/ Модель

Характеристики

Контекст
8 194 токена
Параметры
659 млн
Архитектура
xlm-roberta · dense
Базовая модель
bge-m3
Библиотека
sentence-transformers
Опубликована
6 авг 2026

k_model

ЭмбеддингиТекст → embedding

Дообученная на BAAI/bge-m3 модель создаёт текстовые эмбеддинги для семантического поиска и сравнения предложений и документов.

13скачиваний за 30 дней0лайковвеса обновлены6 авг 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

EmiliaLee/k_model — дообученная на базе BAAI/bge-m3 модель Sentence Transformers для получения плотных векторных представлений предложений и абзацев. Её архитектура включает XLM-RoBERTa, CLS-пулинг и нормализацию; размер вектора составляет 1024 измерения. Максимальная длина входной последовательности — 8192 токена, функция сходства — косинусная.

Модель предназначена для семантического поиска и сравнения текстов, поиска перефразировок, кластеризации и классификации на основе эмбеддингов. Её можно применять для подбора релевантных документов или оценки близости запросов и текстовых фрагментов. В README указан обучающий набор из 284 573 примеров с якорными, положительными и отрицательными текстами. Модель принимает текст и выдаёт векторы; лицензия в предоставленных данных не указана.

Сценарии применения

  • Поиск документов по смыслу запроса
  • Оценка семантической близости двух текстов
  • Поиск перефразировок в коллекции предложений
  • Кластеризация текстов по смыслу
  • Подготовка эмбеддингов для классификации текстов

Ограничения

Результат представляет собой вектор, поэтому для поиска или сравнения требуется последующая обработка эмбеддингов. Языки обучения и лицензия в предоставленных данных не указаны.

Карточка модели

README от автора на Hugging Face, как он его опубликовал

Открыть на Hugging Face

SentenceTransformer based on BAAI/bge-m3

This is a sentence-transformers model finetuned from BAAI/bge-m3. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, classification, clustering, and more.

Model Details

Model Description

  • Model Type: Sentence Transformer
  • Base model: BAAI/bge-m3
  • Maximum Sequence Length: 8192 tokens
  • Output Dimensionality: 1024 dimensions
  • Similarity Function: Cosine Similarity
  • Supported Modality: Text

Model Sources

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'XLMRobertaModel'})
  (1): Pooling({'embedding_dimension': 1024, 'pooling_mode': 'cls', '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 = [
    'compact garage storage shed under 3 feet wide',
    'outdoor 2 ft. 9 in . w x 2 ft. d plastic vertical tool shed Sheds with its tall profile , this 22 cubic foot vertical shed is great for storing ladders , long-handled tools , garden accessories , and more when space is at a premium . the durable , all-weather resin construction provides water resistance and uv protection , while the multi-wall panels are engineere interiorwidth-sidetoside:27|interiorheight-toptobottom:69|compatibleshelfpartnumber : xa1182|windrating:65|overallwidth-sidetoside:33|countryoforigin : united states|interiordepth-fronttoback:20.25|levelofassembly : full assembly needed|countryoforigin-additionaldetails : made in usa|doorwidth-sidet',
    'garden plastic/metal storage shed Sheds easily add storage space and value to your property with an outdoor garden shed . the metal shed has distinct advantages over traditional wooden models that make it a much more attractive choice for purchase . a metal shed is more affordable and being pre-fabricated significantly cuts down on expens dsmetallic : steel|shedtype : storage shed|color : grey|construction : traditional|overallheight-toptobottom:75.5|countryoforigin-additionaldetails : made in china|warrantylength:1 year|levelofassembly : full assembly needed|overallwidth-sidetoside:126.75|overalldepth-fronttoback:109|overallproductw',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.5560, 0.5587],
#         [0.5560, 1.0000, 0.5424],
#         [0.5587, 0.5424, 1.0000]])

Training Details

Training Dataset

Unnamed Dataset

  • Size: 284,573 training samples
  • Columns: anchor, positive, and negative
  • Approximate statistics based on the first 100 samples:
    anchorpositivenegative
    typestringstringstring
    modalitytexttexttext
    detailsmin: 7 tokensmean: 10.2 tokensmax: 16 tokensmin: 115 tokensmean: 173.42 tokensmax: 196 tokensmin: 97 tokensmean: 168.43 tokensmax: 205 tokens
  • Samples:
    anchorpositivenegative
    modern 10 inch semi-flush mount light for entrywaybucholz 1 -light 10 '' semi flush mount Flush Mount Lighting this 1-light semi-flush mount has a sleek , streamlined shape that gives a modern look to your hallway , entryway , or over your dining room table . it has a metal frame and downrod , and an egg-shaped glass shade with an opening at the bottom for light to shine through . the downrod retracts and ro shadematerial : glass|dry , damporwetlocationlisted : dry|whatisdrydamporwetlocationlisted : this indicates whether the fixture is safe to use in dry locations , damp locations ( moist environments ) , or wet locations ( direct exposure to water ) .|estimatedtimetosetup:5|bodyheight-toptobottom:10|wweatherford 1 - light 10 '' semi flush mount Flush Mount Lighting marrying modern minimalism and factory flair , this understated one-light flush mount brings a splash of industrial style as it shines a light down on your space . crafted of metal , this fixture features a round canopy and a conical shade with its upper portion left open to provide a peek at the so canopywidth-sidetoside:4.875|recommendedbulbshapecode : a19|warrantylength:1 year|canopydepth-fronttoback:0.75|powersource : hardwired|dssecondaryproductstyle : nautical|canopyheight-toptobottom:4.875|recommendedbulbshape : standard|producttype : flush mount|whatisullisted : the underwriters laborat
    sleek glass pendant light for dining roombucholz 1 -light 10 '' semi flush mount Flush Mount Lighting this 1-light semi-flush mount has a sleek , streamlined shape that gives a modern look to your hallway , entryway , or over your dining room table . it has a metal frame and downrod , and an egg-shaped glass shade with an opening at the bottom for light to shine through . the downrod retracts and ro shadematerial : glass|dry , damporwetlocationlisted : dry|whatisdrydamporwetlocationlisted : this indicates whether the fixture is safe to use in dry locations , damp locations ( moist environments ) , or wet locations ( direct exposure to water ) .|estimatedtimetosetup:5|bodyheight-toptobottom:10|w6 '' h glass sphere pendant shade ( screw on ) in smoke color : smoke|shape : sphere|overallproductweight:0.67|overalldepth-fronttoback:6|theme : no theme|attachmenttype : screw on|style : modern & contemporary|overallwidth-sidetoside:6|fittersize:3.25|producttype : pendant shade|primarymaterial : glass|overallheight-toptobottom:6| : no|fitterincluded :
    small metal frame flush mount light with glass shadebucholz 1 -light 10 '' semi flush mount Flush Mount Lighting this 1-light semi-flush mount has a sleek , streamlined shape that gives a modern look to your hallway , entryway , or over your dining room table . it has a metal frame and downrod , and an egg-shaped glass shade with an opening at the bottom for light to shine through . the downrod retracts and ro shadematerial : glass|dry , damporwetlocationlisted : dry|whatisdrydamporwetlocationlisted : this indicates whether the fixture is safe to use in dry locations , damp locations ( moist environments ) , or wet locations ( direct exposure to water ) .|estimatedtimetosetup:5|bodyheight-toptobottom:10|w3 - light semi flush mount Flush Mount Lighting|Commercial Ceiling Lighting it is perfect to transform your dining room or entryway with this charming transitional flush mount ceiling light . bodydepth-fronttoback:16.1|producttype : semi flush mount|recommendedbulbshape : candle|bodydepth-fronttoback:12.2|lightdirection : up|warrantylength:2 years|dssecondaryproductstyle : classic glam|dsprimaryproductstyle : glam|fixturedesign : unique/statement|craftsmanshiptype : handcrafted / hand-fo
  • Loss: MultipleNegativesRankingLoss with these parameters:
    {
        "scale": 20.0,
        "similarity_fct": "cos_sim",
        "gather_across_devices": false,
        "directions": [
            "query_to_doc"
        ],
        "partition_mode": "joint",
        "hardness_mode": null,
        "hardness_strength": 0.0
    }
    

Evaluation Dataset

Unnamed Dataset

  • Size: 14,978 evaluation samples
  • Columns: anchor, positive, and negative
  • Approximate statistics based on the first 100 samples:
    anchorpositivenegative
    typestringstringstring
    modalitytexttexttext
    detailsmin: 8 tokensmean: 11.03 tokensmax: 18 tokensmin: 110 tokensmean: 159.21 tokensmax: 193 tokensmin: 90 tokensmean: 168.18 tokensmax: 201 tokens
  • Samples:
    anchorpositivenegative
    quirky rooster shelf sitters4 piece rooster shelf sitter figurine set Decorative Objects add these colorful rooster sitters to shelves , windowsills , and more throughout your home . the set includes four hand-painted roosters with delightful floral accents . each cute sitter has dangling legs and plenty of charm . overallwidth-sidetoside:1.63|geometricshapes : no geometric & shapes|producttype : figurine|subject : animals|nature : no nature|spiritualreligious : no spiritual & religious|structuresbuildings : no structures & buildings|wordstext : no words & text|overallproductweight:0.25|quantity:4|entertainmendunellen farmhouse rooster 2 piece figurine set Decorative Objects be the early bird in grabbing these farmhouse silver metal and wood rooster sculpture by the head and complete your farmhouse set up now . this 2-piece set features two small and medium standing rooster sculptures with a gray finish on the body , and silver on the rest . this will look great as a ce overallwidth-sidetoside:13|color : brown , silver|purposefuldistressingtype : distressed metal|style : farmhouse / country|overallheight-toptobottom:5.1|fantasysci-fi : no fantasy & sci-fi|overallheight-toptobottom:16|nature : no nature|geometricshapes : no geometric & shapes|spiritualreligious : no
    decorative rooster figurines with floral accents4 piece rooster shelf sitter figurine set Decorative Objects add these colorful rooster sitters to shelves , windowsills , and more throughout your home . the set includes four hand-painted roosters with delightful floral accents . each cute sitter has dangling legs and plenty of charm . overallwidth-sidetoside:1.63|geometricshapes : no geometric & shapes|producttype : figurine|subject : animals|nature : no nature|spiritualreligious : no spiritual & religious|structuresbuildings : no structures & buildings|wordstext : no words & text|overallproductweight:0.25|quantity:4|entertainmen2 piece metal figurine set Decorative Objects the antique style of these two metal rooster decors will bring a fascinating appeal to any rustic , vintage , or antique accent table . with its iron construction , these rooster sculptures will continue to enhance the look of your home interior for years to come . featuring a decorative rustic bron geometricshapes : no geometric & shapes|spiritualreligious : no spiritual & religious|overallwidth-sidetoside:20.47|producttype : figurine|nature : no nature|foodbeverage : no food & beverage|fantasysci-fi : no fantasy & sci-fi|people : no people|agegroup : adult|animals : rooster|overallproductweig
    comfortable anti-fatigue mat for kitchen countercatrisha soothing foam comfort anti-fatigue mat Kitchen Mats this anti-fatigue mat provides a full inch of soothing foam comfort for your tired feet . perfect in front of a kitchen counter and has a skid-resistant backing . color : red|pattern : solid color|backingmaterial : polyester|color : brown|shape : rectangle|supplierintendedandapproveduse : non residential use|productcare : machine washable|construction : machine made|color : blue|dssecondaryproductstyle : transitional modern|overalllength-endtoend:30|color : ggrano hello standing anti-fatigue mat Kitchen Mats use this mat in any part of your home . whether it be the kitchen , laundry room , bathroom , or doorway this elegant mat will bring luscious texture , enduring material , and comfortable steps for your convenience . this perfectly crafted pvc mat is joined with a rubber backing for long-lasting usa pattern : no pattern and not solid color|overalllength-endtoend:30|overalllength-endtoend:47|overallproductweight:2.09|holidayoccasion : no holiday|dssecondaryproductstyle : modern farmhouse|overallheight-toptobottom:0.47|material : synthetics|theme : text|materialdetails:100 % pvc|overallwidth-side
  • Loss: MultipleNegativesRankingLoss with these parameters:
    {
        "scale": 20.0,
        "similarity_fct": "cos_sim",
        "gather_across_devices": false,
        "directions": [
            "query_to_doc"
        ],
        "partition_mode": "joint",
        "hardness_mode": null,
        "hardness_strength": 0.0
    }
    

Training Hyperparameters

Non-Default Hyperparameters

  • per_device_train_batch_size: 64
  • num_train_epochs: 1
  • learning_rate: 2e-05
  • warmup_steps: 0.1
  • bf16: True
  • per_device_eval_batch_size: 64
  • dataloader_num_workers: 4

All Hyperparameters

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

Training Logs

EpochStepTraining LossValidation Loss
0.02251001.1596-
0.04502000.6243-
0.06753000.5484-
0.08994000.4963-
0.11245000.46650.4796
0.13496000.4426-
0.15747000.4353-
0.17998000.4284-
0.20249000.4111-
0.224910000.39460.4274
0.247411000.3883-
0.269812000.3806-
0.292313000.3590-
0.314814000.3661-
0.337315000.36320.4086
0.359816000.3717-
0.382317000.3517-
0.404818000.3428-
0.427319000.3574-
0.449720000.34120.3882
0.472221000.3340-
0.494722000.3356-
0.517223000.3261-
0.539724000.3358-
0.562225000.32080.3833
0.584726000.3206-
0.607227000.3254-
0.629628000.3215-
0.652129000.3082-
0.674630000.30990.3684
0.697131000.3060-
0.719632000.3009-
0.742133000.3021-
0.764634000.3163-
0.787035000.30200.3652
0.809536000.3012-
0.832037000.2927-
0.854538000.2878-
0.877039000.2974-
0.899540000.31250.3584
0.922041000.2938-
0.944542000.2997-
0.966943000.2915-
0.989444000.2923-
1.04447-0.3572

Training Time

  • Training: 26.3 minutes
  • Evaluation: 3.2 minutes
  • Total: 29.5 minutes

Framework Versions

  • Python: 3.12.13
  • Sentence Transformers: 5.7.0
  • Transformers: 5.14.1
  • PyTorch: 2.13.0+cu130
  • Accelerate: 1.14.0
  • Datasets: 5.0.1
  • Tokenizers: 0.22.2

Additional Resources

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{oord2019representationlearningcontrastivepredictive,
      title={Representation Learning with Contrastive Predictive Coding},
      author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
      year={2019},
      eprint={1807.03748},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1807.03748},
}

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
XLMRobertaModel
Ревизия весов
fd341c6

0 моделей