/ Модель

Характеристики

Контекст
512 токенов
Параметры
25,9 млн
Архитектура
bert · dense
Базовая модель
all-MiniLM-L6-v2
Библиотека
sentence-transformers
Опубликована
18 фев 2026

Embeddings-Trivia

ЭмбеддингиТекст → embedding10 профессий

Англоязычный энкодер предложений, дообученный на вопросах и ответах для семантического поиска и сравнения текстов.

110скачиваний за 30 дней0лайковвеса обновлены18 фев 2026

Бенчмарки

Как модель справилась с задачами пользователей ailist и что о ней публикуют открытые лидерборды

Тесты пользователейпока нет

Никто ещё не опубликовал тест этой модели. Соберите бенчмарк из своих задач — результат появится здесь.

  1. Добавьте модель в бенчмарк

    Вместе с другими моделями категории «Эмбеддинги» — сравнение идёт внутри одной категории.

  2. Опишите свои задачи

    Напишите запросы, при необходимости приложите файлы и эталонные ответы.

  3. Запустите и опубликуйте

    Модели ответят на каждую задачу, судья оценит ответы. Опубликованный результат появится на этой странице.

Публичные оценки

Из открытых лидербордов. Сравнивайте результаты одного теста при одинаковых условиях — отсутствие оценки не значит, что модель хуже.

В подключённых источниках результатов для этой модели пока нет.

Что умеет модель

Описание составлено по карточке модели и метаданным Hugging Face

VinitT/Embeddings-Trivia — англоязычная модель Sentence Transformer, дообученная разработчиком на наборе trivia-qa-triplet на основе sentence-transformers/all-MiniLM-L6-v2. Она кодирует предложения и абзацы в плотные векторы размерностью 384; архитектура включает BERT-энкодер, усреднение токенных представлений и нормализацию. Для сравнения векторов подходит косинусное сходство. Метаданные указывают 25,9 млн параметров и контекст до 512 токенов; в README указана максимальная длина последовательности 256 токенов.

Модель предназначена для семантического поиска и сравнения текстов, поиска перефразировок, кластеризации и подготовки признаков для классификации. В README приведена точность cosine accuracy 0,834 на оценке TripletEvaluator с trivia_qa_eval. Модель принимает текст и выдаёт векторы; она обучена на англоязычных данных и не предназначена для генерации ответов. Сведения о лицензии в предоставленных источниках отсутствуют.

Сценарии применения

  • семантический поиск по англоязычным документам
  • ранжирование текстов по смысловому сходству
  • поиск перефразировок вопросов и предложений
  • кластеризация текстов по тематическому сходству
  • получение векторных признаков для классификации текстов

Ограничения

Поддерживает англоязычный текст; максимальная длина последовательности указана как 256 токенов в README и 512 токенов в метаданных. Модель создаёт векторы, а не генерирует ответы.

Для каких профессий подходит10

Разделы каталога профессий, в задачах которых используется эта модель

Все профессии

Также подходит для

Карточка модели

README от автора на Hugging Face, как он его опубликовал

Открыть на Hugging Face

SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2 on the trivia-qa-triplet dataset. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

Model Sources

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False, 'architecture': 'BertModel'})
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'In the RAF, what is the rank immediately above Squadron Leader?',
    'Squadron leader Squadron leader Squadron leader (Sqn Ldr in the RAF ; SQNLDR in the RAAF and RNZAF; formerly sometimes S/L in all services) is a commissioned rank in the Royal Air Force and the air forces of many countries which have historical British influence. It is also sometimes used as the English translation of an equivalent rank in countries which have a non-English air force-specific rank structure. An air force squadron leader ranks above flight lieutenant and immediately below wing commander and it is the most junior of the senior officer ranks. The air force rank of squadron leader has a',
    'Squadron leader RAF used major as the equivalent rank to squadron leader. Royal Naval Air Service lieutenant-commanders and Royal Flying Corps majors on 31 March 1918 became RAF majors on 1 April 1918. On 31 August 1919, the RAF rank of major was superseded by squadron leader which has remained in continuous usage ever since. Promotion to squadron leader is strictly on merit, and requires the individual to be appointed to a Career Commission, which will see them remain in the RAF until retirement or voluntary resignation. Before the Second World War, a squadron leader commanded a squadron of aircraft. Today, however,',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.5605, 0.4394],
#         [0.5605, 1.0000, 0.5768],
#         [0.4394, 0.5768, 1.0000]])

Evaluation

Metrics

Triplet

MetricValue
cosine_accuracy0.834

Training Details

Training Dataset

trivia-qa-triplet

  • Dataset: trivia-qa-triplet at bfe9460
  • Size: 59,315 training samples
  • Columns: anchor, positive, and negative
  • Approximate statistics based on the first 1000 samples:
    anchorpositivenegative
    typestringstringstring
    detailsmin: 8 tokensmean: 20.08 tokensmax: 71 tokensmin: 114 tokensmean: 139.64 tokensmax: 226 tokensmin: 113 tokensmean: 138.81 tokensmax: 256 tokens
  • Samples:
    anchorpositivenegative
    "Sirius otherwise known as the ""Dog Star"" can be found in which constellation?"Sirius in fiction below)—and even if such were eventually discovered, with an estimated age of 230 million years the system is too young to have fostered the development of life or a complex biosphere. Sirius AB is the alpha star of the constellation Canis Major (the great dog, sometimes styled as Orion's hunting dog), whence its cognomen "the dog star". The most commonly used proper name of this star comes through the Latin "Sirius", from the Greek Σείριος ("Seirios", "glowing" or "scorcher"). The ancient Greeks observed that the appearance of Sirius heralded the hot and dry "dog days" of summer, and feared thatDog days "Seírios", "Scorcher"), Sothis (, "Sôthis", a transcription of Egyptian "Spdt"), and the Dog Star (, "Kúōn"). The last name reflects the way Sirius follows the constellation Orion into the night sky. Sirius is by far the brightest proper star in the night sky, which caused ancient astronomers to take note of it around the world. In Egypt, its return to the night sky became known as a precursor to the annual flooding of the Nile and was worshipped as the goddess Sopdet. In Greece, it became known as the precursor of the unpleasantly hot phase of the summer. Greek poets
    Which Scottish town is the administrative centre of the Highland Region?Highland Scottish All operations are now part of the Stagecoach Group. Highland Scottish Highland Scottish Omnibuses Ltd was formed as a bus operating subsidiary of the Scottish Transport Group in June 1985 from Highland Omnibuses Ltd, and operated until October 1995 when the company was split into two - Highland Bus & Coach and Highland Country Buses. The companies have since remerged and operate today as Highland Country Buses. From its head office in Seafield Road, Inverness, Highland Scottish operated over the massive geographical, but sparsely populated, area of the Highland region of north west Scotland. Highland Scottish was the largest operatorHighland Scottish in the centre of its 'Highland Country' logo. In January 1996 Highland Country Buses was bought by National Express for £1.8m. Highland Bus & Coach, being the smaller of the two operators, continued to operate with the image its predecessor adopted. The two companies continued to exist under separate ownership until August 1998 when Rapson's bought Highland Country Buses back from National Express for £4m - £2.2m more than Rapson's originally sold the company for. Highland Country Buses is now a wholly owned subsidiary of Rapson's Coaches, and covers the operating area that Highland Scottish had on privatisation. The company
    Which brand of coffee is named after a hotel in Nashville, Tennessee?History of Nashville, Tennessee proprietor thereof, had served a special blend of coffee at the hotel's restaurant, and after drinking a cup of this coffee, Roosevelt proclaimed it "good to the last drop!" Cheek subsequently sold the blend to General Foods and to this day, Maxwell House coffee is enjoyed by millions. In 1913 Nashville was the last of several major cities in Tennessee to adopt a commission form of government, with all members of a small commission elected at-large. Compared to single-member districts, this change resulted in further limiting the political power of any African Americans who were able to vote, as theirRenaissance Nashville Hotel Renaissance Nashville Hotel The Renaissance Nashville Hotel is a hotel in Nashville, Tennessee. The building is 385 feet high with 31 floors. The hotel is physically connected to the Nashville Convention Center and is its anchor hotel. The hotel contains 649 rooms, 24 suites, 25 meeting rooms with 31,000 sq ft of meeting space, and 2 concierge levels including a Starbucks coffee shop, 2 lounges, and a full service restaurant. One of the lounges is located in an enclosed bridge walkway, spanning above Commerce Street, which connects the hotel to a parking garage across the street. This walkway was severely
  • Loss: MultipleNegativesRankingLoss with these parameters:
    {
        "scale": 20.0,
        "similarity_fct": "cos_sim",
        "gather_across_devices": false
    }
    

Evaluation Dataset

trivia-qa-triplet

  • Dataset: trivia-qa-triplet at bfe9460
  • Size: 1,000 evaluation samples
  • Columns: anchor, positive, and negative
  • Approximate statistics based on the first 1000 samples:
    anchorpositivenegative
    typestringstringstring
    detailsmin: 8 tokensmean: 20.18 tokensmax: 103 tokensmin: 111 tokensmean: 139.08 tokensmax: 225 tokensmin: 113 tokensmean: 138.94 tokensmax: 256 tokens
  • Samples:
    anchorpositivenegative
    What African country is projected to pass the United States in population by the year 2055?African immigration to the United States entering the United States. It is estimated that the current population of African immigrants to the United States is about 2.1 million. According to the Migration Policy Institute, as of 2009 two-thirds of the African immigrants were from either East or West Africa. Countries with the most immigrants to the U.S. are Nigeria, Egypt, Ethiopia, Ghana, South Africa, Somalia, Eritrea, and Kenya. Seventy five percent (75%) of the African immigrants to the U.S. come from 12 of the 55 countries, namely Nigeria, Egypt, Ghana, Ethiopia, South Africa, Kenya, Liberia, Somalia, Morocco, Cape Verde, Sierra Leone and Sudan (including what isAfrican immigration to the United States African countries due to many skilled hard-working Africans leaving Africa to seek their economic fortunes in the U.S. mainly and elsewhere. One major factor that contributes to migration from Africa to the United States is labor opportunities. It has been relatively easier for African immigrants for Africans with advanced education to leave and enter international labor markets. In addition, many Africans move to the United States for advanced training. For example, doctors from different African nations would move to the U.S. in order to gain more economic opportunities compared to their home country. However, as more Africans emigrate to the
    Which is the largest species of the turtle family?Leatherback sea turtle Leatherback sea turtle The leatherback sea turtle ("Dermochelys coriacea"), sometimes called the lute turtle or leathery turtle or simply the luth, is the largest of all living turtles and is the fourth-heaviest modern reptile behind three crocodilians. It is the only living species in the genus Dermochelys and family Dermochelyidae. It can easily be differentiated from other modern sea turtles by its lack of a bony shell, hence the name. Instead, its carapace is covered by skin and oily flesh. "Dermochelys" is the only extant genus of the family Dermochelyidae. "Dermochelys coriacea" is the only species in genus "Dermochelys". TheOkavango mud turtle Okavango mud turtle The Okavango mud turtle (Okavango terrapin) ("Pelusios bechuanicus") is a species of turtle in the family Pelomedusidae endemic to Africa. It is found in Angola, Botswana, the Democratic Republic of the Congo, Namibia (Caprivi), Zambia, and Zimbabwe. Found in central Africa, central Angola, northeastern Namibia, northern Botswana, Zimbabwe, and Zambia The Okavango mud turtle is largest species of the genus "Pelusios". The carapace is oval and elongated, with a pronounced dome, and is evenly rounded at the edges which allows the turtle to appear as a smooth rock. The carapace is very dark, often almost black, and
    How many gallons of beer are in an English barrel?Barrel gallon for liquids (the corn gallon of 268.8 cubic inches for solids). In Britain, the wine gallon was replaced by the imperial gallon. The tierce later became the petrol barrel. The tun was originally 256 gallons, which explains from where the quarter, 8 bushels or 64 (wine) gallons, comes. Although it is common to refer to draught beer containers of any size as barrels, in the UK this is strictly correct only if the container holds 36 imperial gallons. The terms "keg" and "cask" refer to containers of any size, the distinction being that kegs are used for beers intendedBeer Barrel Polecats Percy Pomeroy (Eddie Laughton), and work together to flee the prison. They are ultimately captured, and sent to solitary confinement. After nearly half a century later, the graying trio are finally released as senior citizens, in which Curly quips upon leaving "You know what I'm-a gonna do? I'm gonna get myself a tall, big, beautiful bottle of beer!" Moe and Larry become irate and throw Curly back into the jail, leaving him there. The title "Beer Barrel Polecats" is a pun of the song "Beer Barrel Polka". The idea of producing and selling their own beer during Prohibition was borrowed
  • Loss: MultipleNegativesRankingLoss with these parameters:
    {
        "scale": 20.0,
        "similarity_fct": "cos_sim",
        "gather_across_devices": false
    }
    

Training Hyperparameters

Non-Default Hyperparameters

  • eval_strategy: steps
  • per_device_train_batch_size: 64
  • per_device_eval_batch_size: 64
  • learning_rate: 2e-05
  • weight_decay: 0.01
  • num_train_epochs: 4
  • warmup_ratio: 0.1
  • warmup_steps: 0.1
  • fp16: True
  • load_best_model_at_end: True

All Hyperparameters

  • do_predict: False
  • eval_strategy: steps
  • prediction_loss_only: True
  • per_device_train_batch_size: 64
  • per_device_eval_batch_size: 64
  • gradient_accumulation_steps: 1
  • eval_accumulation_steps: None
  • torch_empty_cache_steps: None
  • learning_rate: 2e-05
  • weight_decay: 0.01
  • adam_beta1: 0.9
  • adam_beta2: 0.999
  • adam_epsilon: 1e-08
  • max_grad_norm: 1.0
  • num_train_epochs: 4
  • max_steps: -1
  • lr_scheduler_type: linear
  • lr_scheduler_kwargs: None
  • warmup_ratio: 0.1
  • warmup_steps: 0.1
  • log_level: passive
  • log_level_replica: warning
  • log_on_each_node: True
  • logging_nan_inf_filter: True
  • enable_jit_checkpoint: False
  • save_on_each_node: False
  • save_only_model: False
  • restore_callback_states_from_checkpoint: False
  • use_cpu: False
  • seed: 42
  • data_seed: None
  • bf16: False
  • fp16: True
  • bf16_full_eval: False
  • fp16_full_eval: False
  • tf32: None
  • local_rank: -1
  • ddp_backend: None
  • debug: []
  • dataloader_drop_last: False
  • dataloader_num_workers: 0
  • dataloader_prefetch_factor: None
  • disable_tqdm: False
  • remove_unused_columns: True
  • label_names: None
  • load_best_model_at_end: True
  • ignore_data_skip: False
  • fsdp: []
  • fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
  • accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
  • parallelism_config: None
  • deepspeed: None
  • label_smoothing_factor: 0.0
  • optim: adamw_torch_fused
  • optim_args: None
  • group_by_length: False
  • length_column_name: length
  • project: huggingface
  • trackio_space_id: trackio
  • ddp_find_unused_parameters: None
  • ddp_bucket_cap_mb: None
  • ddp_broadcast_buffers: False
  • dataloader_pin_memory: True
  • dataloader_persistent_workers: False
  • skip_memory_metrics: True
  • push_to_hub: False
  • resume_from_checkpoint: None
  • hub_model_id: None
  • hub_strategy: every_save
  • hub_private_repo: None
  • hub_always_push: False
  • hub_revision: None
  • gradient_checkpointing: False
  • gradient_checkpointing_kwargs: None
  • include_for_metrics: []
  • eval_do_concat_batches: True
  • auto_find_batch_size: False
  • full_determinism: False
  • ddp_timeout: 1800
  • torch_compile: False
  • torch_compile_backend: None
  • torch_compile_mode: None
  • include_num_input_tokens_seen: no
  • neftune_noise_alpha: None
  • optim_target_modules: None
  • batch_eval_metrics: False
  • eval_on_start: False
  • use_liger_kernel: False
  • liger_kernel_config: None
  • eval_use_gather_object: False
  • average_tokens_across_devices: True
  • use_cache: False
  • prompts: None
  • batch_sampler: batch_sampler
  • multi_dataset_batch_sampler: proportional
  • router_mapping: {}
  • learning_rate_mapping: {}

Training Logs

EpochStepTraining LossValidation Losstrivia_qa_eval_cosine_accuracy
0.001110.7126--
0.0539500.7164--
0.10791000.7126--
0.16181500.6888--
0.21572000.6802--
0.26972500.6422--
0.32363000.6562--
0.37763500.6356--
0.43154000.6532--
0.48544500.6106--
0.53945000.61040.54720.7970
0.59335500.6301--
0.64726000.6259--
0.70126500.5759--
0.75517000.6089--
0.80917500.5835--
0.86308000.5890--
0.91698500.5577--
0.97099000.5569--
1.02489500.5427--
1.078710000.46980.50460.8190
1.132710500.4662--
1.186611000.4634--
1.240611500.4597--
1.294512000.4585--
1.348412500.5140--
1.402413000.4542--
1.456313500.4579--
1.510214000.4910--
1.564214500.5067--
1.618115000.48000.48750.8300
1.672115500.4638--
1.726016000.4760--
1.779916500.4699--
1.833917000.4912--
1.887817500.4726--
1.941718000.4764--
1.995718500.4802--
2.049619000.3941--
2.103619500.3991--
2.157520000.41140.47340.838
2.211420500.3981--
2.265421000.4023--
2.319321500.3932--
2.373222000.3887--
2.427222500.3894--
2.481123000.3858--
2.535123500.3907--
2.589024000.3934--
2.642924500.3871--
2.696925000.37630.46810.8310
2.750825500.3997--
2.804726000.3941--
2.858726500.3884--
2.912627000.3771--
2.966627500.4168--
3.020528000.3722--
3.074428500.3565--
3.128429000.3499--
3.182329500.3428--
3.236230000.35830.46690.8320
3.290230500.3444--
3.344131000.3252--
3.398131500.3563--
3.452032000.3465--
3.505932500.3328--
3.559933000.3438--
3.613833500.3330--
3.667734000.3567--
3.721734500.3462--
3.775635000.34350.46390.8340
3.829635500.3532--
3.883536000.3480--
3.937436500.3361--
3.991437000.3628--
  • The bold row denotes the saved checkpoint.

Framework Versions

  • Python: 3.12.12
  • Sentence Transformers: 5.2.2
  • Transformers: 5.0.0
  • PyTorch: 2.9.0+cu128
  • Accelerate: 1.12.0
  • Datasets: 4.0.0
  • Tokenizers: 0.22.2

Citation

BibTeX

Sentence Transformers

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

MultipleNegativesRankingLoss

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

Как запустить

На своей GPU в ailist. Цена — по текущим предложениям провайдеров

GPU в ailist

Универсальный рантайм · sentence-transformers

Можно запустить

Подбираем GPU…

Класс архитектуры
BertModel
Ревизия весов
f1c49cb

0 моделей