/ Модель

Характеристики

Контекст
8 192 токена
Параметры
32,4 млн
Архитектура
bert · dense
Базовая модель
jina-embeddings-v2-small-en
Библиотека
sentence-transformers
Опубликована
12 мар 2025

jina-embeddings-v2-small-en_linkedin_profile_model_run1__modified_config

ЭмбеддингиТекст → embedding10 профессий

Дообученная англоязычная Sentence Transformer преобразует текст в 512-мерные эмбеддинги для семантического поиска и сравнения.

126скачиваний за 30 дней0лайковвеса обновлены12 мар 2025

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

Это дообученная модель Sentence Transformers на основе jinaai/jina-embeddings-v2-small-en. Она преобразует предложения и абзацы в плотные векторы размерностью 512 для сравнения текстов по косинусному сходству. В архитектуре используется JinaBertModel с усреднением токенных представлений; модель рассчитана на английский язык и принимает последовательности длиной до 8192 токенов по метаданным Hugging Face.

Модель предназначена для семантического поиска, оценки сходства предложений, поиска перефразировок, классификации текстов и кластеризации. Эмбеддинги можно применять для поиска по корпусам и построения RAG-систем. В README указан максимальный размер последовательности 2048 токенов, тогда как метаданные указывают нативный контекст 8192 токена; это расхождение следует учитывать при настройке.

Модель опубликована borgcollectivegmbh и сохранена в формате safetensors. Лицензия в предоставленных данных не указана.

Сценарии применения

  • семантический поиск по англоязычным документам
  • поиск похожих предложений и абзацев
  • поиск перефразировок в текстовом корпусе
  • кластеризация англоязычных текстов по смыслу
  • извлечение признаков для классификации текстов
  • поиск релевантных фрагментов для RAG

Ограничения

Модель ориентирована на английский язык. README указывает максимальную длину 2048 токенов, а метаданные — нативный контекст 8192 токена, поэтому допустимую длину входа стоит проверять для используемой конфигурации.

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 jinaai/jina-embeddings-v2-small-en

This is a sentence-transformers model finetuned from jinaai/jina-embeddings-v2-small-en. It maps sentences & paragraphs to a 512-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

https://wandb.ai/deklanw/sentence-transformers/runs/frjg0h13?nw=nwuserdeklanw

Model Description

  • Model Type: Sentence Transformer
  • Base model: jinaai/jina-embeddings-v2-small-en
  • Maximum Sequence Length: 2048 tokens
  • Output Dimensionality: 512 dimensions
  • Similarity Function: Cosine Similarity

Model Sources

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 2048, 'do_lower_case': False}) with Transformer model: JinaBertModel 
  (1): Pooling({'word_embedding_dimension': 512, '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})
)

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("borgcollectivegmbh/jina-embeddings-v2-small-en_linkedin_profile_model_run1")
# Run inference
sentences = [
    'Experienced in SAP consultancy',
    '=== PERSON ===\nMärt Ehrenpreis\nN/A\nTallinn Metropolitan Area\n470\n\n=== ROLES ===\n>>> COMPANY ROLES <<<\n\nCompany Role #1:\n  Board Member CTO\n  2023-04 - N/A\n  N/A\n  Elron / Eesti Liinirongid AS \n  eestiliinirongid\n  Truck Transportation\n  Loome Sulle aega – viime Sind kohale kiiresti, turvaliselt ja mugavalt\n  414\n\nCompany Role #2:\n  Managing Director, Proxion Plan Estonia OÜ\n  2021-01 - 2023-04\n  N/A\n  Proxion\n  proxion-plan-oy\n  Civil Engineering\n  Proxion on osa WSP-yhtiöitä.\nOtathan seurantaan WSP in Finland -tilin!\n  2563\n\n>>> SCHOOL ROLES <<<\nNo school roles listed.',
    "=== PERSON ===\nHarvindar Singh Garcha\nHarvindar's passion for technology can be traced back right from his childhood, Getting into the field of computer science bought him to have more focus on his goal. He is a fast learner and easily adapts to new skills. He always has the hunger and curiosity to learn new skills. <br><br>He has expertise in the design &  development of RESTful APIs and back-end services utilizing Python ecosystem, Django, Flask, Docker, and SQL with an emphasis on scalability and security.<br><br>He is currently working as a full stack developer at AI Automotive company where he has worked on technologies Django, Flask, Python, RESTful APIs, React, Redux, Celery, and JavaScript, while keeping in mind all the secure code practices of OWASP top 10.<br><br>His specialties include quickly learning new skills, Programming languages, and Critical thinking in problem-solving.<br><br>In his free time, you will find him reading articles about the latest trending technologies on Internet,  Spending some time on Quora & hiking on the mountains.\nPune, Maharashtra, India\n1294\n\n=== ROLES ===\n>>> COMPANY ROLES <<<\n\nCompany Role #1:\n  Software Development Analyst\n  2020-04 - 2020-08\n  N/A\n  Metta Social\n  metta-social\n  IT Services and IT Consulting\n  Building world’s largest common good platform to enable sustainable impact at scale!\n  4002\n\nCompany Role #2:\n  Junior Backend Engineer\n  2020-09 - 2021-03\n  N/A\n  SRV Media\n  srv-media\n  Advertising Services\n  Insights | Ideas | Impact\n  49579\n\nCompany Role #3:\n  Full Stack Developer\n  2021-03 - 2022-01\n  N/A\n  SRV Media\n  srv-media\n  Advertising Services\n  Insights | Ideas | Impact\n  49579\n\nCompany Role #4:\n  Software Development Intern\n  2019-10 - 2020-03\n  Currently building a B2B social ecosystem platform where my daily work includes to develop and unit test REST API's using Flask and ORM using SQLAlchemy.<br><br>- Contributed 90% of the API's for mobile application which was build in just a month keeping in mind all the secure code practices of OWASP Top 10.<br><br>- Building REST API using Flask framework, for web app & checking the load balancing of concurrent users.<br>        <br>- Building database model using SQL-Alchemy, and connecting it to Heroku.<br><br>- Security Testing of the API's !\n  Metta Social\n  metta-social\n  IT Services and IT Consulting\n  Building world’s largest common good platform to enable sustainable impact at scale!\n  4002\n\nCompany Role #5:\n  Research And Development Engineer\n  2022-01 - N/A\n  N/A\n  Cerence Inc.\n  cerence\n  Software Development\n  Cerence is the global industry leader in creating unique, moving experiences for the mobility world.\n  44039\n\n>>> SCHOOL ROLES <<<\nNo school roles listed.",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 512]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Binary Classification

  • Datasets: ``, rec_test_evaluator and query_test_evaluator
  • Evaluated with custom_evaluator.BinaryClassificationEvaluator
Metricrec_test_evaluatorquery_test_evaluator
cosine_accuracy0.95270.93690.9622
cosine_accuracy_threshold0.27780.31410.263
cosine_f10.85590.79950.885
cosine_f1_threshold0.25390.28460.2541
cosine_precision0.84740.81650.8894
cosine_recall0.86460.78320.8805
cosine_ap0.92140.870.9475
dot_accuracy0.92870.91740.9483
dot_accuracy_threshold7.19234.78188.4253
dot_f10.77950.74570.8433
dot_f1_threshold6.09994.36567.5479
dot_precision0.76210.74860.8372
dot_recall0.79760.74280.8495
dot_ap0.86510.79540.9205
manhattan_accuracy0.84220.86190.8801
manhattan_accuracy_threshold69.502269.5686116.771
manhattan_f10.39870.5070.56
manhattan_f1_threshold133.935382.6656128.6138
manhattan_precision0.27260.45060.5831
manhattan_recall0.7420.57960.5387
manhattan_ap0.36970.54790.6344
euclidean_accuracy0.84250.86260.8804
euclidean_accuracy_threshold3.85743.86016.4738
euclidean_f10.39990.50620.5614
euclidean_f1_threshold7.49164.61467.1657
euclidean_precision0.27160.44350.5753
euclidean_recall0.75790.58940.5481
euclidean_ap0.370.54870.6369
max_accuracy0.95270.93690.9622
max_accuracy_threshold69.502269.5686116.771
max_f10.85590.79950.885
max_f1_threshold133.935382.6656128.6138
max_precision0.84740.81650.8894
max_recall0.86460.78320.8805
max_ap0.92140.870.9475

Training Details

Training Dataset

Unnamed Dataset

  • Size: 155,651 training samples
  • Columns: text1 and text2
  • Approximate statistics based on the first 1000 samples:
    text1text2
    typestringstring
    detailsmin: 5 tokensmean: 231.14 tokensmax: 2048 tokensmin: 51 tokensmean: 618.32 tokensmax: 2048 tokens
  • Samples:
    text1text2
    === PERSON ===Dr. Amol CharegaonkarAlumnus of IIM Indore (FDP-Executive Education)Specialties: Data Analysis, FinancePune, Maharashtra, India4511=== ROLES ===>>> COMPANY ROLES <<<Company Role #1: Principal Consultant 2015-04 - 2021-06 N/A Maruma Consultancy maruma-consultancy Human Resources Services People Development Simplified 218Company Role #2: Assistant Manager – Credit (Policy - Unsecured loans) 2008-06 - 2009-01 Job Responsibilities: As a Team Member of the Credit Policy & Risk function1.Analysis of Delinquent cases in Personal loans and Business Loans2.Online analysis of cases booked under Personal and Business Loans 3.Auditing Pan India cases for credit underwriting w. r. t. Policy 4.Analyzing and publishing a Monthly Portfolio Review for Personal Loans from the Risk Perspective like Delinquency & Bounce Trend and Credit Appraisal Process 5.Incorporate the necessary changes required in the Lending Policy consid...=== PERSON ===Rajesh BajN/AHyderabad, Telangana, India491=== ROLES ===>>> COMPANY ROLES <<<Company Role #1: Sr.Officer Finance and Accounts 2010-10 - N/A N/A JK Agri Genetics Limited jk-agri-genetics-limited Biotechnology Research Harvesting Happiness Through Innovations 107873Company Role #2: Assistant Manager 2013-10 - N/A N/A JK Agri Genetics Limited jk-agri-genetics-limited Biotechnology Research Harvesting Happiness Through Innovations 107873Company Role #3: Officer- Finance and Accounts 2007-05 - 2007-07 Market Research in following Areas:-1. Bandra-Kurla complex2. Solitaire corporate Park Andheri3. Udyog Bhavan, Garegaon HSBC hsbc Financial Services N/A N/A>>> SCHOOL ROLES <<<No school roles listed.
    === PERSON ===Gabriela Menezes• Experiência em localização Brasil - TAXBRA, Nota Fiscal Eletrônica.• Conhecimento em SAP Activate e ASAP.• Experiência em suporte ao Cliente - AMSSão Paulo, São Paulo, Brazil454=== ROLES ===>>> COMPANY ROLES <<<Company Role #1: Consultora SAP SD S/4HANA 2023 - N/A Atuação em - AMS Analise e resolução de chamados dentro do SLA estabelecido.Atuação nas diversas solicitações de melhoria.Resolução de problemas relacionados a NF-eTestes unitários e integradosConfiguração de impostos e leis fiscais.Configuração de conta contábil.Resolução de erros relacionados a Pricing.Conhecimento e vivência no processo de Retorno Simbólico.Apoio as frentes MM e FIApoio a equipe de projetos em configurações, testes unitários e integrados. ITGCON Integração e Consultoria em Sistemas itgcon IT Services and IT Consulting Mais que uma implementadora, somos a ITGCON consultoria. 4116Company Role #2: Co...=== PERSON ===Rafael MenezesConsultor funcional SD com experiência em AMS, formado em Tecnologia da Informação.- Principais qualificações:- Conhecimento em Localização Brasil, Pricing, Nota Fiscal Eletrônica e GRC.- Conhecimento das principais tabelas do módulo SD.- Conhecimento na configuração de impostos na J1BTAX- Conhecimento no cadastro de dados mestres de clientes emateriais.São Paulo, Brazil117=== ROLES ===>>> COMPANY ROLES <<<Company Role #1: Consultor SAP SD Pleno 2023-05 - N/A N/A Capgemini capgemini IT Services and IT Consulting Get the future you want 6721701Company Role #2: Consultor Funcional SD JR/PL 2021-12 - 2023-01 - Suporte ao cliente – AMS;- Análise e resolução de chamados respeitando SLA;- Testes em cenários de vendas;- Configuração de impostos – J1BTAX;- Resolução de erros referentes a rejeição de NF-e- Resolução de erros referentes a CFOP- Resolução de erros referentes a contas contábeis- Criação de...
    === PERSON ===Anastasia Hanan, M.S., M.B.A (she/her)~10 years of user research in both startups and enterprise. I am a senior researcher passionate about leading research that grounds product vision in data and emotion, highlights the user voice along with immediate/long term project risks, and optimizes for radically collaborative tactical execution through ambiguous problem spaces. Specialties: Complex systems research, moving between qualitative research to quantitative insights at scale, crypto, Qualtrics, innovation and strategy, process optimization, rapid insight, cross functional collaboration, making AI make sense, visual storytelling, presentations that win contests, get millions in funding, and/or create actionable product roadmaps.United States1101=== ROLES ===>>> COMPANY ROLES <<<Company Role #1: UX Researcher 2022-11 - N/A N/A United States Digital Service united-states-digital-service Government Administration We're mission-driven professio...=== PERSON ===Stephanie KokotakisN/ALos Angeles, California, United States325=== ROLES ===>>> COMPANY ROLES <<<No company roles listed.>>> SCHOOL ROLES <<<No school roles listed.
  • Loss: MultipleNegativesSymmetricRankingLoss with these parameters:
    {
        "scale": 20.0,
        "similarity_fct": "cos_sim"
    }
    

Evaluation Dataset

Unnamed Dataset

  • Size: 47,595 evaluation samples
  • Columns: text1, text2, and label
  • Approximate statistics based on the first 1000 samples:
    text1text2label
    typestringstringint
    detailsmin: 5 tokensmean: 221.5 tokensmax: 2048 tokensmin: 50 tokensmean: 605.95 tokensmax: 2048 tokens0: ~82.40%1: ~17.60%
  • Samples:
    text1text2label
    Nonprofit and association management expert=== PERSON ===Marius StrydomMarius Strydom has spent 20 years producing innovative research on the SA financial services industry, with a specific focus on insurance. He was rated the top SA life assurance analyst twice and the top short-term insurance analyst 5 times while working at Investec and BJM. At Bank of America Merrill Lynch, Marius led the EEMEA Financial Sector Research team to a second place in the 2013 Institutional Investor survey. During 2013 and 2014, he worked on academic research, culminating in being published in the South African Actuarial Journal. Since 2015, as part of his work with MLAX Consulting, Marius analysed and produced bespoke research on numerous SA and UK financial companies. In 2020, Marius founded Austin Lawrence Gidon and together with global partner, Edison Investment Research, they are driving the sponsored research revolution in South Africa.City of Cape Town, Western Cape, South Africa1351=== ROLES ===>>> COMPANY ROLES <<<Company Role #...0
    Expert in Alienware product support=== PERSON ===Ryan TalbotWith over 25 years of experience in physical security, Identity management, and video surveillance, Ryan is recognized as a leader in the design and implementation of major technology solutions.Ryan has managed major security and IT system implementations for government, commercial, transport, data centres, gaming & casinos, retail, law enforcement and critical infrastructure environments. Ryan has worked closely with major technology brands in the design and development of hardware and software products throughout; Australia, Europe, North America and Asia.A leader in advanced video solutions and a former committee member of the AS/NZS 62676:5 standards, an experienced project manager and is PRINCE2 certified. He has a certificate IV in security risk management and qualifications in security electronics and sports performance.Ryan has also been involved in the development and design of many new technologies for IP cameras, customised ...0
    === PERSON ===Kevin WatsonSpecialties: Parametric cost estimation,WLC / LCC analysis, Logistics and supportability modelling. Economic LORA, Spares modelling,Tools / Applications: PRICE® TruePlanning®, EDCAS®, VMetric®United Kingdom389=== ROLES ===>>> COMPANY ROLES <<<Company Role #1: Senior Consultant 2013-09 - N/A N/A QinetiQ qinetiq_2 Defense and Space Manufacturing Create It. Test It. Use It. 122597Company Role #2: Support Modelling Team Lead 2004-06 - 2011-02 N/A General Dynamics UK Limited general-dynamics-uk-limited Defense and Space Manufacturing General Dynamics UK is one of the UK’s leading defence companies and an important supplier to the UK Ministry of Defence 29195Company Role #3: Estimation Specialist Senior Engineer 2011-02 - 2013-09 N/A General Dynamics UK Limited general-dynamics-uk-limited Defense and Space Manufacturing General Dynamics UK is one of the UK’s leading defence com...=== PERSON ===Kyle Biron, M.S.N/ARaleigh-Durham-Chapel Hill Area308=== ROLES ===>>> COMPANY ROLES <<<Company Role #1: Clinical Informatics Analyst Associate 2017-05 - 2018-05 • Data analyst at the Grow Baby Grow biostatistics lab at Kennesaw State University • Statistical programming and statistical modeling using the Pediatrix Medical Group, Inc. database of over 1.2 million infant records to solve medical problems• Received acknowledgement in a Neonatology publication comparing BMI with other proportionality measurements• En route to another publication comparing popular intrauterine preterm growth curves• Supervisory experience by managing and collaborating with other biostatisticians Kennesaw State University kennesaw-state-university N/A We are KSU Owls. And together, we're ascending. 167722Company Role #2: Health Data Analyst II 2022-06 - N/A N/A Nuna Inc. nuna-inc IT Services and IT Consulting Driven by Data, Guided by Com...0
  • Loss: MultipleNegativesSymmetricRankingLoss with these parameters:
    {
        "scale": 20.0,
        "similarity_fct": "cos_sim"
    }
    

Training Hyperparameters

Non-Default Hyperparameters

  • eval_strategy: steps
  • per_device_train_batch_size: 16
  • per_device_eval_batch_size: 32
  • learning_rate: 2e-05
  • num_train_epochs: 1
  • warmup_ratio: 0.1
  • bf16: True
  • load_best_model_at_end: True
  • batch_sampler: no_duplicates

All Hyperparameters

  • overwrite_output_dir: False
  • do_predict: False
  • eval_strategy: steps
  • prediction_loss_only: True
  • per_device_train_batch_size: 16
  • per_device_eval_batch_size: 32
  • per_gpu_train_batch_size: None
  • per_gpu_eval_batch_size: None
  • gradient_accumulation_steps: 1
  • eval_accumulation_steps: None
  • torch_empty_cache_steps: None
  • learning_rate: 2e-05
  • weight_decay: 0.0
  • 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: {}
  • warmup_ratio: 0.1
  • warmup_steps: 0
  • log_level: passive
  • log_level_replica: warning
  • log_on_each_node: True
  • logging_nan_inf_filter: True
  • save_safetensors: True
  • save_on_each_node: False
  • save_only_model: False
  • restore_callback_states_from_checkpoint: False
  • no_cuda: False
  • use_cpu: False
  • use_mps_device: False
  • seed: 42
  • data_seed: None
  • jit_mode_eval: False
  • use_ipex: False
  • bf16: True
  • fp16: False
  • fp16_opt_level: O1
  • half_precision_backend: auto
  • bf16_full_eval: False
  • fp16_full_eval: False
  • tf32: None
  • local_rank: 0
  • ddp_backend: None
  • tpu_num_cores: None
  • tpu_metrics_debug: False
  • debug: []
  • dataloader_drop_last: False
  • dataloader_num_workers: 0
  • dataloader_prefetch_factor: None
  • past_index: -1
  • disable_tqdm: False
  • remove_unused_columns: True
  • label_names: None
  • load_best_model_at_end: True
  • ignore_data_skip: False
  • fsdp: []
  • fsdp_min_num_params: 0
  • fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
  • fsdp_transformer_layer_cls_to_wrap: None
  • accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
  • deepspeed: None
  • label_smoothing_factor: 0.0
  • optim: adamw_torch
  • optim_args: None
  • adafactor: False
  • group_by_length: False
  • length_column_name: length
  • 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
  • use_legacy_prediction_loop: False
  • push_to_hub: False
  • resume_from_checkpoint: None
  • hub_model_id: None
  • hub_strategy: every_save
  • hub_private_repo: None
  • hub_always_push: False
  • gradient_checkpointing: False
  • gradient_checkpointing_kwargs: None
  • include_inputs_for_metrics: False
  • include_for_metrics: []
  • eval_do_concat_batches: True
  • fp16_backend: auto
  • push_to_hub_model_id: None
  • push_to_hub_organization: None
  • mp_parameters:
  • auto_find_batch_size: False
  • full_determinism: False
  • torchdynamo: None
  • ray_scope: last
  • ddp_timeout: 1800
  • torch_compile: False
  • torch_compile_backend: None
  • torch_compile_mode: None
  • dispatch_batches: None
  • split_batches: None
  • include_tokens_per_second: False
  • include_num_input_tokens_seen: False
  • neftune_noise_alpha: None
  • optim_target_modules: None
  • batch_eval_metrics: False
  • eval_on_start: False
  • use_liger_kernel: False
  • eval_use_gather_object: False
  • average_tokens_across_devices: False
  • prompts: None
  • batch_sampler: no_duplicates
  • multi_dataset_batch_sampler: proportional

Training Logs

EpochStepTraining LossValidation Lossmax_aprec_test_evaluator_max_apquery_test_evaluator_max_ap
00--0.45840.56980.7804
0.0051501.9282----
0.01031001.754----
0.01541501.4681----
0.02062001.1736----
0.02572501.0527----
0.03083000.9073----
0.03603500.9088----
0.04114000.8556----
0.04634500.77----
0.05145000.7768----
0.05655500.5559----
0.06176000.7102----
0.06686500.6498----
0.07197000.699----
0.07717500.6628----
0.08228000.7298----
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00--0.9020--
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0.997097000.3954----
00--0.92140.87000.9475
  • The bold row denotes the saved checkpoint.

Framework Versions

  • Python: 3.12.8
  • Sentence Transformers: 3.4.1
  • Transformers: 4.48.1
  • PyTorch: 2.6.0+cu124
  • Accelerate: 1.3.0
  • Datasets: 3.2.0
  • Tokenizers: 0.21.0

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",
}

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
JinaBertModel
Ревизия весов
c15fbc7

0 моделей