/ Модель

Характеристики

Контекст
32 768 токенов
Параметры
508 млн
Архитектура
qwen3 · dense
Библиотека
sentence-transformers
Опубликована
29 мая 2026

TrialSpace-0526

ЭмбеддингиТекст → embedding10 профессий

Модель на базе Qwen3 преобразует предложения и абзацы в векторы для семантического поиска и сравнения текстов.

1 615скачиваний за 30 дней0лайковвеса обновлены7 июл 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

ksg-dfci/TrialSpace-0526 — модель Sentence Transformer на базе архитектуры Qwen3, обученная на наборах mnri_dataset и contrastive_dataset. Она преобразует текстовые предложения и абзацы в нормализованные плотные векторы размерностью 1024, пригодные для поиска и сравнения по косинусному сходству. В метаданных указано 507,6 млн параметров и контекст до 32 768 токенов; карточка модели заявляет максимальную длину последовательности 2500 токенов.

Модель предназначена для извлечения признаков и семантического поиска по тексту. Её можно применять для векторизации документов и запросов, поиска релевантных фрагментов и оценки сходства предложений. Репозиторий содержит веса в формате safetensors и использует библиотеку Sentence Transformers. Лицензия в предоставленных данных не указана.

Сценарии применения

  • семантический поиск по документам
  • поиск релевантных фрагментов по текстовому запросу
  • сравнение сходства двух предложений
  • создание эмбеддингов для базы знаний
  • извлечение признаков из текста

Ограничения

Карточка модели не сообщает поддерживаемые естественные языки или лицензию. Заявленная в README максимальная длина последовательности — 2500 токенов, хотя метаданные указывают контекст до 32768 токенов.

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

This is a sentence-transformers model trained on the mnri_dataset and contrastive_dataset datasets. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for retrieval.

Model Details

Model Description

  • Model Type: Sentence Transformer
  • Maximum Sequence Length: 2500 tokens
  • Output Dimensionality: 1024 dimensions
  • Similarity Function: Cosine Similarity
  • Supported Modality: Text
  • Training Datasets:
    • mnri_dataset
    • contrastive_dataset

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': 'Qwen3Model'})
  (1): Pooling({'embedding_dimension': 1024, 'pooling_mode': 'lasttoken', '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
queries = [
    'Instruct: Given a cancer patient summary, retrieve clinical trial options that are reasonable for that patient; or, given a clinical trial option, retrieve cancer patients who are reasonable candidates for that trial. Age: 12\nSex: Male\nCancer type: Mediastinal mixed germ cell tumor\nHistology: Embryonal carcinoma, choriocarcinoma, and yolk sac tumor\nCurrent extent: Metastatic (mediastinal mass, mediastinal lymphadenopathy, and brain metastases in the right frontal and left parietal lobes). Most recent tumor markers (2018-03-30): AFP 310 ng/mL, Beta-hCG 850 mIU/mL, LDH 410 U/L; increasing trend.\nBiomarkers: SALL4 positive, AFP positive (yolk sac), Beta-hCG positive (choriocarcinoma), CD30 positive (embryonal carcinoma), Glypican-3 positive (yolk sac), Cytokeratin (AE1/AE3) positive. NGS: KRAS p.G12D pathogenic mutation, PTEN homozygous deletion, TP53 p.P72R (variant of uncertain significance), isochromosome 12p, gain of 20q.\nTreatment history:\n# 2015: bleomycin, etoposide, cisplatin; achieved partial response\n# 2016-02-09: Partial resection of anterior mediastinal mass\n# 2016-12-05-present: ifosfamide, cisplatin; achieved partial response, subsequently progressed',
]
documents = [
    'Instruct: Given a cancer patient summary, retrieve clinical trial options that are reasonable for that patient; or, given a clinical trial option, retrieve cancer patients who are reasonable candidates for that trial. Age range allowed: 1 to 21 years. Sex allowed: Both. Cancer type allowed: mediastinal mixed germ cell tumor. Histology allowed: pediatric malignant solid tumor. Cancer burden allowed: high-risk (expected 5-year Event-Free Survival < 60%) and < 30% chance at long-term cure and disease status of Stable Disease or better. Prior treatment required: prior primary therapy. Prior treatment excluded: NA. Biomarkers required: satisfactory TIL cellular product availability (assessed during screening). Biomarkers excluded: NA.',
    'Instruct: Given a cancer patient summary, retrieve clinical trial options that are reasonable for that patient; or, given a clinical trial option, retrieve cancer patients who are reasonable candidates for that trial. Age range allowed: 0 to 50 years. Sex allowed: Male. Cancer type allowed: Any cancer. Histology allowed: Hematologic. Cancer burden allowed: Any. Prior treatment required: NA. Prior treatment excluded: NA. Biomarkers required: NA. Biomarkers excluded: NA.',
    'Instruct: Given a cancer patient summary, retrieve clinical trial options that are reasonable for that patient; or, given a clinical trial option, retrieve cancer patients who are reasonable candidates for that trial. Age range allowed: 0 to 29 years. Sex allowed: both. Cancer type allowed: mixed phenotype acute leukemia with T cell dominant phenotype. Histology allowed: mixed phenotype acute leukemia with T cell dominant phenotype. Cancer burden allowed: Second or greater relapse or first relapse post-allogeneic hematopoietic stem cell transplant or primary refractory disease or relapsed and refractory disease. Prior treatment required: NA. Prior treatment excluded: CD7 targeted therapy. Biomarkers required: CD7 expression on leukemic blasts (at least 90% of blasts positive by flow cytometry or immunohistochemistry) assessed during screening. Biomarkers excluded: NA.',
]
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([[0.4132, 0.2552, 0.1695]])

Training Details

Training Datasets

mnri_dataset

  • Dataset: mnri_dataset
  • Size: 1,174,581 training samples
  • Columns: patient_summary_trunc and this_space_trunc
  • Approximate statistics based on the first 1000 samples:
    patient_summary_truncthis_space_trunc
    typestringstring
    detailsmin: 146 tokensmean: 323.8 tokensmax: 554 tokensmin: 101 tokensmean: 155.35 tokensmax: 283 tokens
  • Samples:
    patient_summary_truncthis_space_trunc
    Instruct: Given a cancer patient summary, retrieve clinical trial options that are reasonable for that patient; or, given a clinical trial option, retrieve cancer patients who are reasonable candidates for that trial. Age: 6Sex: MaleCancer type: Kaposiform hemangioendothelioma of the right lower extremityHistology: Spindle cell vascular neoplasm (CD31 positive, CD34 positive, ERG positive, HHV-8 negative)Current extent: Localized; status post wide local excision on 2023-02-06 with negative marginsBiomarkers: PIK3CA p.E542K mutant; PTEN, AKT1, KRAS, and TP53 wild-typeTreatment history:# 2020-10-28 to 2021-04-04: vincristine; achieved partial response (tumor decreased from 7 cm to 5.2 cm)# 2021-11-29 to 2022-10-18: paclitaxel; achieved partial response (tumor decreased from 9 cm to 6.4 cm)# 2023-02-06: wide local excision of right calf mass; negative marginsInstruct: Given a cancer patient summary, retrieve clinical trial options that are reasonable for that patient; or, given a clinical trial option, retrieve cancer patients who are reasonable candidates for that trial. Age range allowed: 0 to 14 years. Sex allowed: Male or Female. Cancer type allowed: Kaposiform hemangioendothelioma. Histology allowed: NA. Cancer burden allowed: NA. Prior treatment required: NA. Prior treatment excluded: sirolimus or other mTOR inhibitors. Biomarkers required: NA. Biomarkers excluded: NA.
    Instruct: Given a cancer patient summary, retrieve clinical trial options that are reasonable for that patient; or, given a clinical trial option, retrieve cancer patients who are reasonable candidates for that trial. Age: 14Sex: MaleCancer type: Kaposiform Hemangioendothelioma of the right lateral neckHistology: Spindle cell proliferation with kaposiform architectureCurrent extent: Stage T1N0M0; stable 3.1 cm mass in the right lateral neck as of 2021-02-08; platelets 228,000/uL as of 2020-12-12 (stable, no Kasabach-Merritt phenomenon)Biomarkers: CD31 positive, CD34 positive, ERG positive, HHV-8 negative, GLUT1 negative/patchy, Ki-67 less than 5%, PIK3CA p.H1047R mutation, PTEN p.S167R mutation, STK11 wild-type, TSC1 wild-typeTreatment history:# 2018-11-21-2021-02-08: observation; stable diseaseInstruct: Given a cancer patient summary, retrieve clinical trial options that are reasonable for that patient; or, given a clinical trial option, retrieve cancer patients who are reasonable candidates for that trial. Age range allowed: 0 to 14 years. Sex allowed: Male or Female. Cancer type allowed: Kaposiform hemangioendothelioma. Histology allowed: NA. Cancer burden allowed: NA. Prior treatment required: NA. Prior treatment excluded: sirolimus or other mTOR inhibitors. Biomarkers required: NA. Biomarkers excluded: NA.
    Instruct: Given a cancer patient summary, retrieve clinical trial options that are reasonable for that patient; or, given a clinical trial option, retrieve cancer patients who are reasonable candidates for that trial. Age: 17
    Sex: Male
    Cancer type: T-cell lymphoblastic lymphoma
    Histology: T-cell lymphoblastic lymphoma
    Current extent: Relapsed and refractory. As of 2019-12-06, anterior mediastinal mass measuring 5.5 cm with associated superior vena cava narrowing and bilateral hilar lymphadenopathy. As of 2020-04-21, bone marrow involvement with 22% lymphoblasts. LDH is elevated (normalized during remission, rose to 412 U/L at relapse in 2019-03-26, and remains elevated as of 2020-08-21).
    Biomarkers: TdT positive, CD3 positive, CD7 positive, CD2 positive, CD5 positive, CD19 negative, CD20 negative, CD45 positive, Ki-67 >95%; NOTCH1 p.V1744G, CDKN2A homozygous deletion, PTEN p.R130Q, 12q gain
    Treatment history:

    2016-07 to 2016-12: vincristine, prednisone, L-asparaginase; best response ... | Instruct: Given a cancer patient summary, retrieve clinical trial options that are reasonable for that patient; or, given a clinical trial option, retrieve cancer patients who are reasonable candidates for that trial. Age range allowed: 0 to 29 years. Sex allowed: both. Cancer type allowed: T-cell acute lymphoblastic leukemia. Histology allowed: T-cell acute lymphoblastic leukemia. Cancer burden allowed: Second or greater relapse or first relapse post-allogeneic hematopoietic stem cell transplant or primary refractory disease or relapsed and refractory disease. Prior treatment required: NA. Prior treatment excluded: CD7 targeted therapy. Biomarkers required: CD7 expression on leukemic blasts (at least 90% of blasts positive by flow cytometry or immunohistochemistry) assessed during screening. Biomarkers excluded: NA. |

  • 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
    }
    

contrastive_dataset

  • Dataset: contrastive_dataset
  • Size: 1,824,636 training samples
  • Columns: patient_summary_trunc, this_space_trunc, and label
  • Approximate statistics based on the first 1000 samples:
    patient_summary_truncthis_space_trunclabel
    typestringstringfloat
    detailsmin: 76 tokensmean: 294.03 tokensmax: 525 tokensmin: 101 tokensmean: 132.43 tokensmax: 179 tokensmin: -1.0mean: -0.7max: 1.0
  • Samples:
    patient_summary_truncthis_space_trunclabel
    Instruct: Given a cancer patient summary, retrieve clinical trial options that are reasonable for that patient; or, given a clinical trial option, retrieve cancer patients who are reasonable candidates for that trial. Age: 6Sex: MaleCancer type: Kaposiform hemangioendothelioma of the right lower extremityHistology: Spindle cell vascular neoplasm (CD31 positive, CD34 positive, ERG positive, HHV-8 negative)Current extent: Localized; status post wide local excision on 2023-02-06 with negative marginsBiomarkers: PIK3CA p.E542K mutant; PTEN, AKT1, KRAS, and TP53 wild-typeTreatment history:# 2020-10-28 to 2021-04-04: vincristine; achieved partial response (tumor decreased from 7 cm to 5.2 cm)# 2021-11-29 to 2022-10-18: paclitaxel; achieved partial response (tumor decreased from 9 cm to 6.4 cm)# 2023-02-06: wide local excision of right calf mass; negative marginsInstruct: Given a cancer patient summary, retrieve clinical trial options that are reasonable for that patient; or, given a clinical trial option, retrieve cancer patients who are reasonable candidates for that trial. Age range allowed: 0 to 14 years. Sex allowed: Male or Female. Cancer type allowed: Kaposiform hemangioendothelioma. Histology allowed: NA. Cancer burden allowed: NA. Prior treatment required: NA. Prior treatment excluded: sirolimus or other mTOR inhibitors. Biomarkers required: NA. Biomarkers excluded: NA.-0.19999999999999996
    Instruct: Given a cancer patient summary, retrieve clinical trial options that are reasonable for that patient; or, given a clinical trial option, retrieve cancer patients who are reasonable candidates for that trial. Age: 14Sex: MaleCancer type: Kaposiform Hemangioendothelioma of the right lateral neckHistology: Spindle cell proliferation with kaposiform architectureCurrent extent: Stage T1N0M0; stable 3.1 cm mass in the right lateral neck as of 2021-02-08; platelets 228,000/uL as of 2020-12-12 (stable, no Kasabach-Merritt phenomenon)Biomarkers: CD31 positive, CD34 positive, ERG positive, HHV-8 negative, GLUT1 negative/patchy, Ki-67 less than 5%, PIK3CA p.H1047R mutation, PTEN p.S167R mutation, STK11 wild-type, TSC1 wild-typeTreatment history:# 2018-11-21-2021-02-08: observation; stable diseaseInstruct: Given a cancer patient summary, retrieve clinical trial options that are reasonable for that patient; or, given a clinical trial option, retrieve cancer patients who are reasonable candidates for that trial. Age range allowed: 0 to 14 years. Sex allowed: Male or Female. Cancer type allowed: Kaposiform hemangioendothelioma. Histology allowed: NA. Cancer burden allowed: NA. Prior treatment required: NA. Prior treatment excluded: sirolimus or other mTOR inhibitors. Biomarkers required: NA. Biomarkers excluded: NA.-0.19999999999999996
    Instruct: Given a cancer patient summary, retrieve clinical trial options that are reasonable for that patient; or, given a clinical trial option, retrieve cancer patients who are reasonable candidates for that trial. Age: 46Sex: FemaleCancer type: Retroperitoneal cancerHistology: Epithelioid HemangioendotheliomaCurrent extent: Metastatic (retroperitoneal mass, liver metastases, and ascites); progressive disease as of 2018-04-09Biomarkers: CD31 positive, ERG positive, CD34 patchy positive, Pan-Cytokeratin negative, S100 negative, HMB-45 negative, SMA negative, Ki-67 15%, WWTR1-YAP1 fusion, PIK3CA p.E545K mutation, RB1 hemizygous loss, STK11 p.T123M mutationTreatment history:# 2014-11-12: retroperitoneal debulking surgery; achieved no evidence of disease as of 2015-07-11# 2016-04-10-2017-05-16: paclitaxel; best response partial response (30 percent reduction in tumor diameter)# 2017-05-16-2018-04-09: capecitabine; best response stable diseaseInstruct: Given a cancer patient summary, retrieve clinical trial options that are reasonable for that patient; or, given a clinical trial option, retrieve cancer patients who are reasonable candidates for that trial. Age range allowed: 0 to 14 years. Sex allowed: Male or Female. Cancer type allowed: Kaposiform hemangioendothelioma. Histology allowed: NA. Cancer burden allowed: NA. Prior treatment required: NA. Prior treatment excluded: sirolimus or other mTOR inhibitors. Biomarkers required: NA. Biomarkers excluded: NA.-1.0
  • Loss: CoSENTLoss with these parameters:
    {
        "scale": 20.0,
        "similarity_fct": "pairwise_cos_sim"
    }
    

Training Hyperparameters

Non-Default Hyperparameters

  • per_device_train_batch_size: 10
  • learning_rate: 2e-05
  • warmup_steps: 0.01
  • bf16: True

All Hyperparameters

  • per_device_train_batch_size: 10
  • num_train_epochs: 3
  • max_steps: -1
  • learning_rate: 2e-05
  • lr_scheduler_type: linear
  • lr_scheduler_kwargs: None
  • warmup_steps: 0.01
  • 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: 8
  • 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: True
  • dataloader_num_workers: 0
  • 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: []
  • fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
  • 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 Loss
0.00271002.1097
0.00532002.1026
0.00803002.0170
0.01074001.9648
0.01335002.0097
0.01606002.0227
0.01877002.0225
0.02138002.0278
0.02409001.9787
0.026710002.0899
0.029311002.1262
0.032012002.0419
0.034713002.1615
0.037314002.1889
0.040015002.1789
0.042716002.1664
0.045317002.1390
0.048018002.1228
0.050719002.1279
0.053320002.1547
0.056021002.1559
0.058722002.0800
0.061323002.0955
0.064024002.1033
0.066725002.1287
0.069426002.1078
0.072027002.1395
0.074728002.0470
0.077429002.1514
0.080030002.0960
0.082731002.1023
0.085432002.0591
0.088033002.0531
0.090734002.1157
0.093435002.0259
0.096036002.0914
0.098737002.0944
0.101438002.0515
0.104039002.0343
0.106740002.1189
0.109441002.1386
0.112042002.0944
0.114743002.1079
0.117444002.1063
0.120045002.0637
0.122746002.0631
0.125447002.0741
0.128048002.0463
0.130749002.0527
0.133450002.0972
0.136051002.0442
0.138752002.0219
0.141453002.0472
0.144054002.0216
0.146755002.0563
0.149456002.0628
0.152057002.0743
0.154758002.0345
0.157459002.0047
0.160060002.0353
0.162761002.0405
0.165462002.0376
0.168063002.0913
0.170764002.0583
0.173465002.0067
0.176066002.0845
0.178767002.0453
0.181468002.0680
0.184069002.1089
0.186770002.0148
0.189471002.0982
0.192172001.9828
0.194773001.9660
0.197474002.0258
0.200175002.0476
0.202776001.9685
0.205477002.0140
0.208178002.0370
0.210779001.9959
0.213480002.0764
0.216181002.0524
0.218782002.0152
0.221483002.0588
0.224184001.9843
0.226785002.0035
0.229486002.0075
0.232187002.0117
0.234788001.9865
0.237489002.0389
0.240190001.9825
0.242791002.0212
0.245492001.9980
0.248193002.0304
0.250794001.9323
0.253495001.9595
0.256196002.0353
0.258797002.0084
0.261498002.0122
0.264199001.9643
0.2667100001.9825
0.2694101002.0026
0.2721102001.9970
0.2747103002.0188
0.2774104001.9387
0.2801105001.9977
0.2827106002.0632
0.2854107002.0115
0.2881108001.965
0.2907109001.9850
0.2934110001.9387
0.2961111001.9670
0.2987112002.0217
0.3014113002.0043
0.3041114001.9939
0.3067115001.9480
0.3094116002.0174
0.3121117002.0616
0.3148118001.9169
0.3174119001.9747
0.3201120001.9779
0.3228121001.9851
0.3254122002.0473
0.3281123001.9748
0.3308124001.9286
0.3334125001.9859
0.3361126001.9763
0.3388127001.9661
0.3414128001.9658
0.3441129002.0157
0.3468130001.9525
0.3494131001.9356
0.3521132001.9726
0.3548133001.9877
0.3574134001.9931
0.3601135002.0022
0.3628136001.9680
0.3654137002.0357
0.3681138001.9998
0.3708139001.9797
0.3734140002.0070
0.3761141001.9707
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Training Time

  • Training: 7.8 hours

Framework Versions

  • Python: 3.13.13
  • Sentence Transformers: 5.4.1
  • Transformers: 5.8.0
  • PyTorch: 2.11.0+cu130
  • Accelerate: 1.13.0
  • Datasets: 4.8.5
  • 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{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},
}

CoSENTLoss

@article{10531646,
    author={Huang, Xiang and Peng, Hao and Zou, Dongcheng and Liu, Zhiwei and Li, Jianxin and Liu, Kay and Wu, Jia and Su, Jianlin and Yu, Philip S.},
    journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing},
    title={CoSENT: Consistent Sentence Embedding via Similarity Ranking},
    year={2024},
    doi={10.1109/TASLP.2024.3402087}
}

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
279dc90

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