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  "aboutHtml": "<p>all-MiniLM-L12-v2 — gaplar va qisqa paragraflar uchun Sentence Transformers enkoderi. U semantik qidiruv, ma’lumot qidirish, klasterlash va gaplar o‘xshashligi uchun har matndan bitta 384 o‘lchamli zich vektor hosil qiladi. Model MiniLM-L12-H384-uncased asosidan foydalanadi va bir milliard gap jufti to‘plamida kontrastiv o‘rganish bilan nozik sozlangan.</p><h4>Topmoqchi bo‘lgan birlikni kodlang</h4><p>Bitta foydali fikrni ifodalaydigan gap yoki qisqa paragrafdan foydalaning. Mahsulot tavsiflari to‘plamida buyumni farqlaydigan xususiyat va shartni saqlang: “The adapter connects a wired headset to a USB-C port” iborasi “Audio accessory.”dan ko‘proq ma’lumot beradi. Yozuv identifikatori va asl tavsifni vektor yonida saqlang.</p><p>Keyin qidiruv so‘rovini kodlangan tavsiflar bilan taqqoslash mumkin. Yakuniy tanlovni ko‘rsatishdan oldin qaytgan nomzodlarni ulagich turi yoki qurilma mosligi kabi aniq talablar bo‘yicha tekshiring. Vektor matn ma’nosini ifodalaydi; tashqi qidiruv ilovasi esa tegishli yozuvlarni topadi.</p><h4>O‘rtacha pooling va normallangan vektorlar</h4><p>E’lon qilingan kodlash misoli to‘ldiruvchi pozitsiyalar o‘rtachaga qo‘shilmasligi uchun diqqat niqobi bilan token ifodalarining o‘rtachasini oladi. Keyin L2 normallashni qo‘llaydi. Bu bosqichlar asosiy transformerdan ixtiyoriy token olish ko‘rsatmasi emas, gap vektorini olishning rasmiy usulining bir qismidir.</p><p>Indekslangan matnlar va yangi so‘rovlar o‘rtasida model, pooling va normallash konfiguratsiyasi izchil bo‘lsin. Hosil bo‘lgan 384 komponentli ifodalarni kosinus o‘xshashligi bilan taqqoslash mumkin. Konfiguratsiya yozuvini to‘plam bilan saqlang; boshqa 384 o‘lchamli enkoder faqat vektorlar uzunligi bir xil bo‘lgani uchun mos bo‘lib qolmaydi.</p><h4>Odatiy kirish uzunligi va o‘qitish qamrovi</h4><p>Ishlab chiqaruvchi 256 ta word piece birligidan ortiq qismning odatiy kesilishini belgilaydi. Bu oddiy so‘z yoki belgilar soni emas, tokenizer chegarasidir. Uzunroq yozuvda izchil qisqa bo‘limlar tanlang va har birini talqin qilishga kerakli sarlavha yoki aniqlovchi tafsilotni saqlang.</p><p>Modelning o‘qitish jarayoni 128 tokengacha ketma-ketliklardan foydalangan. Bu o‘qitish sozlamasi kodlash paytidagi e’lon qilingan 256 ta word piece birlikli odatiy sozlamadan alohida. Odatiy chegaraga sig‘adigan parcha ham o‘qitishdagi qisqa gap materialidan sezilarli farq qilishi mumkin; uzun yoki maxsus tavsiflarni namunaviy misollarda baholang.</p><h4>O‘xshashlik va klasterlash jarayonlari</h4><p>Qo‘llab-quvvatlash xabarlari to‘plamida embeddinglar guruhlash bosqichiga raqamli kirishlar berishi mumkin. Odam o‘qiydigan yorliq tayinlashdan oldin har bir taklif etilgan guruhning bir nechta xabarini va chegarasi yaqinidagilarni tekshiring. Takroriy mazmunni tekshirishda o‘xshash yozuvlarni toping va haqiqiy ifodalarini taqqoslang; ikki parcha bir mavzuni muhokama qilib, turli ko‘rsatmalar berishi mumkin.</p><p>Aynan L12-v2 nashri inglizcha gap embedding profilidir. U all-MiniLM-L6-v2 va paraphrase-MiniLM checkpointlaridan farq qiladi. Umumiy oila nomlari ularning ketma-ketlik konfiguratsiyasi yoki pooling usulini ko‘chirmaydi. O‘xshashlik qiymatini tasdiqlangan javob yoki yakuniy tahririy qaror deb bilish o‘rniga, ixcham zich vektorlarni manba yozuvlari va vazifaga xos baholash bilan birga ishlating.</p>",
  "aboutText": "all-MiniLM-L12-v2 — gaplar va qisqa paragraflar uchun Sentence Transformers enkoderi. U semantik qidiruv, ma’lumot qidirish, klasterlash va gaplar o‘xshashligi uchun har matndan bitta 384 o‘lchamli zich vektor hosil qiladi. Model MiniLM-L12-H384-uncased asosidan foydalanadi va bir milliard gap jufti to‘plamida kontrastiv o‘rganish bilan nozik sozlangan.\n\n### Topmoqchi bo‘lgan birlikni kodlang\n\nBitta foydali fikrni ifodalaydigan gap yoki qisqa paragrafdan foydalaning. Mahsulot tavsiflari to‘plamida buyumni farqlaydigan xususiyat va shartni saqlang: “The adapter connects a wired headset to a USB-C port” iborasi “Audio accessory.”dan ko‘proq ma’lumot beradi. Yozuv identifikatori va asl tavsifni vektor yonida saqlang.\n\nKeyin qidiruv so‘rovini kodlangan tavsiflar bilan taqqoslash mumkin. Yakuniy tanlovni ko‘rsatishdan oldin qaytgan nomzodlarni ulagich turi yoki qurilma mosligi kabi aniq talablar bo‘yicha tekshiring. Vektor matn ma’nosini ifodalaydi; tashqi qidiruv ilovasi esa tegishli yozuvlarni topadi.\n\n### O‘rtacha pooling va normallangan vektorlar\n\nE’lon qilingan kodlash misoli to‘ldiruvchi pozitsiyalar o‘rtachaga qo‘shilmasligi uchun diqqat niqobi bilan token ifodalarining o‘rtachasini oladi. Keyin L2 normallashni qo‘llaydi. Bu bosqichlar asosiy transformerdan ixtiyoriy token olish ko‘rsatmasi emas, gap vektorini olishning rasmiy usulining bir qismidir.\n\nIndekslangan matnlar va yangi so‘rovlar o‘rtasida model, pooling va normallash konfiguratsiyasi izchil bo‘lsin. Hosil bo‘lgan 384 komponentli ifodalarni kosinus o‘xshashligi bilan taqqoslash mumkin. Konfiguratsiya yozuvini to‘plam bilan saqlang; boshqa 384 o‘lchamli enkoder faqat vektorlar uzunligi bir xil bo‘lgani uchun mos bo‘lib qolmaydi.\n\n### Odatiy kirish uzunligi va o‘qitish qamrovi\n\nIshlab chiqaruvchi 256 ta word piece birligidan ortiq qismning odatiy kesilishini belgilaydi. Bu oddiy so‘z yoki belgilar soni emas, tokenizer chegarasidir. Uzunroq yozuvda izchil qisqa bo‘limlar tanlang va har birini talqin qilishga kerakli sarlavha yoki aniqlovchi tafsilotni saqlang.\n\nModelning o‘qitish jarayoni 128 tokengacha ketma-ketliklardan foydalangan. Bu o‘qitish sozlamasi kodlash paytidagi e’lon qilingan 256 ta word piece birlikli odatiy sozlamadan alohida. Odatiy chegaraga sig‘adigan parcha ham o‘qitishdagi qisqa gap materialidan sezilarli farq qilishi mumkin; uzun yoki maxsus tavsiflarni namunaviy misollarda baholang.\n\n### O‘xshashlik va klasterlash jarayonlari\n\nQo‘llab-quvvatlash xabarlari to‘plamida embeddinglar guruhlash bosqichiga raqamli kirishlar berishi mumkin. Odam o‘qiydigan yorliq tayinlashdan oldin har bir taklif etilgan guruhning bir nechta xabarini va chegarasi yaqinidagilarni tekshiring. Takroriy mazmunni tekshirishda o‘xshash yozuvlarni toping va haqiqiy ifodalarini taqqoslang; ikki parcha bir mavzuni muhokama qilib, turli ko‘rsatmalar berishi mumkin.\n\nAynan L12-v2 nashri inglizcha gap embedding profilidir. U all-MiniLM-L6-v2 va paraphrase-MiniLM checkpointlaridan farq qiladi. Umumiy oila nomlari ularning ketma-ketlik konfiguratsiyasi yoki pooling usulini ko‘chirmaydi. O‘xshashlik qiymatini tasdiqlangan javob yoki yakuniy tahririy qaror deb bilish o‘rniga, ixcham zich vektorlarni manba yozuvlari va vazifaga xos baholash bilan birga ishlating.",
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      "html": "<div class=\"sheet-head\"><span class=\"mav\" aria-hidden=\"true\">•</span><h2 id=\"sheet-title\">Sentence Transformers: all-MiniLM-L12-v2</h2><button class=\"icon-btn\" type=\"button\" data-close-sheet aria-label=\"Yopish\">×</button></div><div class=\"idline\"><span>Sentence Transformers /</span><code>sentence-transformers/all-minilm-l12-v2</code><button type=\"button\" id=\"copy-id\">ID ni nusxalash</button></div><nav class=\"card-jump-nav\" aria-label=\"Model kartochkasi bo‘limlari\"><button type=\"button\" data-card-jump=\"overview\">Boshlash</button><button type=\"button\" data-card-jump=\"methods\">Kirish ma’lumotlari va usullar</button><button type=\"button\" data-card-jump=\"pricing\">Tariflar</button><button type=\"button\" data-card-jump=\"examples\">Namunalar</button><button type=\"button\" data-card-jump=\"results\">Natijalar</button><button type=\"button\" data-card-jump=\"aliases\">Taxalluslar</button></nav><div class=\"card-method-tabs\" role=\"tablist\" aria-label=\"API usullari\"><button type=\"button\" role=\"tab\" tabindex=\"0\" aria-controls=\"card-method-content\" aria-selected=\"true\" id=\"card-method-tab-0\" data-card-method=\"Ultra|embeddings|/v1/embeddings|sentence-transformers%2Fall-minilm-l12-v2\">Embeddinglar</button></div><div id=\"card-method-content\" role=\"tabpanel\" aria-labelledby=\"card-method-tab-0\"><section class=\"card-section\" data-card-section=\"overview\"><h3 tabindex=\"-1\">Boshlash</h3><p class=\"mdesc-full\">Sentence Transformers all-MiniLM-L12-v2 gaplar va qisqa paragraflarni qidiruv, o‘xshashlik va klasterlash uchun 384 o‘lchamli vektorlarga kodlaydi. Rasmiy usul o‘rtacha pooling va normallashdan foydalanadi; odatiy kesish chegarasi 256 ta word piece birligi.</p><dl class=\"card-io\" data-card-io><div><dt>Qabul qilinadigan kirish ma’lumotlari</dt><dd>Matn · token_ids</dd></div><div><dt>Natijalar</dt><dd>embedding</dd></div></dl><details class=\"card-about\"><summary>Model haqida</summary><p class=\"note-s\" data-model-method-boundary>Bu tavsif model imkoniyatlarini yoritadi. Ailoot da tanlangan usul uchun mavjud kirish, chiqish va parametrlar quyida keltirilgan.</p><div class=\"mabout\" id=\"m-about\"><p>all-MiniLM-L12-v2 — gaplar va qisqa paragraflar uchun Sentence Transformers enkoderi. U semantik qidiruv, ma’lumot qidirish, klasterlash va gaplar o‘xshashligi uchun har matndan bitta 384 o‘lchamli zich vektor hosil qiladi. Model MiniLM-L12-H384-uncased asosidan foydalanadi va bir milliard gap jufti to‘plamida kontrastiv o‘rganish bilan nozik sozlangan.</p><h4>Topmoqchi bo‘lgan birlikni kodlang</h4><p>Bitta foydali fikrni ifodalaydigan gap yoki qisqa paragrafdan foydalaning. Mahsulot tavsiflari to‘plamida buyumni farqlaydigan xususiyat va shartni saqlang: “The adapter connects a wired headset to a USB-C port” iborasi “Audio accessory.”dan ko‘proq ma’lumot beradi. Yozuv identifikatori va asl tavsifni vektor yonida saqlang.</p><p>Keyin qidiruv so‘rovini kodlangan tavsiflar bilan taqqoslash mumkin. Yakuniy tanlovni ko‘rsatishdan oldin qaytgan nomzodlarni ulagich turi yoki qurilma mosligi kabi aniq talablar bo‘yicha tekshiring. Vektor matn ma’nosini ifodalaydi; tashqi qidiruv ilovasi esa tegishli yozuvlarni topadi.</p><h4>O‘rtacha pooling va normallangan vektorlar</h4><p>E’lon qilingan kodlash misoli to‘ldiruvchi pozitsiyalar o‘rtachaga qo‘shilmasligi uchun diqqat niqobi bilan token ifodalarining o‘rtachasini oladi. Keyin L2 normallashni qo‘llaydi. Bu bosqichlar asosiy transformerdan ixtiyoriy token olish ko‘rsatmasi emas, gap vektorini olishning rasmiy usulining bir qismidir.</p><p>Indekslangan matnlar va yangi so‘rovlar o‘rtasida model, pooling va normallash konfiguratsiyasi izchil bo‘lsin. Hosil bo‘lgan 384 komponentli ifodalarni kosinus o‘xshashligi bilan taqqoslash mumkin. Konfiguratsiya yozuvini to‘plam bilan saqlang; boshqa 384 o‘lchamli enkoder faqat vektorlar uzunligi bir xil bo‘lgani uchun mos bo‘lib qolmaydi.</p><h4>Odatiy kirish uzunligi va o‘qitish qamrovi</h4><p>Ishlab chiqaruvchi 256 ta word piece birligidan ortiq qismning odatiy kesilishini belgilaydi. Bu oddiy so‘z yoki belgilar soni emas, tokenizer chegarasidir. Uzunroq yozuvda izchil qisqa bo‘limlar tanlang va har birini talqin qilishga kerakli sarlavha yoki aniqlovchi tafsilotni saqlang.</p><p>Modelning o‘qitish jarayoni 128 tokengacha ketma-ketliklardan foydalangan. Bu o‘qitish sozlamasi kodlash paytidagi e’lon qilingan 256 ta word piece birlikli odatiy sozlamadan alohida. Odatiy chegaraga sig‘adigan parcha ham o‘qitishdagi qisqa gap materialidan sezilarli farq qilishi mumkin; uzun yoki maxsus tavsiflarni namunaviy misollarda baholang.</p><h4>O‘xshashlik va klasterlash jarayonlari</h4><p>Qo‘llab-quvvatlash xabarlari to‘plamida embeddinglar guruhlash bosqichiga raqamli kirishlar berishi mumkin. Odam o‘qiydigan yorliq tayinlashdan oldin har bir taklif etilgan guruhning bir nechta xabarini va chegarasi yaqinidagilarni tekshiring. Takroriy mazmunni tekshirishda o‘xshash yozuvlarni toping va haqiqiy ifodalarini taqqoslang; ikki parcha bir mavzuni muhokama qilib, turli ko‘rsatmalar berishi mumkin.</p><p>Aynan L12-v2 nashri inglizcha gap embedding profilidir. U all-MiniLM-L6-v2 va paraphrase-MiniLM checkpointlaridan farq qiladi. Umumiy oila nomlari ularning ketma-ketlik konfiguratsiyasi yoki pooling usulini ko‘chirmaydi. O‘xshashlik qiymatini tasdiqlangan javob yoki yakuniy tahririy qaror deb bilish o‘rniga, ixcham zich vektorlarni manba yozuvlari va vazifaga xos baholash bilan birga ishlating.</p></div></details></section><section class=\"card-section\" data-card-section=\"methods\"><h3 tabindex=\"-1\">Ailoot da mavjud · kirish ma’lumotlari va usullar</h3><details class=\"card-method\" open><summary><b>Ailoot Ultra · Embeddinglar</b> <code>POST /v1/embeddings</code></summary><dl class=\"method-capabilities\"><div><dt>Kirish</dt><dd><code>text</code>, <code>token_ids</code></dd></div><div><dt>Chiqish</dt><dd><code>embedding</code></dd></div><div><dt>Chiqish formati</dt><dd>json</dd></div></dl><div class=\"tw\" data-card-parameters data-typed-parameters><table><thead><tr><th>Maydon</th><th>Tur</th><th>Talab</th><th>Qiymatlar va cheklovlar</th><th>Standart</th></tr></thead><tbody><tr data-parameter-availability=\"source_interface\"><th data-cell-label=\"Maydon\" scope=\"row\"><span class=\"card-cell-label\">Maydon</span><code class=\"card-field-name\">model</code></th><td data-cell-label=\"Tur\" data-field-type><span class=\"card-cell-label\">Tur</span><code>string</code></td><td data-cell-label=\"Talab\"><span class=\"card-cell-label\">Talab</span>Majburiy</td><td data-cell-label=\"Qiymatlar va cheklovlar\"><span class=\"card-cell-label\">Qiymatlar va cheklovlar</span><div>sentence-transformers/all-minilm-l12-v2</div></td><td data-cell-label=\"Standart\" data-default=\"unspecified\"><span class=\"card-cell-label\">Standart</span>—</td></tr><tr data-parameter-availability=\"source_interface\"><th data-cell-label=\"Maydon\" scope=\"row\"><span class=\"card-cell-label\">Maydon</span><code class=\"card-field-name\">input</code></th><td data-cell-label=\"Tur\" data-field-type><span class=\"card-cell-label\">Tur</span><code>string|string[]|integer[]|integer[][]</code></td><td data-cell-label=\"Talab\"><span class=\"card-cell-label\">Talab</span>Majburiy</td><td data-cell-label=\"Qiymatlar va cheklovlar\"><span class=\"card-cell-label\">Qiymatlar va cheklovlar</span><div>Elementlarning eng kam soni: 1</div><div>Shartlar: Bo‘sh bo‘lmagan, bir xil turdagi elementlardan iborat paket. Token ID lari manfiy bo‘lmagan butun sonlardir.</div><div>Kirish budjeti: Eng ko‘p: 512 · Matn birligi: UTF8 baytlari · Token birligi: Tokenlar massivi elementlari</div></td><td data-cell-label=\"Standart\" data-default=\"unspecified\"><span class=\"card-cell-label\">Standart</span>—</td></tr><tr data-parameter-availability=\"source_interface\"><th data-cell-label=\"Maydon\" scope=\"row\"><span class=\"card-cell-label\">Maydon</span><code class=\"card-field-name\">encoding_format</code></th><td data-cell-label=\"Tur\" data-field-type><span class=\"card-cell-label\">Tur</span><code>string</code></td><td data-cell-label=\"Talab\"><span class=\"card-cell-label\">Talab</span>Ixtiyoriy</td><td data-cell-label=\"Qiymatlar va cheklovlar\"><span class=\"card-cell-label\">Qiymatlar va cheklovlar</span><div>float</div></td><td data-cell-label=\"Standart\" data-default=\"known\"><span class=\"card-cell-label\">Standart</span>float</td></tr><tr data-parameter-availability=\"unsupported\"><th data-cell-label=\"Maydon\" scope=\"row\"><span class=\"card-cell-label\">Maydon</span><code class=\"card-field-name\">dimensions</code></th><td data-cell-label=\"Tur\" data-field-type><span class=\"card-cell-label\">Tur</span><code>integer</code></td><td data-cell-label=\"Talab\"><span class=\"card-cell-label\">Talab</span>Qo‘llab-quvvatlanmaydi</td><td data-cell-label=\"Qiymatlar va cheklovlar\"><span class=\"card-cell-label\">Qiymatlar va cheklovlar</span><div>Eng kam: 1</div></td><td data-cell-label=\"Standart\" data-default=\"unspecified\"><span class=\"card-cell-label\">Standart</span>—</td></tr><tr data-parameter-availability=\"pass_through_metadata\"><th data-cell-label=\"Maydon\" scope=\"row\"><span class=\"card-cell-label\">Maydon</span><code class=\"card-field-name\">user</code></th><td data-cell-label=\"Tur\" data-field-type><span class=\"card-cell-label\">Tur</span><code>json_value</code></td><td data-cell-label=\"Talab\"><span class=\"card-cell-label\">Talab</span>Ixtiyoriy</td><td data-cell-label=\"Qiymatlar va cheklovlar\"><span class=\"card-cell-label\">Qiymatlar va cheklovlar</span><div>Shartlar: Manba ushbu JSON metama’lumot maydoniga ruxsat beradi; uning ta’minotchi xizmatidagi aniq ma’nosi modelga bog‘liq.</div></td><td data-cell-label=\"Standart\" data-default=\"unspecified\"><span class=\"card-cell-label\">Standart</span>—</td></tr><tr data-parameter-availability=\"pass_through_metadata\"><th data-cell-label=\"Maydon\" scope=\"row\"><span class=\"card-cell-label\">Maydon</span><code class=\"card-field-name\">session_id</code></th><td data-cell-label=\"Tur\" data-field-type><span class=\"card-cell-label\">Tur</span><code>json_value</code></td><td data-cell-label=\"Talab\"><span class=\"card-cell-label\">Talab</span>Ixtiyoriy</td><td data-cell-label=\"Qiymatlar va cheklovlar\"><span class=\"card-cell-label\">Qiymatlar va cheklovlar</span><div>Shartlar: Manba ushbu JSON metama’lumot maydoniga ruxsat beradi; uning ta’minotchi xizmatidagi aniq ma’nosi modelga bog‘liq.</div></td><td data-cell-label=\"Standart\" data-default=\"unspecified\"><span class=\"card-cell-label\">Standart</span>—</td></tr><tr data-parameter-availability=\"pass_through_metadata\"><th data-cell-label=\"Maydon\" scope=\"row\"><span class=\"card-cell-label\">Maydon</span><code class=\"card-field-name\">trace</code></th><td data-cell-label=\"Tur\" data-field-type><span class=\"card-cell-label\">Tur</span><code>json_value</code></td><td data-cell-label=\"Talab\"><span class=\"card-cell-label\">Talab</span>Ixtiyoriy</td><td data-cell-label=\"Qiymatlar va cheklovlar\"><span class=\"card-cell-label\">Qiymatlar va cheklovlar</span><div>Shartlar: Manba ushbu JSON metama’lumot maydoniga ruxsat beradi; uning ta’minotchi xizmatidagi aniq ma’nosi modelga bog‘liq.</div></td><td data-cell-label=\"Standart\" data-default=\"unspecified\"><span class=\"card-cell-label\">Standart</span>—</td></tr><tr data-parameter-availability=\"source_interface\"><th data-cell-label=\"Maydon\" scope=\"row\"><span class=\"card-cell-label\">Maydon</span><code class=\"card-field-name\">provider</code></th><td data-cell-label=\"Tur\" data-field-type><span class=\"card-cell-label\">Tur</span><code>object</code></td><td data-cell-label=\"Talab\"><span class=\"card-cell-label\">Talab</span>Ixtiyoriy</td><td data-cell-label=\"Qiymatlar va cheklovlar\"><span class=\"card-cell-label\">Qiymatlar va cheklovlar</span><div>Shartlar: provider.options rad etildi</div></td><td data-cell-label=\"Standart\" data-default=\"unspecified\"><span class=\"card-cell-label\">Standart</span>—</td></tr></tbody></table></div><details class=\"card-about\" data-material-combinations><summary>Kirish materiallari birikmalari</summary><p class=\"note-s\"><b>text_to_embedding</b></p></details><details class=\"note-s\" data-method-contract><summary>Usul parametrlari va hayot sikli</summary><div class=\"tw\" data-card-table=\"contract\"><table><thead><tr><th>Parametr</th><th>Parametr talablari</th></tr></thead><tbody><tr><th data-cell-label=\"Parametr\" scope=\"row\"><span class=\"card-cell-label\">Parametr</span><code>content_type</code></th><td data-cell-label=\"Parametr talablari\"><span class=\"card-cell-label\">Parametr talablari</span>application/json</td></tr><tr><th data-cell-label=\"Parametr\" scope=\"row\"><span class=\"card-cell-label\">Parametr</span><code>required</code></th><td data-cell-label=\"Parametr talablari\"><span class=\"card-cell-label\">Parametr talablari</span>model, input</td></tr><tr><th data-cell-label=\"Parametr\" scope=\"row\"><span class=\"card-cell-label\">Parametr</span><code>input_shapes</code></th><td data-cell-label=\"Parametr talablari\"><span class=\"card-cell-label\">Parametr talablari</span>string, string[], nonnegative_integer_token_array, nonnegative_integer_token_array[]</td></tr><tr><th data-cell-label=\"Parametr\" scope=\"row\"><span class=\"card-cell-label\">Parametr</span><code>encoding_format</code></th><td data-cell-label=\"Parametr talablari\"><span class=\"card-cell-label\">Parametr talablari</span>float</td></tr><tr><th data-cell-label=\"Parametr\" scope=\"row\"><span class=\"card-cell-label\">Parametr</span><code>dimensions_override</code></th><td data-cell-label=\"Parametr talablari\"><span class=\"card-cell-label\">Parametr talablari</span>false</td></tr><tr><th data-cell-label=\"Parametr\" scope=\"row\"><span class=\"card-cell-label\">Parametr</span><code>max_input_budget_units</code></th><td data-cell-label=\"Parametr talablari\"><span class=\"card-cell-label\">Parametr talablari</span>512</td></tr><tr><th data-cell-label=\"Parametr\" scope=\"row\"><span class=\"card-cell-label\">Parametr</span><code>multimodal_objects</code></th><td data-cell-label=\"Parametr talablari\"><span class=\"card-cell-label\">Parametr talablari</span>false</td></tr><tr><th data-cell-label=\"Parametr\" scope=\"row\"><span class=\"card-cell-label\">Parametr</span><code>material_types</code></th><td data-cell-label=\"Parametr talablari\"><span class=\"card-cell-label\">Parametr talablari</span>text, token_ids</td></tr></tbody></table></div><p>Hayot sikli: <code>synchronous_json</code></p><p class=\"note-s\">Har bir vektorning indeksi va o‘lchamini saqlang.</p><p class=\"note-s\">Model nomida multimodal deyilgani aynan shu yo‘nalishda rasm obyektlariga ruxsat borligini anglatmaydi.</p></details></details></section><section class=\"card-section\" data-card-section=\"pricing\"><h3 tabindex=\"-1\">Tariflar</h3><div data-native-semantic-method=\"v1\" data-tariff-reference-only><p class=\"note-s\" data-rate-support-note>Tarif ma’lumotlari so‘rov imkoniyatlarini qo‘shmaydi. Qo‘llab-quvvatlanadigan parametrlar tanlangan usulda ko‘rsatilgan.</p><dl data-price-conditions><dt>Tezkor rejim</dt><dd>Ko‘rsatilmagan <span class=\"note-s\">Tezkor rejim tanlanganda</span></dd><dt>Flex rejimi</dt><dd>Ko‘rsatilmagan <span class=\"note-s\">flex rejimi tanlanganda</span></dd><dt>Maksimal fikrlash darajasi</dt><dd>Ko‘rsatilmagan <span class=\"note-s\">Maksimal fikrlash darajasi uchun standart (default) tuzatish qo‘llanganda</span></dd></dl></div><div data-declared-tariff-pricing><h4>Ailoot tariflari</h4><h4>Sozlamalar bo‘yicha narxlar</h4><div class=\"tw\" data-pricing-profiles><table><thead><tr><th>Sozlama</th><th>Tariflar jadvali</th><th>To‘lov</th></tr></thead><tbody><tr><th data-cell-label=\"Sozlama\" scope=\"row\"><span class=\"card-cell-label\">Sozlama</span>Tokenlar uchun bazaviy tariflar</th><td data-cell-label=\"Tariflar jadvali\"><span class=\"card-cell-label\">Tariflar jadvali</span>Asosiy tarif</td><td data-cell-label=\"To‘lov\"><span class=\"card-cell-label\">To‘lov</span><div><span>Kirish · Kirish tokenlari</span> <b>$0.00001</b> <span class=\"price-unit\">/ 1K token</span></div><div><span>Chiqish · Chiqish tokenlari</span> <b>$0</b> <span class=\"price-unit\">/ 1K token</span></div><div><span>Kirish · Keshdan o‘qish</span> <b>—</b> <span class=\"price-unit\">/ 1K token</span></div><div><span>Kirish · Keshga yozish</span> <b>—</b> <span class=\"price-unit\">/ 1K token</span></div><div><span>Kirish · Keshga yozish, 1 soat</span> <b>—</b> <span class=\"price-unit\">/ 1K token</span></div></td></tr></tbody></table></div></div><p class=\"note-s\">Ailoot so‘rovdan oldin taxminiy summani band qiladi. Natijadan keyin o‘lchangan sarf asosidagi yakuniy to‘lovni yechadi. Oqimli javob bo‘yicha yakuniy hisob-kitob yakuniy sarf yozuviga asoslanadi. <a href=\"/docs/uz/billing/reserve/\">Summani band qilish va to‘lovni hisoblash tartibi →</a></p></section><section class=\"card-section\" data-card-section=\"examples\"><h3 tabindex=\"-1\">So‘rov namunalari</h3><h4>Ailoot Ultra · Embeddinglar</h4><div class=\"code-group\"><div class=\"cg-bar\"><div class=\"cg-tabs\" role=\"tablist\" aria-label=\"Kod variantlari\"><button class=\"cg-tab\" id=\"model-code-2596-tab-0\" role=\"tab\" type=\"button\" aria-controls=\"model-code-2596-panel-0\" aria-selected=\"true\" tabindex=\"0\">curl</button><button class=\"cg-tab\" id=\"model-code-2596-tab-1\" role=\"tab\" type=\"button\" aria-controls=\"model-code-2596-panel-1\" aria-selected=\"false\" tabindex=\"-1\">Python SDK</button></div><div class=\"cg-tools\"><button class=\"cg-btn cg-copy\" type=\"button\" aria-label=\"Kodni nusxalash\"><svg class=\"cp\" viewBox=\"0 0 16 16\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.4\"><rect x=\"5.25\" y=\"5.25\" width=\"8.5\" height=\"8.5\" rx=\"1.75\"/><path d=\"M10.75 5.25V3.5a1.25 1.25 0 0 0-1.25-1.25H3.5A1.25 1.25 0 0 0 2.25 3.5v6a1.25 1.25 0 0 0 1.25 1.25h1.75\"/></svg><svg class=\"ok\" viewBox=\"0 0 16 16\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.4\"><path d=\"m3 8.5 3.25 3.25L13 5\"/></svg></button></div></div><div class=\"cg-body\"><div class=\"cg-pane\" id=\"model-code-2596-panel-0\" role=\"tabpanel\" aria-labelledby=\"model-code-2596-tab-0\"><pre data-lang=\"bash\"><code>set -eu\n: &quot;${AILOOT_API_KEY:?Set your Ailoot API key}&quot;\npython3 - &lt;&lt;&#39;PY_REQUEST&#39;\nimport json,os\nfrom pathlib import Path\np=json.loads(&#39;{&quot;model&quot;: &quot;sentence-transformers/all-minilm-l12-v2&quot;, &quot;input&quot;: [&quot;First document&quot;, &quot;Second document&quot;]}&#39;)\n\nPath(&quot;request.json&quot;).write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/embeddings \\\n  -H &quot;Authorization: Bearer $AILOOT_API_KEY&quot; -H &#39;Content-Type: application/json&#39; \\\n  --data-binary @request.json -D response-headers.txt -o result.json\npython3 - &lt;&lt;&#39;PY_RESULT&#39;\nimport json\nfrom pathlib import Path\nr=json.loads(Path(&quot;result.json&quot;).read_text())\nfor x in r.get(&quot;data&quot;,[]): print(json.dumps({&quot;index&quot;:x.get(&quot;index&quot;),&quot;embedding&quot;:x.get(&quot;embedding&quot;)}))\nPY_RESULT\n</code></pre></div><div class=\"cg-pane\" id=\"model-code-2596-panel-1\" role=\"tabpanel\" aria-labelledby=\"model-code-2596-tab-1\" hidden><pre data-lang=\"python\"><code>from openai import OpenAI\nimport os\n\nclient=OpenAI(base_url=&quot;https://api.ailoot.ai/v1&quot;,api_key=os.environ[&quot;AILOOT_API_KEY&quot;],max_retries=0)\nresult=client.embeddings.create(model=&#39;sentence-transformers/all-minilm-l12-v2&#39;,input=[&quot;First document&quot;,&quot;Second document&quot;])\nprint(result)\nfor item in result.data:print(item.index,item.embedding)\n</code></pre></div></div></div></section><section class=\"card-section\" data-card-section=\"results\"><h3 tabindex=\"-1\">Natijalarni olish va saqlash</h3><h4>Ailoot Ultra · Embeddinglar</h4><p class=\"note-s\">json</p><div class=\"card-result-process\" data-all-results><h5>Barcha tayyor natijalarni saqlash</h5><p class=\"note-s\">Qaytarilgan har bir to‘liq javob, fayl va kontent qismini saqlang.</p><p class=\"note-s\">Har bir vektorni indeksi bilan saqlang. Vektorlar soni kirish elementlari soniga teng bo‘lishi va o‘lchamlar bir xil bo‘lishi kerak.</p></div><div class=\"card-result-process\" data-unknown-recovery><h5>Asl javobni tekshirish kerak bo‘lsa</h5><p class=\"note-s\">Embedding vazifalari uchun endpoint qo‘shilmaydi.</p></div><details class=\"card-about\" data-output-fields><summary>Javob maydonlari</summary><div class=\"tw\" data-card-table=\"output\"><table><thead><tr><th>Maydon</th><th>Tur</th></tr></thead><tbody><tr><th data-cell-label=\"Maydon\" scope=\"row\"><span class=\"card-cell-label\">Maydon</span><code class=\"card-field-name\">object</code></th><td data-cell-label=\"Tur\" data-field-type><span class=\"card-cell-label\">Tur</span>string</td></tr><tr><th data-cell-label=\"Maydon\" scope=\"row\"><span class=\"card-cell-label\">Maydon</span><code class=\"card-field-name\">model</code></th><td data-cell-label=\"Tur\" data-field-type><span class=\"card-cell-label\">Tur</span>string</td></tr><tr><th data-cell-label=\"Maydon\" scope=\"row\"><span class=\"card-cell-label\">Maydon</span><code class=\"card-field-name\">data[].index</code></th><td data-cell-label=\"Tur\" data-field-type><span class=\"card-cell-label\">Tur</span>integer</td></tr><tr><th data-cell-label=\"Maydon\" scope=\"row\"><span class=\"card-cell-label\">Maydon</span><code class=\"card-field-name\">data[].embedding</code></th><td data-cell-label=\"Tur\" data-field-type><span class=\"card-cell-label\">Tur</span>finite float[]</td></tr><tr><th data-cell-label=\"Maydon\" scope=\"row\"><span class=\"card-cell-label\">Maydon</span><code class=\"card-field-name\">usage</code></th><td data-cell-label=\"Tur\" data-field-type><span class=\"card-cell-label\">Tur</span>object</td></tr></tbody></table></div></details><details class=\"card-about\" data-method-errors><summary>Xatolar va keyingi qadamlar</summary><div class=\"tw\" data-card-table=\"errors\"><table><thead><tr><th>Javob</th><th>Shart</th><th>Keyingi qadam</th></tr></thead><tbody><tr><th data-cell-label=\"Javob\" scope=\"row\"><span class=\"card-cell-label\">Javob</span>400 · <code>invalid_request_error</code></th><td data-cell-label=\"Shart\"><span class=\"card-cell-label\">Shart</span>Majburiy maydonlar, material ifodasi yoki parametr qiymati noto‘g‘ri</td><td data-cell-label=\"Keyingi qadam\"><span class=\"card-cell-label\">Keyingi qadam</span>Yangi yaratish so‘rovidan oldin ko‘rsatilgan maydonlarni tuzating.</td></tr></tbody></table></div></details></section><section class=\"card-section\" data-card-section=\"aliases\"><h3 tabindex=\"-1\">Taxalluslar</h3><h4>Model taxalluslari</h4><p class=\"note-s\"><span data-model-alias-route=\"Ultra\"><b>Ailoot Ultra</b> <a href=\"#m=sentence-transformers%2Fall-minilm-l12-v2&amp;route=Ultra&amp;caller=sentence-transformers%2Fall-minilm-l12-v2&amp;fmt=embeddings\"><code>sentence-transformers/all-minilm-l12-v2</code></a></span></p><h4>Usul yo‘llari taxalluslari</h4><p class=\"note-s\">Yuqorida ko‘rsatilgan usul yo‘llaridan foydalaning.</p><h4>Chaqirish uchun tegishli ID lar</h4><p class=\"note-s\"><a href=\"#m=sentence-transformers%2Fall-minilm-l12-v2&amp;route=Ultra&amp;caller=sentence-transformers%2Fall-minilm-l12-v2&amp;fmt=embeddings\"><code>sentence-transformers/all-minilm-l12-v2</code></a></p></section></div>",
      "method_identity": {
        "effective_request_model_id": "sentence-transformers/all-minilm-l12-v2",
        "fmt": "embeddings",
        "http": "POST",
        "path": "/v1/embeddings",
        "request_model_id": "sentence-transformers/all-minilm-l12-v2",
        "route_label": "Ultra"
      },
      "method_key": "Ultra|embeddings|/v1/embeddings|sentence-transformers%2Fall-minilm-l12-v2",
      "ordinal": 0,
      "rawMethod": {
        "available": null,
        "cache_read": null,
        "cache_write": null,
        "cache_write_1h": null,
        "contract": {
          "data": {
            "all_results": "Keep every vector with its index. Vector count must equal input count; dimensions must be consistent.",
            "errors": [
              {
                "code": null,
                "condition": "Majburiy maydonlar, material ifodasi yoki parametr qiymati noto‘g‘ri",
                "http_status": 400,
                "recovery": "Yangi yaratish so‘rovidan oldin ko‘rsatilgan maydonlarni tuzating.",
                "type": "invalid_request_error"
              }
            ],
            "hints": [],
            "ignored_fields": [],
            "lifecycle": {
              "create": {
                "http": "POST",
                "path": "/v1/embeddings"
              },
              "job_read": null,
              "mode": "synchronous_json",
              "unknown": "Embedding vazifalari uchun endpoint qo‘shilmaydi."
            },
            "material_combinations": [
              {
                "alternatives": [
                  "string",
                  "string[]",
                  "nonnegative_integer_token_array",
                  "nonnegative_integer_token_array[]"
                ],
                "mode": "text_to_embedding"
              }
            ],
            "operation_modes": [
              "single_embedding",
              "batched_embeddings"
            ],
            "outputs": {
              "all_results": "Har bir vektorni indeksi bilan saqlang. Vektorlar soni kirish elementlari soniga teng bo‘lishi va o‘lchamlar bir xil bo‘lishi kerak.",
              "default_dimensions": null,
              "family": "embedding",
              "fields": [
                {
                  "description": null,
                  "name": "object",
                  "type": "string"
                },
                {
                  "description": null,
                  "name": "model",
                  "type": "string"
                },
                {
                  "description": null,
                  "name": "data[].index",
                  "type": "integer"
                },
                {
                  "description": null,
                  "name": "data[].embedding",
                  "type": "finite float[]"
                },
                {
                  "description": null,
                  "name": "usage",
                  "type": "object"
                }
              ],
              "format": "json"
            },
            "parameters": [
              {
                "availability": "source_interface",
                "default": null,
                "default_known": false,
                "enum": [
                  "sentence-transformers/all-minilm-l12-v2"
                ],
                "enum_known": true,
                "name": "model",
                "required": true,
                "type": "string"
              },
              {
                "availability": "source_interface",
                "budget": {
                  "maximum": 512,
                  "text_unit": "UTF8 baytlari",
                  "token_unit": "Tokenlar massivi elementlari"
                },
                "conditions": "Bo‘sh bo‘lmagan, bir xil turdagi elementlardan iborat paket. Token ID lari manfiy bo‘lmagan butun sonlardir.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "min_items": 1,
                "name": "input",
                "required": true,
                "type": "string|string[]|integer[]|integer[][]"
              },
              {
                "availability": "source_interface",
                "default": "float",
                "default_known": true,
                "enum": [
                  "float"
                ],
                "enum_known": true,
                "name": "encoding_format",
                "required": false,
                "type": "string"
              },
              {
                "availability": "unsupported",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "minimum": 1,
                "name": "dimensions",
                "required": false,
                "type": "integer"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "Manba ushbu JSON metama’lumot maydoniga ruxsat beradi; uning ta’minotchi xizmatidagi aniq ma’nosi modelga bog‘liq.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "user",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "Manba ushbu JSON metama’lumot maydoniga ruxsat beradi; uning ta’minotchi xizmatidagi aniq ma’nosi modelga bog‘liq.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "session_id",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "Manba ushbu JSON metama’lumot maydoniga ruxsat beradi; uning ta’minotchi xizmatidagi aniq ma’nosi modelga bog‘liq.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "trace",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "source_interface",
                "conditions": "provider.options rad etildi",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "provider",
                "required": false,
                "type": "object"
              }
            ],
            "profile_binding": "Exact existing Ailoot model and method; interface constraints below do not assert provider qualification.",
            "unsupported": [
              "mixed input kinds",
              "empty input",
              "image/audio/file objects",
              "encoding_format=base64",
              "dimensions override"
            ]
          },
          "hints": [
            "Har bir vektorning indeksi va o‘lchamini saqlang.",
            "Model nomida multimodal deyilgani aynan shu yo‘nalishda rasm obyektlariga ruxsat borligini anglatmaydi."
          ],
          "input": {
            "content_type": "application/json",
            "dimensions_override": false,
            "encoding_format": [
              "float"
            ],
            "input_shapes": [
              "string",
              "string[]",
              "nonnegative_integer_token_array",
              "nonnegative_integer_token_array[]"
            ],
            "material_types": [
              "text",
              "token_ids"
            ],
            "max_input_budget_units": 512,
            "multimodal_objects": false,
            "required": [
              "model",
              "input"
            ]
          },
          "lifecycle": {
            "mode": "synchronous_json",
            "unknown": "no embedding job endpoint inferred"
          },
          "manufacturer_capability": null,
          "method": {
            "http": "POST",
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          "method_aliases": [],
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            "text_to_embedding"
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          "output": {
            "default_dimensions": null,
            "family": "embedding",
            "format": "json",
            "http_success": 200,
            "vectors": "one dense float vector per input; indices/model/usage validated"
          }
        },
        "ctx": null,
        "declared_pricing_profiles": [
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            "dimensions": {
              "regime": "base"
            },
            "label": "Tokenlar uchun bazaviy tariflar",
            "units": [
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                "billing_input": true,
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                "label": "Input tokens",
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                "side": "in",
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            ]
          }
        ],
        "examples": [
          {
            "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{\"model\": \"sentence-transformers/all-minilm-l12-v2\", \"input\": [\"First document\", \"Second document\"]}')\n\nPath(\"request.json\").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/embeddings \\\n  -H \"Authorization: Bearer $AILOOT_API_KEY\" -H 'Content-Type: application/json' \\\n  --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nr=json.loads(Path(\"result.json\").read_text())\nfor x in r.get(\"data\",[]): print(json.dumps({\"index\":x.get(\"index\"),\"embedding\":x.get(\"embedding\")}))\nPY_RESULT\n",
            "label": "curl · retained exact creation",
            "language": "bash"
          },
          {
            "code": "from openai import OpenAI\nimport os\n\nclient=OpenAI(base_url=\"https://api.ailoot.ai/v1\",api_key=os.environ[\"AILOOT_API_KEY\"],max_retries=0)\nresult=client.embeddings.create(model='sentence-transformers/all-minilm-l12-v2',input=[\"First document\",\"Second document\"])\nprint(result)\nfor item in result.data:print(item.index,item.embedding)\n",
            "label": "Python SDK · all vectors",
            "language": "python"
          }
        ],
        "family": "openai",
        "fast_multiplier": null,
        "flex_multiplier": null,
        "fmt": "embeddings",
        "hints": [
          "Keep every vector index and dimension.",
          "Multimodal model name does not admit image objects on this exact path."
        ],
        "http": "POST",
        "in": 0.01,
        "input": [
          "text",
          "token_ids"
        ],
        "label": "Эмбеддинги",
        "max_out": null,
        "max_reasoning_effort_multiplier": null,
        "method_aliases": [],
        "out": 0,
        "output": [
          "embedding"
        ],
        "path": "/v1/embeddings",
        "price_conditions": [
          {
            "label": "Fast mode",
            "value": "Not specified",
            "when": "When fast mode is selected"
          },
          {
            "label": "Flex mode",
            "value": "Not specified",
            "when": "When flex mode is selected"
          },
          {
            "label": "Maximum reasoning effort",
            "value": "Not specified",
            "when": "When maximum reasoning effort uses the default adjustment"
          }
        ],
        "pricing": {
          "billing_mode": "token",
          "image_input_price": null,
          "image_output_price": null,
          "per_request_price": null
        },
        "pricing_profiles": [
          {
            "dimensions": {
              "regime": "base"
            },
            "label": "Base token rates",
            "units": [
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                "label": "Input tokens",
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              },
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                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
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                "key": "cache_read_price",
                "label": "Cache read",
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                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "one_hour_cache_creation_token"
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          }
        ],
        "request_model_id": "sentence-transformers/all-minilm-l12-v2",
        "route_label": "Ultra",
        "status_reads": [],
        "tiers": [],
        "units": [
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  ],
  "model_id": "sentence-transformers/all-minilm-l12-v2",
  "nativeProvenance": {
    "input_facts_canonical_sha256": "213680156813c2a4c1ea37f611a0ac80392d089d503d6ec93006a9d68866968f",
    "input_html_sha256": "9219fd2a5d1d93ef8e2325029a90b2bdc857a936c876eac25ba7dbf8330cc299",
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    "role": "historical-normalized-input-audit-hashes",
    "schema": "ailoot-native-model-provenance-v2"
  },
  "page": {
    "canonical": "https://ailoot.ai/models/uz/sentence-transformers-all-minilm-l12-v2/",
    "description": "Sentence Transformers all-MiniLM-L12-v2 gaplar va qisqa paragraflarni qidiruv, o‘xshashlik va klasterlash uchun 384 o‘lchamli vektorlarga kodlaydi. Rasmiy usul o‘rtacha pooling va normallashdan foydalanadi; odatiy kesish chegarasi 256 ta word piece birligi.",
    "locale": "uz",
    "md_url": "https://ailoot.ai/models/uz/sentence-transformers-all-minilm-l12-v2/index.md",
    "title": "Sentence Transformers: all-MiniLM-L12-v2 — Ailoot",
    "url": "https://ailoot.ai/models/uz/sentence-transformers-all-minilm-l12-v2/"
  },
  "rawModel": {
    "about": "all-MiniLM-L12-v2 — gaplar va qisqa paragraflar uchun Sentence Transformers enkoderi. U semantik qidiruv, ma’lumot qidirish, klasterlash va gaplar o‘xshashligi uchun har matndan bitta 384 o‘lchamli zich vektor hosil qiladi. Model MiniLM-L12-H384-uncased asosidan foydalanadi va bir milliard gap jufti to‘plamida kontrastiv o‘rganish bilan nozik sozlangan.\n\n### Topmoqchi bo‘lgan birlikni kodlang\n\nBitta foydali fikrni ifodalaydigan gap yoki qisqa paragrafdan foydalaning. Mahsulot tavsiflari to‘plamida buyumni farqlaydigan xususiyat va shartni saqlang: “The adapter connects a wired headset to a USB-C port” iborasi “Audio accessory.”dan ko‘proq ma’lumot beradi. Yozuv identifikatori va asl tavsifni vektor yonida saqlang.\n\nKeyin qidiruv so‘rovini kodlangan tavsiflar bilan taqqoslash mumkin. Yakuniy tanlovni ko‘rsatishdan oldin qaytgan nomzodlarni ulagich turi yoki qurilma mosligi kabi aniq talablar bo‘yicha tekshiring. Vektor matn ma’nosini ifodalaydi; tashqi qidiruv ilovasi esa tegishli yozuvlarni topadi.\n\n### O‘rtacha pooling va normallangan vektorlar\n\nE’lon qilingan kodlash misoli to‘ldiruvchi pozitsiyalar o‘rtachaga qo‘shilmasligi uchun diqqat niqobi bilan token ifodalarining o‘rtachasini oladi. Keyin L2 normallashni qo‘llaydi. Bu bosqichlar asosiy transformerdan ixtiyoriy token olish ko‘rsatmasi emas, gap vektorini olishning rasmiy usulining bir qismidir.\n\nIndekslangan matnlar va yangi so‘rovlar o‘rtasida model, pooling va normallash konfiguratsiyasi izchil bo‘lsin. Hosil bo‘lgan 384 komponentli ifodalarni kosinus o‘xshashligi bilan taqqoslash mumkin. Konfiguratsiya yozuvini to‘plam bilan saqlang; boshqa 384 o‘lchamli enkoder faqat vektorlar uzunligi bir xil bo‘lgani uchun mos bo‘lib qolmaydi.\n\n### Odatiy kirish uzunligi va o‘qitish qamrovi\n\nIshlab chiqaruvchi 256 ta word piece birligidan ortiq qismning odatiy kesilishini belgilaydi. Bu oddiy so‘z yoki belgilar soni emas, tokenizer chegarasidir. Uzunroq yozuvda izchil qisqa bo‘limlar tanlang va har birini talqin qilishga kerakli sarlavha yoki aniqlovchi tafsilotni saqlang.\n\nModelning o‘qitish jarayoni 128 tokengacha ketma-ketliklardan foydalangan. Bu o‘qitish sozlamasi kodlash paytidagi e’lon qilingan 256 ta word piece birlikli odatiy sozlamadan alohida. Odatiy chegaraga sig‘adigan parcha ham o‘qitishdagi qisqa gap materialidan sezilarli farq qilishi mumkin; uzun yoki maxsus tavsiflarni namunaviy misollarda baholang.\n\n### O‘xshashlik va klasterlash jarayonlari\n\nQo‘llab-quvvatlash xabarlari to‘plamida embeddinglar guruhlash bosqichiga raqamli kirishlar berishi mumkin. Odam o‘qiydigan yorliq tayinlashdan oldin har bir taklif etilgan guruhning bir nechta xabarini va chegarasi yaqinidagilarni tekshiring. Takroriy mazmunni tekshirishda o‘xshash yozuvlarni toping va haqiqiy ifodalarini taqqoslang; ikki parcha bir mavzuni muhokama qilib, turli ko‘rsatmalar berishi mumkin.\n\nAynan L12-v2 nashri inglizcha gap embedding profilidir. U all-MiniLM-L6-v2 va paraphrase-MiniLM checkpointlaridan farq qiladi. Umumiy oila nomlari ularning ketma-ketlik konfiguratsiyasi yoki pooling usulini ko‘chirmaydi. O‘xshashlik qiymatini tasdiqlangan javob yoki yakuniy tahririy qaror deb bilish o‘rniga, ixcham zich vektorlarni manba yozuvlari va vazifaga xos baholash bilan birga ishlating.",
    "alias_of": null,
    "aliases": [],
    "author": "Sentence Transformers",
    "available": null,
    "canonical_model": "sentence-transformers/all-minilm-l12-v2",
    "capabilities": {
      "context_length": 512,
      "manufacturer_input": [
        "text"
      ],
      "manufacturer_output": [
        "embeddings"
      ]
    },
    "card_routes": [
      {
        "label": "Ultra",
        "rates": [
          {
            "amount": "0.00001000",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "input_price",
            "label": "Input tokens",
            "native_amount_usd_per_unit": "0.00000001",
            "native_quantity": 1,
            "native_unit": "input_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "input_token"
          },
          {
            "amount": "0",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "output_price",
            "label": "Output tokens",
            "native_amount_usd_per_unit": "0",
            "native_quantity": 1,
            "native_unit": "output_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "out",
            "unit": "output_token"
          }
        ],
        "summary": "Konfiguratsiya narxlari tafsilotlarda to‘liq ko‘rsatilgan."
      }
    ],
    "ctx": null,
    "entity_type": "model",
    "formats": [
      "embeddings"
    ],
    "id": "sentence-transformers/all-minilm-l12-v2",
    "input": [
      "text",
      "token_ids"
    ],
    "kind": "embedding",
    "max_out": null,
    "methods": [
      {
        "available": null,
        "cache_read": null,
        "cache_write": null,
        "cache_write_1h": null,
        "contract": {
          "data": {
            "all_results": "Keep every vector with its index. Vector count must equal input count; dimensions must be consistent.",
            "errors": [
              {
                "code": null,
                "condition": "Majburiy maydonlar, material ifodasi yoki parametr qiymati noto‘g‘ri",
                "http_status": 400,
                "recovery": "Yangi yaratish so‘rovidan oldin ko‘rsatilgan maydonlarni tuzating.",
                "type": "invalid_request_error"
              }
            ],
            "hints": [],
            "ignored_fields": [],
            "lifecycle": {
              "create": {
                "http": "POST",
                "path": "/v1/embeddings"
              },
              "job_read": null,
              "mode": "synchronous_json",
              "unknown": "Embedding vazifalari uchun endpoint qo‘shilmaydi."
            },
            "material_combinations": [
              {
                "alternatives": [
                  "string",
                  "string[]",
                  "nonnegative_integer_token_array",
                  "nonnegative_integer_token_array[]"
                ],
                "mode": "text_to_embedding"
              }
            ],
            "operation_modes": [
              "single_embedding",
              "batched_embeddings"
            ],
            "outputs": {
              "all_results": "Har bir vektorni indeksi bilan saqlang. Vektorlar soni kirish elementlari soniga teng bo‘lishi va o‘lchamlar bir xil bo‘lishi kerak.",
              "default_dimensions": null,
              "family": "embedding",
              "fields": [
                {
                  "description": null,
                  "name": "object",
                  "type": "string"
                },
                {
                  "description": null,
                  "name": "model",
                  "type": "string"
                },
                {
                  "description": null,
                  "name": "data[].index",
                  "type": "integer"
                },
                {
                  "description": null,
                  "name": "data[].embedding",
                  "type": "finite float[]"
                },
                {
                  "description": null,
                  "name": "usage",
                  "type": "object"
                }
              ],
              "format": "json"
            },
            "parameters": [
              {
                "availability": "source_interface",
                "default": null,
                "default_known": false,
                "enum": [
                  "sentence-transformers/all-minilm-l12-v2"
                ],
                "enum_known": true,
                "name": "model",
                "required": true,
                "type": "string"
              },
              {
                "availability": "source_interface",
                "budget": {
                  "maximum": 512,
                  "text_unit": "UTF8 baytlari",
                  "token_unit": "Tokenlar massivi elementlari"
                },
                "conditions": "Bo‘sh bo‘lmagan, bir xil turdagi elementlardan iborat paket. Token ID lari manfiy bo‘lmagan butun sonlardir.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "min_items": 1,
                "name": "input",
                "required": true,
                "type": "string|string[]|integer[]|integer[][]"
              },
              {
                "availability": "source_interface",
                "default": "float",
                "default_known": true,
                "enum": [
                  "float"
                ],
                "enum_known": true,
                "name": "encoding_format",
                "required": false,
                "type": "string"
              },
              {
                "availability": "unsupported",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "minimum": 1,
                "name": "dimensions",
                "required": false,
                "type": "integer"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "Manba ushbu JSON metama’lumot maydoniga ruxsat beradi; uning ta’minotchi xizmatidagi aniq ma’nosi modelga bog‘liq.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "user",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "Manba ushbu JSON metama’lumot maydoniga ruxsat beradi; uning ta’minotchi xizmatidagi aniq ma’nosi modelga bog‘liq.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "session_id",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "Manba ushbu JSON metama’lumot maydoniga ruxsat beradi; uning ta’minotchi xizmatidagi aniq ma’nosi modelga bog‘liq.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "trace",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "source_interface",
                "conditions": "provider.options rad etildi",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "provider",
                "required": false,
                "type": "object"
              }
            ],
            "profile_binding": "Exact existing Ailoot model and method; interface constraints below do not assert provider qualification.",
            "unsupported": [
              "mixed input kinds",
              "empty input",
              "image/audio/file objects",
              "encoding_format=base64",
              "dimensions override"
            ]
          },
          "hints": [
            "Har bir vektorning indeksi va o‘lchamini saqlang.",
            "Model nomida multimodal deyilgani aynan shu yo‘nalishda rasm obyektlariga ruxsat borligini anglatmaydi."
          ],
          "input": {
            "content_type": "application/json",
            "dimensions_override": false,
            "encoding_format": [
              "float"
            ],
            "input_shapes": [
              "string",
              "string[]",
              "nonnegative_integer_token_array",
              "nonnegative_integer_token_array[]"
            ],
            "material_types": [
              "text",
              "token_ids"
            ],
            "max_input_budget_units": 512,
            "multimodal_objects": false,
            "required": [
              "model",
              "input"
            ]
          },
          "lifecycle": {
            "mode": "synchronous_json",
            "unknown": "no embedding job endpoint inferred"
          },
          "manufacturer_capability": null,
          "method": {
            "http": "POST",
            "path": "/v1/embeddings"
          },
          "method_aliases": [],
          "operation_modes": [
            "text_to_embedding"
          ],
          "output": {
            "default_dimensions": null,
            "family": "embedding",
            "format": "json",
            "http_success": 200,
            "vectors": "one dense float vector per input; indices/model/usage validated"
          }
        },
        "ctx": null,
        "declared_pricing_profiles": [
          {
            "dimensions": {
              "regime": "base"
            },
            "label": "Tokenlar uchun bazaviy tariflar",
            "units": [
              {
                "amount": "0.00001000",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0.00000001",
                "native_quantity": 1,
                "native_unit": "input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "input_token"
              },
              {
                "amount": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "output_price",
                "label": "Output tokens",
                "native_amount_usd_per_unit": "0",
                "native_quantity": 1,
                "native_unit": "output_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "out",
                "unit": "output_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_read_price",
                "label": "Cache read",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "cached_input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "cached_input_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_price",
                "label": "Cache write",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "generic_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "generic_cache_creation_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_1h_price",
                "label": "Cache write, 1 hour",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "one_hour_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "one_hour_cache_creation_token"
              }
            ]
          }
        ],
        "examples": [
          {
            "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{\"model\": \"sentence-transformers/all-minilm-l12-v2\", \"input\": [\"First document\", \"Second document\"]}')\n\nPath(\"request.json\").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/embeddings \\\n  -H \"Authorization: Bearer $AILOOT_API_KEY\" -H 'Content-Type: application/json' \\\n  --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nr=json.loads(Path(\"result.json\").read_text())\nfor x in r.get(\"data\",[]): print(json.dumps({\"index\":x.get(\"index\"),\"embedding\":x.get(\"embedding\")}))\nPY_RESULT\n",
            "label": "curl · retained exact creation",
            "language": "bash"
          },
          {
            "code": "from openai import OpenAI\nimport os\n\nclient=OpenAI(base_url=\"https://api.ailoot.ai/v1\",api_key=os.environ[\"AILOOT_API_KEY\"],max_retries=0)\nresult=client.embeddings.create(model='sentence-transformers/all-minilm-l12-v2',input=[\"First document\",\"Second document\"])\nprint(result)\nfor item in result.data:print(item.index,item.embedding)\n",
            "label": "Python SDK · all vectors",
            "language": "python"
          }
        ],
        "family": "openai",
        "fast_multiplier": null,
        "flex_multiplier": null,
        "fmt": "embeddings",
        "hints": [
          "Keep every vector index and dimension.",
          "Multimodal model name does not admit image objects on this exact path."
        ],
        "http": "POST",
        "in": 0.01,
        "input": [
          "text",
          "token_ids"
        ],
        "label": "Эмбеддинги",
        "max_out": null,
        "max_reasoning_effort_multiplier": null,
        "method_aliases": [],
        "out": 0,
        "output": [
          "embedding"
        ],
        "path": "/v1/embeddings",
        "price_conditions": [
          {
            "label": "Fast mode",
            "value": "Not specified",
            "when": "When fast mode is selected"
          },
          {
            "label": "Flex mode",
            "value": "Not specified",
            "when": "When flex mode is selected"
          },
          {
            "label": "Maximum reasoning effort",
            "value": "Not specified",
            "when": "When maximum reasoning effort uses the default adjustment"
          }
        ],
        "pricing": {
          "billing_mode": "token",
          "image_input_price": null,
          "image_output_price": null,
          "per_request_price": null
        },
        "pricing_profiles": [
          {
            "dimensions": {
              "regime": "base"
            },
            "label": "Base token rates",
            "units": [
              {
                "amount": "0.00001000",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0.00000001",
                "native_quantity": 1,
                "native_unit": "input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "input_token"
              },
              {
                "amount": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "output_price",
                "label": "Output tokens",
                "native_amount_usd_per_unit": "0",
                "native_quantity": 1,
                "native_unit": "output_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "out",
                "unit": "output_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_read_price",
                "label": "Cache read",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "cached_input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "cached_input_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_price",
                "label": "Cache write",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "generic_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "generic_cache_creation_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_1h_price",
                "label": "Cache write, 1 hour",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "one_hour_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "one_hour_cache_creation_token"
              }
            ]
          }
        ],
        "request_model_id": "sentence-transformers/all-minilm-l12-v2",
        "route_label": "Ultra",
        "status_reads": [],
        "tiers": [],
        "units": [
          {
            "amount": "0.00001000",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "input_price",
            "label": "Input tokens",
            "native_amount_usd_per_unit": "0.00000001",
            "native_quantity": 1,
            "native_unit": "input_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "input_token"
          },
          {
            "amount": "0",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "output_price",
            "label": "Output tokens",
            "native_amount_usd_per_unit": "0",
            "native_quantity": 1,
            "native_unit": "output_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "out",
            "unit": "output_token"
          },
          {
            "amount": null,
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "cache_read_price",
            "label": "Cache read",
            "native_amount_usd_per_unit": null,
            "native_quantity": 1,
            "native_unit": "cached_input_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "cached_input_token"
          },
          {
            "amount": null,
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "cache_write_price",
            "label": "Cache write",
            "native_amount_usd_per_unit": null,
            "native_quantity": 1,
            "native_unit": "generic_cache_creation_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "generic_cache_creation_token"
          },
          {
            "amount": null,
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "cache_write_1h_price",
            "label": "Cache write, 1 hour",
            "native_amount_usd_per_unit": null,
            "native_quantity": 1,
            "native_unit": "one_hour_cache_creation_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "one_hour_cache_creation_token"
          }
        ]
      }
    ],
    "model_aliases": [
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        "request_model_id": "sentence-transformers/all-minilm-l12-v2",
        "route_label": "Ultra"
      }
    ],
    "name": "Sentence Transformers: all-MiniLM-L12-v2",
    "price": {
      "in": 0.01,
      "in_label": null,
      "out": 0,
      "out_label": null,
      "scope_label": "Token rates",
      "sort_in": 0.01,
      "sort_out": 0,
      "varies": false
    },
    "public_id": "sentence-transformers/all-minilm-l12-v2",
    "short": "Sentence Transformers all-MiniLM-L12-v2 gaplar va qisqa paragraflarni qidiruv, o‘xshashlik va klasterlash uchun 384 o‘lchamli vektorlarga kodlaydi. Rasmiy usul o‘rtacha pooling va normallashdan foydalanadi; odatiy kesish chegarasi 256 ta word piece birligi."
  },
  "renderer_schema": {
    "method_order": "Original DTO methods order; every exact route/format/path/caller survives.",
    "prices": "Original public DTO fields and original native display HTML; no flattened or normalized offers."
  },
  "representation": "exact-public-dto-and-native-renderer-html",
  "representationNotes": {
    "current_reference": {
      "about_json_pointer": "/aboutHtml",
      "methods": [
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          "contract_json_pointer": "/methods/0/rawMethod/contract",
          "html_json_pointer": "/methods/0/html",
          "method_ordinal": 0
        }
      ],
      "role": "current-semantic-reference"
    },
    "raw_input": {
      "examples_role": "historical-input-not-current-ready-examples",
      "json_pointers": [
        "/rawModel",
        "/methods/0/rawMethod"
      ],
      "role": "historical-owner-input"
    },
    "schema": "ailoot-native-model-representations-v1"
  },
  "schema": "ailoot-static-model-facts-v1",
  "schema_version": 1,
  "shortText": "Sentence Transformers all-MiniLM-L12-v2 gaplar va qisqa paragraflarni qidiruv, o‘xshashlik va klasterlash uchun 384 o‘lchamli vektorlarga kodlaydi. Rasmiy usul o‘rtacha pooling va normallashdan foydalanadi; odatiy kesish chegarasi 256 ta word piece birligi.",
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