/ Модель

Характеристики

Контекст
32 768 токенов
Параметры
508 млн
Архитектура
qwen3 · dense
Базовая модель
Qwen3-Embedding-0.6B
Библиотека
sentence-transformers
Опубликована
24 янв 2026

Qwen3-Embedding-0.6B-netsecgame-finetuned-pairs

ЭмбеддингиТекст → embedding3 профессии

Дообученная на парах NetSecGame модель Qwen3 создаёт текстовые эмбеддинги для семантического поиска и сопоставления предложений.

124скачиваний за 30 дней0лайковвеса обновлены3 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

Модель stratosphere/Qwen3-Embedding-0.6B-netsecgame-finetuned-pairs — текстовый Sentence Transformer, дообученный на наборе пар netsecgame-embedding-finetuning-pairs на базе Qwen/Qwen3-Embedding-0.6B. Она преобразует предложения и абзацы в плотные векторы размерности 1024; архитектура включает Qwen3Model, пулинг последнего токена и нормализацию. Максимальная длина последовательности составляет 32 768 токенов, сходство можно оценивать косинусной мерой.

Модель подходит для семантического поиска и сравнения текстов, поиска перефразировок, кластеризации и классификации. Обучающие пары связаны с NetSecGame, поэтому модель может быть особенно полезна для семантического сопоставления текстов в этом сценарии. В README приведены метрики бинарной классификации на валидационном наборе topology_val, но их нельзя считать универсальной оценкой качества для других задач. Лицензия в предоставленных данных не указана.

Сценарии применения

  • семантический поиск по описаниям и журналам NetSecGame
  • сопоставление похожих текстовых состояний сети
  • оценка семантического сходства пар предложений
  • поиск перефразировок в технических описаниях
  • кластеризация текстовых записей по смыслу
  • классификация текста на основе эмбеддингов

Ограничения

Дообучение ориентировано на пары текстов NetSecGame, поэтому качество на других доменах может отличаться; приведённые метрики относятся только к указанной в README проверке. Язык обучающих данных явно не обозначен.

Для каких профессий подходит3

Разделы каталога профессий, в задачах которых используется эта модель

Все профессии

Карточка модели

README от автора на Hugging Face, как он его опубликовал

Открыть на Hugging Face

SentenceTransformer based on Qwen/Qwen3-Embedding-0.6B

This is a sentence-transformers model finetuned from Qwen/Qwen3-Embedding-0.6B on the netsecgame-embedding-finetuning-pairs dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

Model Sources

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 32768, 'do_lower_case': False, 'architecture': 'Qwen3Model'})
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': True, '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("stratosphere/Qwen3-Embedding-0.6B-netsecgame-finetuned-pairs")
# Run inference
queries = [
    "Current status:\nControlled hosts are 10.7.44.3 and 10.7.44.5 and 10.7.44.4 and 54.123.53.21 and 10.7.44.2\nKnown networks are 10.7.45.0/24 and 54.123.53.29/26 and 10.7.44.0/24 and 10.7.43.0/24\nKnown hosts are 10.7.45.4 and 10.7.45.3 and 10.7.44.6 and 10.7.44.1 and 10.7.45.2\nKnown services for host 10.7.44.2 are powershell and 3389/tcp, ms-wbt-server and \nKnown services for host 10.7.44.5 are 22/tcp, ssh and bash and \nKnown services for host 54.123.53.21 are listener and bash and \nKnown services for host 10.7.44.4 are 22/tcp, ssh and bash and \nKnown services for host 10.7.44.3 are 3389/tcp, ms-wbt-server and \nKnown services for host 10.7.45.2 are 3389/tcp, ms-wbt-server and 445/tcp, microsoft-ds and \nKnown data are none\n",
]
documents = [
    'Current status:\nControlled hosts are 172.19.2.2 and 101.32.5.23 and 172.19.1.4 and 172.19.1.3 and 172.19.1.2\nKnown networks are 54.123.53.29/26 and 172.19.0.0/24 and 172.19.1.0/24 and 172.19.2.0/24\nKnown hosts are 172.19.2.4 and 172.19.2.3 and 172.19.1.5 and 172.19.1.1 and 172.19.1.6\nKnown services for host 172.19.1.4 are bash and 22/tcp, ssh and \nKnown services for host 172.19.1.3 are 3389/tcp, ms-wbt-server and \nKnown services for host 101.32.5.23 are bash and listener and \nKnown services for host 172.19.1.5 are 22/tcp, ssh and \nKnown services for host 172.19.2.3 are 22/tcp, ssh and 5432/tcp, postgresql and \nKnown services for host 172.19.1.2 are 3389/tcp, ms-wbt-server and \nKnown services for host 172.19.2.2 are 3389/tcp, ms-wbt-server and 445/tcp, microsoft-ds and \nKnown data are none\n',
    'Current status:\nControlled hosts are 172.20.7.6 and 32.8.23.1\nKnown networks are 172.20.8.0/24 and 172.20.6.0/24 and 172.20.7.0/24 and 32.8.23.1/26\nKnown hosts are 172.20.7.2 and 172.20.7.4 and 172.20.8.3 and 172.20.8.2 and 172.20.7.3 and 172.20.7.1 and 172.20.7.5 and 172.20.8.4\nKnown services for host 172.20.8.2 are 445/tcp, microsoft-ds and 3389/tcp, ms-wbt-server and \nKnown services for host 172.20.8.4 are 22/tcp, ssh and 80/tcp, http and \nKnown services for host 172.20.7.6 are bash and \nKnown data are none\n',
    'Current status:\nControlled hosts are 172.20.8.3 and 172.20.7.2 and 32.8.23.1 and 172.20.8.4\nKnown networks are 172.20.8.0/24 and 172.20.6.0/24 and 172.20.7.0/24 and 32.8.23.1/26\nKnown hosts are 172.20.7.6 and 172.20.7.4 and 172.20.8.2 and 172.20.7.3 and 172.20.7.1 and 172.20.7.5\nKnown services for host 32.8.23.1 are listener and bash and \nKnown services for host 172.20.7.2 are powershell and 3389/tcp, ms-wbt-server and \nKnown services for host 172.20.8.4 are 22/tcp, ssh and bash and 80/tcp, http and \nKnown services for host 172.20.8.3 are 22/tcp, ssh and 5432/tcp, postgresql and \nKnown data for host 172.20.8.4 are (User2, WebServerData) and \n',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 1024] [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[0.5508, 0.4785, 0.4883]], dtype=torch.bfloat16)

Evaluation

Metrics

Binary Classification

MetricValue
cosine_accuracy0.9147
cosine_accuracy_threshold0.9851
cosine_f10.9104
cosine_f1_threshold0.985
cosine_precision0.9475
cosine_recall0.8761
cosine_ap0.9741
cosine_mcc0.8316

Training Details

Training Dataset

netsecgame-embedding-finetuning-pairs

  • Dataset: netsecgame-embedding-finetuning-pairs at 1a27835
  • Size: 21,011 training samples
  • Columns: sentence1, sentence2, and label
  • Approximate statistics based on the first 1000 samples:
    sentence1sentence2label
    typestringstringfloat
    detailsmin: 111 tokensmean: 338.8 tokensmax: 577 tokensmin: 111 tokensmean: 339.66 tokensmax: 658 tokensmin: 0.0mean: 0.5max: 1.0
  • Samples:
    sentence1sentence2label
    Current status:Controlled hosts are 32.8.23.1 and 172.20.7.3Known networks are 172.20.8.0/24 and 172.20.6.0/24 and 172.20.7.0/24 and 32.8.23.1/26Known hosts are 172.20.7.2 and 172.20.7.6 and 172.20.7.4 and 172.20.8.3 and 172.20.8.2 and 172.20.7.1 and 172.20.7.5 and 172.20.8.4Known services for host 32.8.23.1 are listener and bash and Known services for host 172.20.7.2 are 3389/tcp, ms-wbt-server and Known services for host 172.20.7.5 are 22/tcp, ssh and Known data are noneCurrent status:Controlled hosts are 32.8.23.1 and 172.20.7.5Known networks are 172.20.8.0/24 and 172.20.6.0/24 and 172.20.7.0/24 and 32.8.23.1/26Known hosts are 172.20.7.2 and 172.20.7.6 and 172.20.7.4 and 172.20.8.3 and 172.20.8.2 and 172.20.7.3 and 172.20.7.1 and 172.20.8.4Known services for host 32.8.23.1 are listener and bash and Known services for host 172.20.7.3 are 3389/tcp, ms-wbt-server and Known services for host 172.20.7.4 are 22/tcp, ssh and Known data are none1.0
    Current status:Controlled hosts are 213.47.23.195 and 192.168.2.3 and 192.168.2.2 and 192.168.2.4Known networks are 192.168.2.0/24 and 213.47.23.192/26 and 192.168.3.0/24 and 192.168.1.0/24Known hosts are 192.168.1.3 and 192.168.2.6 and 192.168.2.5 and 192.168.2.1 and 192.168.1.4 and 192.168.1.2Known services for host 213.47.23.195 are listener and bash and Known services for host 192.168.2.5 are 22/tcp, ssh and Known services for host 192.168.2.3 are powershell and 3389/tcp, ms-wbt-server and Known services for host 192.168.2.4 are 22/tcp, ssh and Known services for host 192.168.2.2 are 3389/tcp, ms-wbt-server and Known data are noneCurrent status:Controlled hosts are 10.7.45.4 and 10.7.44.2 and 10.7.44.4 and 54.123.53.21Known networks are 10.7.45.0/24 and 54.123.53.29/26 and 10.7.44.0/24 and 10.7.43.0/24Known hosts are 10.7.44.3 and 10.7.45.3 and 10.7.44.5 and 10.7.44.6 and 10.7.44.1 and 10.7.45.2Known services for host 54.123.53.21 are listener and bash and Known services for host 10.7.44.4 are 22/tcp, ssh and Known services for host 10.7.44.2 are powershell and 3389/tcp, ms-wbt-server and Known services for host 10.7.45.4 are 22/tcp, ssh and 80/tcp, http and Known data are none0.0
    Current status:Controlled hosts are 172.20.7.2 and 32.8.23.1Known networks are 172.20.8.0/24 and 172.20.6.0/24 and 172.20.7.0/24 and 32.8.23.1/26Known hosts are 172.20.7.6 and 172.20.7.4 and 172.20.8.3 and 172.20.8.2 and 172.20.7.3 and 172.20.7.1 and 172.20.7.5 and 172.20.8.4Known services for host 172.20.7.2 are powershell and 3389/tcp, ms-wbt-server and Known services for host 172.20.7.3 are 3389/tcp, ms-wbt-server and Known services for host 32.8.23.1 are listener and bash and Known data are noneCurrent status:Controlled hosts are 32.8.23.1 and 172.20.7.3Known networks are 172.20.8.0/24 and 172.20.6.0/24 and 172.20.7.0/24 and 32.8.23.1/26Known hosts are 172.20.7.2 and 172.20.7.6 and 172.20.7.4 and 172.20.8.3 and 172.20.8.2 and 172.20.7.1 and 172.20.7.5 and 172.20.8.4Known services for host 172.20.7.3 are powershell and 3389/tcp, ms-wbt-server and Known services for host 172.20.7.2 are 3389/tcp, ms-wbt-server and Known services for host 32.8.23.1 are listener and bash and Known data are none1.0
  • Loss: ContrastiveLoss with these parameters:
    {
        "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
        "margin": 0.5,
        "size_average": true
    }
    

Evaluation Dataset

netsecgame-embedding-finetuning-pairs

  • Dataset: netsecgame-embedding-finetuning-pairs at 1a27835
  • Size: 2,626 evaluation samples
  • Columns: sentence1, sentence2, and label
  • Approximate statistics based on the first 1000 samples:
    sentence1sentence2label
    typestringstringfloat
    detailsmin: 147 tokensmean: 338.35 tokensmax: 567 tokensmin: 147 tokensmean: 339.84 tokensmax: 599 tokensmin: 0.0mean: 0.51max: 1.0
  • Samples:
    sentence1sentence2label
    Current status:Controlled hosts are 213.47.23.195 and 192.168.2.3 and 192.168.2.2Known networks are 192.168.2.0/24 and 213.47.23.192/26 and 192.168.3.0/24 and 192.168.1.0/24Known hosts are 192.168.2.4 and 192.168.1.3 and 192.168.2.6 and 192.168.2.5 and 192.168.2.1 and 192.168.1.4 and 192.168.1.2Known services for host 192.168.2.2 are powershell and 3389/tcp, ms-wbt-server and Known services for host 213.47.23.195 are listener and bash and Known services for host 192.168.2.5 are 22/tcp, ssh and Known services for host 192.168.2.4 are 22/tcp, ssh and Known services for host 192.168.2.3 are 3389/tcp, ms-wbt-server and Known data are noneCurrent status:Controlled hosts are 213.47.23.195 and 192.168.2.3 and 192.168.2.2Known networks are 192.168.2.0/24 and 213.47.23.192/26 and 192.168.3.0/24 and 192.168.1.0/24Known hosts are 192.168.2.4 and 192.168.1.3 and 192.168.2.6 and 192.168.2.5 and 192.168.2.1 and 192.168.1.4 and 192.168.1.2Known services for host 192.168.2.3 are powershell and 3389/tcp, ms-wbt-server and Known services for host 213.47.23.195 are listener and bash and Known services for host 192.168.2.5 are 22/tcp, ssh and Known services for host 192.168.2.4 are 22/tcp, ssh and Known services for host 192.168.2.2 are 3389/tcp, ms-wbt-server and Known data are none1.0
    Current status:Controlled hosts are 10.7.45.4 and 10.7.44.2 and 10.7.44.4 and 54.123.53.21Known networks are 10.7.45.0/24 and 54.123.53.29/26 and 10.7.44.0/24 and 10.7.43.0/24Known hosts are 10.7.44.3 and 10.7.45.3 and 10.7.44.5 and 10.7.44.6 and 10.7.44.1 and 10.7.45.2Known services for host 54.123.53.21 are listener and bash and Known services for host 10.7.44.4 are 22/tcp, ssh and Known services for host 10.7.44.2 are powershell and 3389/tcp, ms-wbt-server and Known services for host 10.7.45.4 are 22/tcp, ssh and 80/tcp, http and Known data are noneCurrent status:Controlled hosts are 172.19.1.2 and 101.32.5.23Known networks are 54.123.53.29/26 and 172.19.0.0/24 and 172.19.1.0/24 and 172.19.2.0/24Known hosts are 172.19.2.2 and 172.19.1.4 and 172.19.2.4 and 172.19.2.3 and 172.19.1.5 and 172.19.1.1 and 172.19.1.3 and 172.19.1.6Known services for host 101.32.5.23 are bash and listener and Known services for host 172.19.1.4 are 22/tcp, ssh and Known services for host 172.19.1.2 are powershell and 3389/tcp, ms-wbt-server and Known services for host 172.19.2.4 are 22/tcp, ssh and 80/tcp, http and Known data are none0.0
    Current status:Controlled hosts are 172.20.7.4 and 172.20.7.2 and 32.8.23.1 and 172.20.8.2Known networks are 172.20.8.0/24 and 172.20.6.0/24 and 172.20.7.0/24 and 32.8.23.1/26Known hosts are 172.20.7.6 and 172.20.8.3 and 172.20.7.3 and 172.20.7.1 and 172.20.7.5 and 172.20.8.4Known services for host 172.20.7.2 are powershell and 3389/tcp, ms-wbt-server and Known services for host 172.20.7.4 are 22/tcp, ssh and Known services for host 172.20.7.3 are 3389/tcp, ms-wbt-server and Known services for host 172.20.8.2 are 445/tcp, microsoft-ds and 3389/tcp, ms-wbt-server and Known services for host 32.8.23.1 are listener and bash and Known services for host 172.20.7.5 are 22/tcp, ssh and Known services for host 172.20.8.4 are 22/tcp, ssh and 80/tcp, http and Known data are noneCurrent status:Controlled hosts are 10.0.47.2 and 10.0.46.6 and 55.34.2.5 and 10.0.47.4Known networks are 10.0.46.0/24 and 10.0.45.0/24 and 55.34.2.4/26 and 10.0.47.0/24Known hosts are 10.0.46.1 and 10.0.47.3 and 10.0.46.2 and 10.0.46.5 and 10.0.46.4 and 10.0.46.3Known services for host 55.34.2.5 are bash and listener and Known services for host 10.0.46.5 are 22/tcp, ssh and Known services for host 10.0.47.2 are 3389/tcp, ms-wbt-server and 445/tcp, microsoft-ds and Known services for host 10.0.46.6 are bash and Known services for host 10.0.47.3 are 22/tcp, ssh and 5432/tcp, postgresql and Known services for host 10.0.46.3 are 3389/tcp, ms-wbt-server and Known services for host 10.0.47.4 are 22/tcp, ssh and 80/tcp, http and Known data are none0.0
  • Loss: ContrastiveLoss with these parameters:
    {
        "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
        "margin": 0.5,
        "size_average": true
    }
    

Training Hyperparameters

Non-Default Hyperparameters

  • eval_strategy: steps
  • gradient_accumulation_steps: 4
  • learning_rate: 1e-06
  • weight_decay: 0.01
  • num_train_epochs: 1
  • load_best_model_at_end: True

All Hyperparameters

  • do_predict: False
  • eval_strategy: steps
  • prediction_loss_only: True
  • per_device_train_batch_size: 8
  • per_device_eval_batch_size: 8
  • gradient_accumulation_steps: 4
  • eval_accumulation_steps: None
  • torch_empty_cache_steps: None
  • learning_rate: 1e-06
  • weight_decay: 0.01
  • adam_beta1: 0.9
  • adam_beta2: 0.999
  • adam_epsilon: 1e-08
  • max_grad_norm: 1.0
  • num_train_epochs: 1
  • max_steps: -1
  • lr_scheduler_type: linear
  • lr_scheduler_kwargs: None
  • warmup_ratio: None
  • warmup_steps: 0
  • 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: False
  • 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 Losstopology_val_cosine_ap
0.0152100.0446--
0.0305200.0403--
0.0457300.0391--
0.0609400.0387--
0.0761500.0385--
0.0914600.0334--
0.1066700.0354--
0.1218800.0325--
0.1370900.0309--
0.15231000.0245--
0.16751100.0292--
0.18271200.0275--
0.19791300.0260--
0.21321400.0229--
0.22841500.0226--
0.24361600.0227--
0.25891700.0230--
0.27411800.0238--
0.28931900.0248--
0.30452000.0254--
0.31982100.0207--
0.33502200.0237--
0.35022300.0234--
0.36542400.0181--
0.38072500.0201--
0.39592600.0216--
0.41112700.0183--
0.42632800.0203--
0.44162900.0196--
0.45683000.0179--
0.47203100.0170--
0.48723200.0209--
0.50253300.0204--
0.51773400.0204--
0.53293500.0232--
0.54823600.0213--
0.56343700.0214--
0.57863800.0212--
0.59383900.0188--
0.60914000.0165--
0.62434100.0208--
0.63954200.0203--
0.65474300.0193--
0.67004400.0236--
0.68524500.0191--
0.70044600.0207--
0.71564700.0215--
0.73094800.0253--
0.74614900.0189--
0.76135000.02320.01950.9741
0.77665100.0204--
0.79185200.0174--
0.80705300.0163--
0.82225400.0173--
0.83755500.0199--
0.85275600.0198--
0.86795700.0213--
0.88315800.0180--
0.89845900.0179--
0.91366000.0193--
0.92886100.0240--
0.94406200.0203--
0.95936300.0203--
0.97456400.0194--
0.98976500.0214--
  • The bold row denotes the saved checkpoint.

Framework Versions

  • Python: 3.10.12
  • Sentence Transformers: 5.2.2
  • Transformers: 5.0.0
  • PyTorch: 2.10.0+cu128
  • Accelerate: 1.12.0
  • Datasets: 4.5.0
  • 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",
}

ContrastiveLoss

@inproceedings{hadsell2006dimensionality,
    author={Hadsell, R. and Chopra, S. and LeCun, Y.},
    booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
    title={Dimensionality Reduction by Learning an Invariant Mapping},
    year={2006},
    volume={2},
    number={},
    pages={1735-1742},
    doi={10.1109/CVPR.2006.100}
}

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
Qwen3Model
Ревизия весов
5f0f9ed

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