.5578 \| - \| - \| - \| - \| - \| - \|
	\| 3.7099 \| 2200 \| 0.4277 \| - \| - \| - \| - \| - \| - \|
	\| 3.7268 \| 2210 \| 0.1677 \| - \| - \| - \| - \| - \| - \|
	\| 3.7437 \| 2220 \| 0.3124 \| - \| - \| - \| - \| - \| - \|
	\| 3.7605 \| 2230 \| 0.4027 \| - \| - \| - \| - \| - \| - \|
	\| 3.7774 \| 2240 \| 0.4156 \| - \| - \| - \| - \| - \| - \|
	\| 3.7943 \| 2250 \| 0.6655 \| - \| - \| - \| - \| - \| - \|
	\| 3.8111 \| 2260 \| 0.0406 \| - \| - \| - \| - \| - \| - \|
	\| 3.8280 \| 2270 \| 0.0429 \| - \| - \| - \| - \| - \| - \|
	\| 3.8449 \| 2280 \| 0.2318 \| - \| - \| - \| - \| - \| - \|
	\| 3.8617 \| 2290 \| 0.2173 \| - \| - \| - \| - \| - \| - \|
	\| 3.8786 \| 2300 \| 0.1336 \| - \| - \| - \| - \| - \| - \|
	\| 3.8954 \| 2310 \| 0.1048 \| - \| - \| - \| - \| - \| - \|
	\| 3.9123 \| 2320 \| 0.1166 \| - \| - \| - \| - \| - \| - \|
	\| 3.9292 \| 2330 \| 0.6615 \| - \| - \| - \| - \| - \| - \|
	\| 3.9460 \| 2340 \| 0.3252 \| - \| - \| - \| - \| - \| - \|
	\| 3.9629 \| 2350 \| 0.1032 \| - \| - \| - \| - \| - \| - \|
	\| 3.9798 \| 2360 \| 0.1283 \| - \| - \| - \| - \| - \| - \|
	\| 3.9966 \| 2370 \| 0.2071 \| - \| - \| - \| - \| - \| - \|
	\| 4.0 \| 2372 \| - \| 0.7946 \| 0.7933 \| 0.7915 \| 0.7779 \| 0.7727 \| 0.7555 \|

	* The bold row denotes the saved checkpoint.
	</details>

	### Framework Versions
	- Python: 3.11.11
	- Sentence Transformers: 3.4.1
	- Transformers: 4.47.1
	- PyTorch: 2.5.1+cu124
	- Accelerate: 1.2.1
	- Datasets: 3.2.0
	- Tokenizers: 0.21.0

	## Citation

	### BibTeX

	#### Sentence Transformers
	```bibtex
	@inproceedings{reimers-2019-sentence-bert,
	title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
	author = "Reimers, Nils and Gurevych, Iryna",
	booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
	month = "11",
	year = "2019",
	publisher = "Association for Computational Linguistics",
	url = "https://arxiv.org/abs/1908.10084",
	}
	```

	#### MatryoshkaLoss
	```bibtex
	@misc{kusupati2024matryoshka,
	title={Matryoshka Representation Learning},
	author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
	year={2024},
	eprint={2205.13147},
	archivePrefix={arXiv},
	primaryClass={cs.LG}
	}
	```

	#### MultipleNegativesRankingLoss
	```bibtex
	@misc{henderson2017efficient,
	title={Efficient Natural Language Response Suggestion for Smart Reply},
	author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
	year={2017},
	eprint={1705.00652},
	archivePrefix={arXiv},
	primaryClass={cs.CL}
	}
	```

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