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+https://lenta.ru/news/2025/10/10/v-kremle-nazvali-chudovischnymi-zayavleniya-zelenskogo-o-raketah-tomahawk/
+https://lenta.ru/news/2025/10/10/56-letnyaya-supermodel-v-prozrachnom-platie-bez-nizhnego-belya-posetila-balet/
+https://lenta.ru/news/2025/10/10/musorovoz-pereehal-pensionerku-v-rossiyskom-gorode-i-popal-na-video/
+https://lenta.ru/news/2025/10/10/zhenschina-nedelyu-probolela-doma-i-razbogatela-na-vosem-millionov-rubley/
+https://lenta.ru/news/2025/10/10/iphone17-battery/
+https://lenta.ru/news/2025/10/10/v-rossii-raskryli-tseli-udara-po-severu-ukrainy/
+https://lenta.ru/news/2025/10/10/v-rossiyskom-gorode-poymali-napavshego-na-devushku-na-pustyre-manyaka/
+https://lenta.ru/news/2025/10/10/byvshiy-sovladelets-banka-oktrytie-obvinen-v-hischenii-34-milliardov-rubley-iz-banka/
+https://lenta.ru/news/2025/10/10/opublikovan-obnovlennyy-reyting-putina-sredi-rossiyan/
+https://lenta.ru/news/2025/10/10/rabotodateli-stali-chasche-upominat-odin-navyk-pri-poiske-sotrudnikov/
+https://lenta.ru/news/2025/10/10/stali-izvestny-podrobnosti-taynogo-vozvrascheniya-plennyh-rossiyskih-snayperov-iz-plena-vsu/
+https://lenta.ru/news/2025/10/10/raskryta-detal/
+https://lenta.ru/news/2025/10/10/zarubivshiy-toporom-pyateryh-chelovek-izhevskiy-raskolnikov-vyshel-na-svobodu/
+https://lenta.ru/news/2025/10/10/passazhir-samoleta-razdelsya-i-popytalsya-iznasilovat-poputchitsu-vo-vremya-poleta/
+https://lenta.ru/news/2025/10/10/rossiyanin-povstrechalsya-s-kitayankami-i-opisal-otnosheniya-s-nimi-slovami-eto-bylo-udushayusche/
+https://lenta.ru/news/2025/10/10/putin-progulyalsya-v-kompanii-alieva-i-drugih-liderov-sng-i-popal-na-video/
+https://lenta.ru/news/2025/10/10/killer-s-posobnikom-za-300-dollarov-raspravilis-s-direktorom-litseya/
+https://lenta.ru/news/2025/10/10/glava-minzdrava-ssha-rasskazal-o-svyazi-mezhdu-obrezaniem-i-autizmom/
+https://lenta.ru/news/2025/10/10/prezidenta-odnoy-strany-otstranili-ot-dolzhnosti/
+https://lenta.ru/news/2025/10/10/alesya-kafelnikova-v-poluprozrachnoy-yubke-prokatilas-na-loshadi/
+https://lenta.ru/news/2025/10/10/vrach-raskryla-dva-deystvennyh-sposoba-umenshit-podtekaniya-mochi/
+https://lenta.ru/news/2025/10/10/malkin-vyshel-na-30-e-mesto-v-spiske-luchshih-bombardirov-v-istorii-nhl/
+https://lenta.ru/news/2025/10/10/byvshaya-koroleva-krasoty-popalas-s-358-kilogrammami-narkotikov-i-izbezhala-tyurmy/
+https://lenta.ru/news/2025/10/10/fedoseevu-shukshinu-ekstrenno-gospitalizirovali-v-moskve/
+https://lenta.ru/news/2025/10/10/kaprizov-sdelal-assistentskiy-het-trik-v-matche-nhl/
+https://lenta.ru/news/2025/10/10/izvestnyy-komik-perenes-srochnuyu-operatsiyu-i-nazval-ee-uzhasayuschey/
+https://lenta.ru/news/2025/10/10/yak-130m/
+https://lenta.ru/news/2025/10/10/rossiya-raskryla-otnoshenie-k-nobelevskoy-premii-mira-dlya-trampa/
+https://lenta.ru/news/2025/10/10/amerikanets-rasskazal-o-mechte-stat-grazhdaninom-rossii/
+https://lenta.ru/news/2025/10/10/putin-i-aliev-obnyalis-na-neformalnoy-vstreche/
+https://lenta.ru/news/2025/10/10/viktoriya-bekhem-ob-yasnila-ugryumoe-vyrazhenie-litsa-na-krasnyh-dorozhkah/
+https://lenta.ru/news/2025/10/10/rossiyanka-pobyvala-v-sochi-i-opisala-otdyh-na-kurorte-slovami-plyazhnyy-ad/
+https://lenta.ru/news/2025/10/10/no-hands/
+https://lenta.ru/news/2025/10/10/fon-der-lyayen-dali-shest-mesyatsev-na-ispravlenie-svoih-oshibok/
+https://lenta.ru/news/2025/10/10/srednie-pensii-v-rossii-proseli-otnositelno-zarplat/
+https://lenta.ru/news/2025/10/10/millionam-rossiyan-predskazali-uho-v-ten/
+https://lenta.ru/news/2025/10/10/raskryt-podhod-trampa-k-uregulirovaniyu-konflikta-v-gaze/
+https://lenta.ru/news/2025/10/10/v-moskve-ischez-svet/
+https://lenta.ru/news/2025/10/10/na-ukraine-perekryli-dvizhenie-po-dambe-dneproges/
+https://lenta.ru/news/2025/10/10/v-rossii-predrekli-otkaz-ssha-ot-otvetstvennosti-za-primenenie-raket-tomahawk/
+https://lenta.ru/news/2025/10/10/v-dnepropetrovske-nachalsya-pozhar-posle-vzryvov/
+https://lenta.ru/news/2025/10/10/vs-rossii-unichtozhili-obstrelivavshiy-belgorodskuyu-oblast-raschet-bpla/
+https://lenta.ru/news/2025/10/09/v-rossii-izmenilis-tseny-na-zhilie/
+https://lenta.ru/news/2025/10/10/vrach-otsenil-trend-na-uvelichenie-klitora/
+https://lenta.ru/news/2025/10/10/amerikanskiy-polkovnik-zayavil-o-provale-podgotovki-evropy-k-voyne-s-rossiey/
+https://lenta.ru/news/2025/10/10/v-gosdume-rasskazali-o-vliyanii-uchastnikov-svo-na-posleduyuschie-pokoleniya/
+https://lenta.ru/news/2025/10/10/ssha-nanesli-serieznyy-uron-ekonomike-svoego-soyuznika/
+https://lenta.ru/news/2025/10/10/otets-oblil-pertsovkoy-obidchikov-syna-vozle-shkoly-v-rossiyskom-regione/
+https://lenta.ru/news/2025/10/10/u-eks-glavy-rossiyskoy-stroitelnoy-kompanii-nashli-osobuyu-cherno-beluyu-knigu/
+https://lenta.ru/news/2025/10/10/starmer-vyskazalsya-o-sudbe-uvolennogo-iz-za-svyazey-s-epshteynom-posla-britanii/
+https://lenta.ru/news/2025/10/10/partner-po-briks-narastil-postavki-myasa-rossii/
+https://lenta.ru/news/2025/10/10/natsistskoe-nasledie-oslozhnilo-germanii-borbu-s-dronami/
+https://lenta.ru/news/2025/10/10/rossiyskie-voyska-za-sutki-prodvinulis-v-dnr/
+https://lenta.ru/news/2025/10/10/rossiyskiy-boets-rasskazal-o-prorytom-vsu-tunnele/
+https://lenta.ru/news/2025/10/10/raskryta-shema-sokrytiya-nalogov-na-260-millionov-eks-glavoy-rossiyskoy-stroykompanii/
+https://lenta.ru/news/2025/10/10/v-gosdume-zahoteli-zakonodatelno-ogranichit-chislo-koshek-i-sobak-v-kvartire/
+https://lenta.ru/news/2025/10/10/v-rossii-rasskazali-o-vozmozhnoy-nezakonnoy-voenno-biologicheskoy-deyatelnosti-stran-nato/
+https://lenta.ru/news/2025/10/10/voenkory-soobschili-o-novyh-udarah-po-kievu/
+https://lenta.ru/news/2025/10/10/minprosvescheniya-ustanovilo-sroki-polucheniya-nachalnogo-obschego-obrazovaniya/
+https://lenta.ru/news/2025/10/10/raskryty-roli-spetsposlannika-i-zyatya-trampa-v-uregulirovanii-konflikta-v-gaze/
+https://lenta.ru/news/2025/10/10/perespavshaya-s-tysyachey-muzhchin-pornozvezda-bonni-blyu-otsenila-privlekatelnost-trampa/
+https://lenta.ru/news/2025/10/10/v-rossii-otvetili-na-vopros-o-nobelevskoy-premii-mira-dlya-trampa/
+https://lenta.ru/news/2025/10/10/na-ukraine-ivana-susanina-priznali-propagandoy-rossiyskogo-imperializma/
+https://lenta.ru/news/2025/10/10/strana-briks-minimizirovala-postavki-kofe-v-rossiyu/
+https://lenta.ru/news/2025/10/10/stalo-izvestno-o-planah-kieva-dostavit-patriot-v-harkov/
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+https://lenta.ru/news/2025/10/10/rossiyanka-ukrala-iz-elektrichki-sumku-s-dengami-i-popala-na-video/
+https://lenta.ru/news/2025/10/10/moment-pogruzivshego-kiev-vo-tmu-udara-rakety-popal-na-video/
+https://lenta.ru/news/2025/10/10/rossiya-popala-v-spisok-stran-s-samymi-deshevymi-yaytsami/
+https://lenta.ru/news/2025/10/10/nazvany-tseli-nochnyh-udarov-po-kievu/
+https://lenta.ru/news/2025/10/10/s-nachala-2025-goda-iz-vsu-dezertirovali-bolee-150-tysyach-chelovek/
+https://lenta.ru/news/2025/10/10/raskryty-podrobnosti-o-nochnyh-atakah-vsu-na-rossiyu/
+https://lenta.ru/news/2025/10/10/obnaruzhen-sposob-prokachat-immunitet-bez-lekarstv/
+https://lenta.ru/news/2025/10/10/prodazham-kitayskih-legkovushek-predskazali-obrushenie/
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+https://lenta.ru/news/2025/10/10/v-partii-zelenskogo-zahoteli-snyat-klichko-s-posta-mera-kieva/
+https://lenta.ru/news/2025/10/10/risk-vozniknoveniya-paniki-iz-za-tsen-na-sahar-otsenili/
+https://lenta.ru/news/2025/10/10/v-rossii-otreagirovali-na-prizyv-evroparlamenta-po-rossiyskim-aktivam/
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+https://lenta.ru/news/2025/10/10/marochko-rasskazal-ob-otvode-komandovaniya-vsu-iz-severska/
+https://lenta.ru/news/2025/10/10/energosistema-na-grani-kollapsa-armiya-rossii-vyvela-iz-stroya-klyuchevye-ges-i-tes-ukrainy-goroda-pogruzilis-vo-tmu/
+https://lenta.ru/news/2025/10/10/kartavyh/
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+https://lenta.ru/news/2025/10/10/deputat-rady-nazval-bessmyslennymi-prizyvy-ustroit-blekaut-v-rossii/
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+https://lenta.ru/news/2025/10/10/dreyfovavshiy-dva-mesyatsa-v-ohotskom-more-rossiyanin-poydet-pod-sud/
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+https://lenta.ru/news/2025/10/10/stoit-vykinut-tramp-predlozhil-isklyuchit-ispaniyu-iz-nato-kakaya-pretenziya-u-prezidenta-ssha-k-evropeyskoy-strane/
+https://lenta.ru/news/2025/10/10/ushedshiy-iz-rossii-tehnologicheskiy-gigant-zaregistriroval-v-strane-tovarnyy-znak/
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+https://lenta.ru/news/2025/10/09/rossiyskie-voennye-likvidirovali-amerikanskogo-naemnika-iz-azova/
+https://lenta.ru/news/2025/10/09/v-peterburge-peredumali-ubirat-ulitsy-robotami/
+https://lenta.ru/news/2025/10/09/vuchich-porassuzhdal-o-vine-rossii-za-sanktsii-ssha/
+https://lenta.ru/news/2025/10/09/new-shepard-sovershil-shestoy-pilotiruemyy-polet-v-2025-godu/
+https://lenta.ru/news/2025/10/09/u-pokinuvshego-rossiyu-proizvoditelya-gazirovki-upala-pribyl/
+https://lenta.ru/news/2025/10/09/muzhchina-s-el-tri-kilogramma-marmelada-za-tri-dnya-i-popal-v-bolnitsu/
+https://lenta.ru/news/2025/10/09/iz-rossiyskogo-vuza-evakuirovali-neskolko-soten-studentov/
+https://lenta.ru/news/2025/10/09/inostrannyy-lider-pozdravil-putina-na-russkom-yazyke/
+https://lenta.ru/news/2025/10/09/ubezhdavshie-shkolnikov-oformlyat-karty-dlya-moshennikov-rossiyane-stali-figurantami-dela/
+https://lenta.ru/news/2025/10/09/anita-tsoy-rasskazala-o-borbe-s-depressiey/
+https://lenta.ru/news/2025/10/09/nazvany-lyubimye-strany-rossiyskih-millenialov/
+https://lenta.ru/news/2025/10/09/nayden-sposob-mgnovenno-opredelit-tuberkulez-bez-analizov/
+https://lenta.ru/news/2025/10/09/ukrainskaya-nalogovaya-sluzhba-nachala-pisat-onlifanschitsam/
+https://lenta.ru/news/2025/10/09/jan-kholod/
+https://lenta.ru/news/2025/10/09/stilisty-nazvali-snimayuschie-ustalost-tsveta-v-odezhde/
+https://lenta.ru/news/2025/10/09/trener-vashingtona-otsenil-igru-ovechkina-v-pervom-matche-novogo-sezona-nhl/
+https://lenta.ru/news/2025/10/09/izvestnuyu-lgbt-aktivistku-vnesli-v-reestr-terroristov-v-rossii/
+https://lenta.ru/news/2025/10/09/raskryta-summa-vyplat-uchastnikam-svo-i-ih-semyam/
+https://lenta.ru/news/2025/10/09/proniknovenie-fanata-v-kvartiru-rossiyskoy-pevitsy-nazvali-piar-hodom/
+https://lenta.ru/news/2025/10/09/samoylova-razvedetsya-s-dzhiganom/
+https://lenta.ru/news/2025/10/09/predskazano-vliyanie-vstrechi-putina-i-alieva-na-otnosheniya-rossii-i-azerbaydzhana/
+https://lenta.ru/news/2025/10/09/v-mahachkale-izbrali-novogo-mera/
+https://lenta.ru/news/2025/10/09/rezultativnost-rossiyskoy-zanozy-v-zone-svo-raskryli/
+https://lenta.ru/news/2025/10/09/v-rossiyskom-gorode-vybrali-novogo-mera/
+https://lenta.ru/news/2025/10/09/msk-zima/
+https://lenta.ru/news/2025/10/09/uitaker-rasskazal-o-esche-ne-peredannom-ukraine-moschnom-oruzhii-ssha/
+https://lenta.ru/news/2025/10/09/vstrecha-putina-i-alieva-zavershilas/
+https://lenta.ru/news/2025/10/09/kandidat-v-premiery-chehii-babish-anonsiroval-vizit-na-ukrainu/
+https://lenta.ru/news/2025/10/09/okeanolog-usomnilsya-v-dostovernosti-prognozov-ob-izmeneniyah-v-mirovom-okeane/
+https://lenta.ru/news/2025/10/09/arheologi-nashli-redkiy-kamennyy-molot-vozrastom-devyat-tysyach-let/
+https://lenta.ru/news/2025/10/09/mishustin-rasskazal-o-posobii-dlya-samozanyatyh/
+https://lenta.ru/news/2025/10/09/v-moskovskom-kafe-muzhchina-s-lopatoy-pohitil-konstruktor-dlya-syna-i-popal-na-video/
+https://lenta.ru/news/2025/10/09/babkina-posovetovala-rossiyanam-borotsya-s-osenney-handroy-uborkoy/
+https://lenta.ru/news/2025/10/09/nazvana-bezopasnaya-sutochnaya-doza-kofe/
+https://lenta.ru/news/2025/10/09/nachalnitsu-sudebnyh-pristavov-etapiruyut-v-moskvu/
+https://lenta.ru/news/2025/10/09/stalo-izvestno-o-nedvizhimosti-bulanovoy-v-moskve-i-peterburge/
+https://lenta.ru/news/2025/10/09/msk-dozhdi/
+https://lenta.ru/news/2025/10/09/v-rossiyskom-regione-zhiteli-pozhalovalis-na-razryvayuschih-domashnih-zhivotnyh-sobak/
+https://lenta.ru/news/2025/10/09/iphone-fold/
+https://lenta.ru/news/2025/10/09/eks-premier-ukrainy-predrek-territorialnoe-buduschee-strany/
+https://lenta.ru/news/2025/10/09/zakon-o-shtrafah-za-poisk-ekstremistskih-materialov-mogut-izmenit/
+https://lenta.ru/news/2025/10/09/mnogodetnaya-mat-iz-moskvy-podderzhala-vsu-i-popala-v-sizo/
+https://lenta.ru/news/2025/10/09/polzovateli-max-smogut-sozdat-tsifrovoy-id-po-voditelskomu-udostovereniyu/
+https://lenta.ru/news/2025/10/09/moskovskiy-dvornik-podralsya-s-derevom-i-popal-na-video/
+https://lenta.ru/news/2025/10/09/umer-zvezda-shansona-efrem-amiramov-ispolnitelyu-hita-molodaya-bylo-69-let/
+https://lenta.ru/news/2025/10/09/v-sovfede-nazvali-realnogo-sponsora-terrorizma/
+https://lenta.ru/news/2025/10/09/sanktsii-ssha-udarili-po-postavkam-nefti-v-serbiyu/
+https://lenta.ru/news/2025/10/09/v-rossii-desyatki-krepkih-muzhchin-v-chernom-v-balaklavah-i-s-nashivkami-vnov-vyshli-na-ulitsy/
+https://lenta.ru/news/2025/10/09/stalo-izvestno-o-smerti-rossiyanina-na-polu-v-tualete-bolnitsy/
+https://lenta.ru/news/2025/10/09/bitva/
+https://lenta.ru/news/2025/10/09/sobirayuschimsya-v-kitay-rossiyanam-dali-odin-sovet/
+https://lenta.ru/news/2025/10/09/postavschikam-kompleksov-razminirovaniya-dlya-rosgvardii-nazvali-srok/
+https://lenta.ru/news/2025/10/09/v-rossii-zayavili-o-silnom-otstavanii-v-kosmonavtike/
+https://lenta.ru/news/2025/10/09/byvshaya-zhena-mamaeva-proigrala-v-borbe-za-elitnye-apartamenty-eks-muzha/
+https://lenta.ru/news/2025/10/09/zelenskiy-pogovoril-s-kritikovavshim-pomosch-kievu-pobeditelem-vyborov-v-chehii/
+https://lenta.ru/news/2025/10/09/guzeeva-otvetila-na-posvyaschennyy-ey-vypusk-programmy-kudryavtsevoy-i-sochla-ee-nezdorovoy/
+https://lenta.ru/news/2025/10/09/tur-rossiyskoy-repershi-okazalsya-pod-ugrozoy-iz-za-propagandy-lgbt/
+https://lenta.ru/news/2025/10/09/novuyu-strizhku-zheny-dzhordzha-kluni-podnyali-na-smeh-v-seti/
+https://lenta.ru/news/2025/10/09/ukhodi/
+https://lenta.ru/news/2025/10/09/rukovodstvo-ob-yasnilos-posle-organizatsii-dnya-beremennyh-v-rossiyskom-detsadu/
+https://lenta.ru/news/2025/10/09/iz-kanalizatsii-odnogo-goroda-izvlekli-zhirberg-vesom-100-tonn/
+https://lenta.ru/news/2025/10/09/skoruyu-vyzvali-v-sud-posle-slov-prokurora-po-delu-ob-obmane-rosgvardii-na-283-milliona/
+https://lenta.ru/news/2025/10/09/evroparlament-prizval-nemedlenno-konfiskovat-rossiyskie-aktivy/
+https://lenta.ru/news/2025/10/09/putin-raskryl-novuyu-detal-v-krushenii-samoleta-azal/
+https://lenta.ru/news/2025/10/09/putin-raskryl-alievu-podrobnosti-krusheniya-samoleta-azal-iz-za-chego-proizoshla-aviakatastrofa/
+https://lenta.ru/news/2025/10/09/moskvichey-prizvali-ne-boyatsya-letuchih-myshey/
+https://lenta.ru/news/2025/10/09/aliev-ob-yavil-uspehe-otnosheniy-s-rossiey-v-2025-godu/
+https://lenta.ru/news/2025/10/09/nazvana-vozmozhnaya-prichina-pozhara-v-dome-na-kolesah-sgorevshego-zazhivo-veterana-svo/
+https://lenta.ru/news/2025/10/10/zhenschina-s-chetvertoy-stadiey-raka-rasskazala-o-neochevidnyh-simptomah-zabolevaniya/
+https://lenta.ru/news/2025/10/10/rossiyskaya-aviakompaniya-narushila-prava-passazhirov-i-poplatilas/
+https://lenta.ru/news/2025/10/10/stali-izvestny-plany-ssha-posle-pervogo-etapa-mirnogo-plana-po-gaze/
+https://lenta.ru/news/2025/10/10/vengriya-prizvala-vseh-zapomnit-odin-vazhnyy-urok/
+https://lenta.ru/news/2025/10/10/gospitalizirovan-byvshiy-sudya-verhovnogo-suda-rossii-momotov/
+https://lenta.ru/news/2025/10/10/rossiyanka-nashla-syna-podrostka-v-komnate-mertvym-i-s-ozhogom-na-grudi/
+https://lenta.ru/news/2025/10/10/v-krymu-vskryli-peredavavshego-informatsiyu-o-korablyah-rossiyanina/
+https://lenta.ru/news/2025/10/10/peskov-obvinil-kiev-v-ostanovke-peregovornogo-protsessa/
+https://lenta.ru/news/2025/10/10/gendirektor-dtek-zayavil-o-nesposobnosti-pogruzit-ukrainu-vo-tmu-pered-nachalom-blekauta/
+https://lenta.ru/news/2025/10/10/zelenskiy-rasskazal-o-nochnoy-atake-po-ukraine/

src/tokenizers_analysis.py

@@ -0,0 +1,89 @@
+import json
+from nltk.tokenize import word_tokenize
+from razdel import tokenize as razdel_tokenize
+from snowballstemmer import RussianStemmer
+import pymorphy2
+import re
+import time
+from sentence_transformers import SentenceTransformer
+from sklearn.metrics.pairwise import cosine_similarity
+
+
+def tokenize_naive_space(text):
+    return text.split(' ')
+
+
+def tokenize_regex(text):
+    return re.findall(r'[а-яА-ЯёЁ]+', text)
+
+
+def tokenize_nltk(text):
+    return word_tokenize(text, language='russian')
+
+
+def tokenize_razdel(text):
+    return list(map(lambda x: x.text, razdel_tokenize(text)))
+
+
+def stem_snowball(tokens):
+    stemmer = RussianStemmer()
+    return stemmer.stemWords(tokens)
+
+
+def lemmatize_pymorphy(words):
+    morph = pymorphy2.MorphAnalyzer()
+    lemmas = []
+    for word in words:
+        lemmas.append(morph.parse(word)[0].normal_form)
+    return lemmas
+
+
+def calculate_oov(text, vocabulary):
+    words = text.split(' ')
+    oov_count = 0
+    for word in words:
+        if word not in vocabulary:
+            oov_count += 1
+    return oov_count / len(words)
+
+
+def calculate_similarity(text1, text2, model):
+    embedding1 = model.encode(text1, convert_to_tensor=False).reshape(1, -1)
+    embedding2 = model.encode(text2, convert_to_tensor=False).reshape(1, -1)
+    return cosine_similarity(embedding1, embedding2)[0][0]
+
+
+if __name__ == '__main__':
+    texts = []
+    with open('core/preprocessed_core.jsonl', encoding='utf-8') as file:
+        for line in file:
+            row = json.loads(line)
+            texts.append(row['text'])
+    n_articles = len(texts)
+    tokenizers = [tokenize_naive_space, tokenize_regex, tokenize_nltk, tokenize_razdel]
+
+    methods = []
+    for tokenizer in tokenizers:
+        methods.extend([[tokenizer], [tokenizer, stem_snowball], [tokenizer, lemmatize_pymorphy]])
+
+    csv_string = 'method;vocabulary volume;OOV percentage;processing speed;semantic consistency\n'
+    sim_model = SentenceTransformer('all-MiniLM-L6-v2')
+    for method in methods:
+        print('running', " + ".join(map(lambda x: x.__name__, method)))
+        start_time = time.time()
+        vocabulary = set()
+        similarities = []
+        for text in texts:
+            tokens = text
+            for func in method:
+                tokens = func(tokens)
+            similarities.append(calculate_similarity(text, ' '.join(tokens), sim_model))
+            with open(f'tokenized_texts/{"_".join(map(lambda x: x.__name__, method))}', 'a', encoding='utf-8') as file:
+                file.write(' '.join(tokens) + '\n')
+            vocabulary = vocabulary.union(tokens)
+        end_time = time.time()
+        csv_string += f'{" + ".join(map(lambda x: x.__name__, method))};{len(vocabulary)};'
+        csv_string += f'{calculate_oov(" ".join(texts), vocabulary)};{end_time - start_time};'
+        csv_string += f'{sum(similarities)/len(similarities)}\n'
+    with open('reports/tokenization_metrics.csv', 'w') as file:
+        file.write(csv_string)

src/tokenizers_trainer.py

@@ -0,0 +1,239 @@
+import os
+import re
+import json
+
+CORPUS_FILE = 'core/united_core.txt'
+VOCAB_SIZE = 10000
+OUTPUT_DIR = 'tokenizers'
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+
+
+def simple_tokenize(text):
+    return re.findall(r'\S+', text)
+
+
+def train_bpe(vocab_size, min_freq, corpus_path=None):
+    from tokenizers import Tokenizer
+    from tokenizers.models import BPE
+    from tokenizers.trainers import BpeTrainer
+    from tokenizers.pre_tokenizers import Whitespace
+
+    tokenizer = Tokenizer(BPE(unk_token='<UNK>'))
+    tokenizer.pre_tokenizer = Whitespace()
+    trainer = BpeTrainer(
+        vocab_size=vocab_size,
+        min_frequency=min_freq,
+        special_tokens=['<UNK>', '<NUM>', '<URL>', '<EMAIL>'],
+        continuing_subword_prefix='',
+    )
+    tokenizer.train(files=[corpus_path if corpus_path else CORPUS_FILE], trainer=trainer)
+    dir_path = f'{OUTPUT_DIR}/bpe_v{vocab_size//1000}k_f{min_freq}'
+    os.makedirs(dir_path, exist_ok=True)
+    tokenizer.save(os.path.join(dir_path, 'tokenizer.json'))
+    tokenizer_config = {
+        "added_tokens_decoder": {},
+        "unk_token": "<UNK>",
+        "cls_token": None,
+        "sep_token": None,
+        "mask_token": None,
+        "model_max_length": 512,
+    }
+    for token in ['<UNK>', '<NUM>', '<URL>', '<EMAIL>']:
+        t_id = str(tokenizer.encode(token).ids[0])
+        tokenizer_config['added_tokens_decoder'][t_id] = {
+            "content": token,
+            "lstrip": False,
+            "normalized": False,
+            "rstrip": False,
+            "single_word": False,
+            "special": True
+        }
+    with open(os.path.join(dir_path, "tokenizer_config.json"), "w", encoding="utf-8") as file:
+        json.dump(tokenizer_config, file, indent=2)
+
+    special_tokens_map = {
+        "unk_token": "<UNK>",
+    }
+    with open(os.path.join(dir_path, "special_tokens_map.json"), "w", encoding="utf-8") as file:
+        json.dump(special_tokens_map, file, indent=2)
+
+    return tokenizer
+
+
+def train_wordpiece(vocab_size, min_freq, corpus_path=None):
+    from tokenizers import Tokenizer
+    from tokenizers.models import WordPiece
+    from tokenizers.trainers import WordPieceTrainer
+    from tokenizers.pre_tokenizers import Whitespace
+
+    tokenizer = Tokenizer(WordPiece(unk_token='<UNK>'))
+    tokenizer.pre_tokenizer = Whitespace()
+    trainer = WordPieceTrainer(
+        vocab_size=vocab_size,
+        min_frequency=min_freq,
+        special_tokens=['<UNK>', '<NUM>', '<URL>', '<EMAIL>'],
+        continuing_subword_prefix='',
+    )
+    tokenizer.train(files=[corpus_path if corpus_path else CORPUS_FILE], trainer=trainer)
+    path = f'{OUTPUT_DIR}/wordpiece_v{vocab_size}_f{min_freq}.json'
+    tokenizer.save(path)
+    return tokenizer
+
+
+def train_unigram(vocab_size, min_freq, corpus_path=None):
+    import sentencepiece as spm
+
+    model_prefix = f'{OUTPUT_DIR}/unigram_v{vocab_size}_f{min_freq}'
+    try:
+        spm.SentencePieceTrainer.train(
+            input=corpus_path if corpus_path else CORPUS_FILE,
+            model_prefix=model_prefix,
+            vocab_size=vocab_size,
+            model_type='unigram',
+            character_coverage=0.9995,
+            pad_id=0, unk_id=1, bos_id=-1, eos_id=-1,
+            user_defined_symbols='<NUM>,<URL>,<EMAIL>',
+            shuffle_input_sentence=True,
+            input_sentence_size=100000,
+            normalization_rule_name='nmt_nfkc',
+            num_threads=8
+        )
+        sp = spm.SentencePieceProcessor()
+        sp.load(f'{model_prefix}.model')
+        return sp
+    except RuntimeError as e:
+        raise e
+
+
+def fragmentation_rate(tokenizer_func, texts):
+    total_words = 0
+    fragmented = 0
+    for text in texts:
+        words = simple_tokenize(text)
+        for word in words:
+            tokens = tokenizer_func(word)
+            total_words += 1
+            if len(tokens) > 1:
+                fragmented += 1
+    return fragmented / total_words if total_words else 0
+
+
+def compression_ratio(tokenizer_func, texts):
+    total_syms = 0
+    total_tokens = 0
+    for text in texts:
+        tokens = tokenizer_func(text)
+        total_syms += len(text)
+        total_tokens += len(tokens)
+    return total_syms / total_tokens if total_tokens else 0
+
+
+def reconstruction_accuracy(tokenizer_obj, texts, model_type='hf'):
+    reconstructed_ok = 0
+    total_words = 0
+
+    for text in texts:
+        words = simple_tokenize(text)
+        for word in words:
+            total_words += 1
+            try:
+                if model_type == 'hf':
+                    tokens = tokenizer_obj.encode(word).tokens
+                    decoded = tokenizer_obj.decode(
+                        tokenizer_obj.encode(word).ids,
+                        skip_special_tokens=True
+                    )
+                    cleaned_decoded = re.sub(r'\s+', '', decoded.lower())
+                    cleaned_word = re.sub(r'\s+|[^\w]', '', word.lower())
+                    if cleaned_decoded == cleaned_word:
+                        reconstructed_ok += 1
+                    else:
+                        pass
+
+                elif model_type == 'sp':
+                    pieces = tokenizer_obj.encode_as_pieces(word)
+                    decoded = ''.join(pieces).replace('▁', '')
+                    cleaned_decoded = re.sub(r'\s+|[^\w]', '', decoded.lower())
+                    cleaned_word = re.sub(r'\s+|[^\w]', '', word.lower())
+                    if cleaned_decoded == cleaned_word:
+                        reconstructed_ok += 1
+
+            except:
+                pass
+
+    return reconstructed_ok / total_words if total_words else 0
+
+
+if __name__ == '__main__':
+
+    texts = []
+    with open('core/united_core.txt', encoding='utf-8') as file:
+        texts = file.readlines()
+
+    vocab_sizes = [8000, 16000, 32000]
+    min_freqs = [2, 3, 4, 5]
+
+    results = []
+
+    for vocab_size in vocab_sizes:
+        for min_freq in min_freqs:
+            print(f'vocab_size {vocab_size} min_freq {min_freq}')
+
+            try:
+                bpe = train_bpe(vocab_size, min_freq)
+                bpe_func = lambda x: bpe.encode(x).tokens
+                bpe_frag = fragmentation_rate(bpe_func, texts)
+                bpe_comp = compression_ratio(bpe_func, texts)
+                bpe_recon = reconstruction_accuracy(bpe, texts, model_type='hf')
+                results.append({
+                    'model': 'BPE',
+                    'vocab_size': vocab_size,
+                    'min_freq': min_freq,
+                    'fragmentation_rate': bpe_frag,
+                    'compression_ratio': bpe_comp,
+                    'reconstruction_acc': bpe_recon
+                })
+            except Exception as e:
+                print(f'BPE error: {e}')
+
+            try:
+                wp = train_wordpiece(vocab_size, min_freq)
+                wp_func = lambda x: wp.encode(x).tokens
+                wp_frag = fragmentation_rate(wp_func, texts)
+                wp_comp = compression_ratio(wp_func, texts)
+                wp_recon = reconstruction_accuracy(wp, texts, model_type='hf')
+                results.append({
+                    'model': 'WordPiece',
+                    'vocab_size': vocab_size,
+                    'min_freq': min_freq,
+                    'fragmentation_rate': wp_frag,
+                    'compression_ratio': wp_comp,
+                    'reconstruction_acc': wp_recon
+                })
+            except Exception as e:
+                print(f'WordPiece error: {e}')
+
+            try:
+                unigram = train_unigram(vocab_size, min_freq)
+                if unigram is not None:
+                    uni_func = lambda x: unigram.encode_as_pieces(x)
+                    uni_frag = fragmentation_rate(uni_func, texts)
+                    uni_comp = compression_ratio(uni_func, texts)
+                    uni_recon = reconstruction_accuracy(unigram, texts, model_type='sp')
+
+                    results.append({
+                        'model': 'Unigram',
+                        'vocab_size': vocab_size,
+                        'min_freq': min_freq,
+                        'fragmentation_rate': uni_frag,
+                        'compression_ratio': uni_comp,
+                        'reconstruction_acc': uni_recon
+                    })
+            except Exception as e:
+                print(f'Unigram error: {e}')
+
+    with open('reports/hf_sp_metrics.csv', 'w') as file:
+        file.write('model;vocab_size;min_freq;fragmentation_rate;compression_ratio;reconstruction_accuracy\n')
+        for r in results:
+            file.write(f'{r["model"]};{r["vocab_size"]};{r["min_freq"]};{round(r["fragmentation_rate"], 3)};')
+            file.write(f'{round(r["compression_ratio"], 3)};{round(r["reconstruction_acc"], 3)}\n')