/ Модель

Характеристики

Контекст
8 192 токена
Параметры
149 млн
Лицензия
Apache 2.0
Архитектура
modernbert · dense
Базовая модель
gte-reranker-modernbert-base
Библиотека
sentence-transformers
Опубликована
13 авг 2025

langcache-reranker-v1-bce

Ранжирование текстаТекст → score7 профессий

Англоязычный Cross-Encoder Redis для оценки релевантности пар текстов и переранжирования результатов в LangCache.

1 229скачиваний за 30 дней0лайковвеса обновлены14 авг 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 «Ранжирование» — 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

Redis разработала эту англоязычную модель-переранжировщик для semantic caching в LangCache. Это Cross-Encoder на базе ModernBERT, дообученный из Alibaba-NLP/gte-reranker-modernbert-base на парах текстов LangCache с помощью библиотеки sentence-transformers. Модель получает пару текстов и выдаёт оценку, пригодную для классификации пары или ранжирования кандидатов по релевантности; в каталоге основная задача указана как text-ranking.

Модель содержит 149 млн параметров, поддерживает последовательности длиной до 8192 токенов и имеет один выходной показатель. Карточка сообщает метрики классификации на validation и test, но не задаёт универсальных порогов для интерпретации оценки. Модель рассчитана на английский язык и распространяется по лицензии Apache-2.0.

Сценарии применения

  • переранжирование документов по запросу в поисковой выдаче
  • оценка релевантности фрагментов для RAG
  • сравнение двух текстов для semantic caching
  • классификация пары текстов по сходству или соответствию

Ограничения

Модель предназначена для английского языка. Выход представляет собой оценку пары текстов; README не задаёт универсального порога для её интерпретации.

Для каких профессий подходит7

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

Redis fine-tuned CrossEncoder model for semantic caching on LangCache

This is a Cross Encoder model finetuned from Alibaba-NLP/gte-reranker-modernbert-base on the LangCache Sentence Pairs (all) dataset using the sentence-transformers library. It computes scores for pairs of texts, which can be used for sentence pair classification.

Model Details

Model Description

Model Sources

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 CrossEncoder

# Download from the 🤗 Hub
model = CrossEncoder("aditeyabaral-redis/langcache-reranker-v1-test2")
# Get scores for pairs of texts
pairs = [
    ['The newer Punts are still very much in existence today and race in the same fleets as the older boats .', 'The newer punts are still very much in existence today and run in the same fleets as the older boats .'],
    ['Turner Valley , was at the Turner Valley Bar N Ranch Airport , southwest of the Turner Valley Bar N Ranch , Alberta , Canada .', 'Turner Valley Bar N Ranch Airport , , was located at Turner Valley Bar N Ranch , southwest of Turner Valley , Alberta , Canada .'],
    ['After losing his second election , he resigned as opposition leader and was replaced by Geoff Pearsall .', 'Max Bingham resigned as opposition leader after losing his second election , and was replaced by Geoff Pearsall .'],
    ['She married Peter Haygarth on 29 May 1964 in Durban . Her second marriage , to Robin Osborne , took place in 1977 .', 'She married Robin Osborne on May 29 , 1964 in Durban , and her second marriage with Peter Haygarth took place in 1977 .'],
    ['In 2005 she moved to Norway , settled in Geilo and worked as a rafting guide , in 2006 she started mountain biking - races .', 'In 2005 , she moved to Geilo , settling in Norway and worked as a rafting guide . She started mountain bike races in 2006 .'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'The newer Punts are still very much in existence today and race in the same fleets as the older boats .',
    [
        'The newer punts are still very much in existence today and run in the same fleets as the older boats .',
        'Turner Valley Bar N Ranch Airport , , was located at Turner Valley Bar N Ranch , southwest of Turner Valley , Alberta , Canada .',
        'Max Bingham resigned as opposition leader after losing his second election , and was replaced by Geoff Pearsall .',
        'She married Robin Osborne on May 29 , 1964 in Durban , and her second marriage with Peter Haygarth took place in 1977 .',
        'In 2005 , she moved to Geilo , settling in Norway and worked as a rafting guide . She started mountain bike races in 2006 .',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

Evaluation

Metrics

Cross Encoder Classification

Metricvaltest
accuracy0.77180.8932
accuracy_threshold0.89270.7741
f10.69340.8793
f1_threshold0.87590.1352
precision0.67880.8524
recall0.70860.908
average_precision0.76760.9357

Training Details

Training Dataset

LangCache Sentence Pairs (all)

  • Dataset: LangCache Sentence Pairs (all)
  • Size: 62,021 training samples
  • Columns: sentence1, sentence2, and label
  • Approximate statistics based on the first 1000 samples:
    sentence1sentence2label
    typestringstringint
    detailsmin: 27 charactersmean: 112.72 charactersmax: 197 charactersmin: 27 charactersmean: 112.54 charactersmax: 198 characters0: ~50.30%1: ~49.70%
  • Samples:
    sentence1sentence2label
    The newer Punts are still very much in existence today and race in the same fleets as the older boats .The newer punts are still very much in existence today and run in the same fleets as the older boats .1
    Turner Valley , was at the Turner Valley Bar N Ranch Airport , southwest of the Turner Valley Bar N Ranch , Alberta , Canada .Turner Valley Bar N Ranch Airport , , was located at Turner Valley Bar N Ranch , southwest of Turner Valley , Alberta , Canada .0
    After losing his second election , he resigned as opposition leader and was replaced by Geoff Pearsall .Max Bingham resigned as opposition leader after losing his second election , and was replaced by Geoff Pearsall .1
  • Loss: BinaryCrossEntropyLoss with these parameters:
    {
        "activation_fn": "torch.nn.modules.linear.Identity",
        "pos_weight": null
    }
    

Evaluation Dataset

LangCache Sentence Pairs (all)

  • Dataset: LangCache Sentence Pairs (all)
  • Size: 62,021 evaluation samples
  • Columns: sentence1, sentence2, and label
  • Approximate statistics based on the first 1000 samples:
    sentence1sentence2label
    typestringstringint
    detailsmin: 27 charactersmean: 112.72 charactersmax: 197 charactersmin: 27 charactersmean: 112.54 charactersmax: 198 characters0: ~50.30%1: ~49.70%
  • Samples:
    sentence1sentence2label
    The newer Punts are still very much in existence today and race in the same fleets as the older boats .The newer punts are still very much in existence today and run in the same fleets as the older boats .1
    Turner Valley , was at the Turner Valley Bar N Ranch Airport , southwest of the Turner Valley Bar N Ranch , Alberta , Canada .Turner Valley Bar N Ranch Airport , , was located at Turner Valley Bar N Ranch , southwest of Turner Valley , Alberta , Canada .0
    After losing his second election , he resigned as opposition leader and was replaced by Geoff Pearsall .Max Bingham resigned as opposition leader after losing his second election , and was replaced by Geoff Pearsall .1
  • Loss: BinaryCrossEntropyLoss with these parameters:
    {
        "activation_fn": "torch.nn.modules.linear.Identity",
        "pos_weight": null
    }
    

Training Hyperparameters

Non-Default Hyperparameters

  • eval_strategy: steps
  • per_device_train_batch_size: 48
  • per_device_eval_batch_size: 48
  • learning_rate: 0.0002
  • num_train_epochs: 50
  • warmup_steps: 100
  • load_best_model_at_end: True
  • optim: adamw_torch
  • push_to_hub: True
  • hub_model_id: aditeyabaral-redis/langcache-reranker-v1-test2

All Hyperparameters

  • overwrite_output_dir: False
  • do_predict: False
  • eval_strategy: steps
  • prediction_loss_only: True
  • per_device_train_batch_size: 48
  • per_device_eval_batch_size: 48
  • 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: 0.0002
  • weight_decay: 0.0
  • adam_beta1: 0.9
  • adam_beta2: 0.999
  • adam_epsilon: 1e-08
  • max_grad_norm: 1.0
  • num_train_epochs: 50
  • max_steps: -1
  • lr_scheduler_type: linear
  • lr_scheduler_kwargs: {}
  • warmup_ratio: 0.0
  • warmup_steps: 100
  • 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: False
  • 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: True
  • 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: True
  • resume_from_checkpoint: None
  • hub_model_id: aditeyabaral-redis/langcache-reranker-v1-test2
  • hub_strategy: every_save
  • hub_private_repo: None
  • hub_always_push: False
  • hub_revision: None
  • 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
  • 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
  • liger_kernel_config: None
  • eval_use_gather_object: False
  • average_tokens_across_devices: True
  • prompts: None
  • batch_sampler: batch_sampler
  • multi_dataset_batch_sampler: proportional
  • router_mapping: {}
  • learning_rate_mapping: {}

Training Logs

EpochStepTraining LossValidation Lossval_average_precisiontest_average_precision
-1-1--0.76760.6907
0.181810000.30580.3991-0.8742
0.363620000.24170.3729-0.8962
0.545530000.22870.3356-0.9039
0.727340000.21830.3729-0.9109
0.909150000.2120.3404-0.9215
1.090960000.19990.3171-0.9240
1.272770000.19440.3131-0.9263
1.454580000.19330.3116-0.9264
1.636490000.1930.3211-0.9243
1.8182100000.18790.2984-0.9291
2.0110000.18690.2983-0.9305
2.1818120000.17260.3009-0.9252
2.3636130000.17250.3058-0.9277
2.5455140000.17430.2991-0.9239
2.7273150000.17240.2875-0.9317
2.9091160000.1730.2872-0.9369
3.0909170000.16520.2840-0.9364
3.2727180000.15970.2825-0.9360
3.4545190000.16130.2988-0.9375
3.6364200000.15870.2868-0.9311
3.8182210000.160.2863-0.9327
4.0220000.15990.2853-0.9351
4.1818230000.1460.2876-0.9376
4.3636240000.14810.2813-0.9424
4.5455250000.14770.2831-0.9403
4.7273260000.1480.2769-0.9405
4.9091270000.14850.2758-0.9418
5.0909280000.1420.2771-0.9420
5.2727290000.13590.2878-0.9392
5.4545300000.13680.2777-0.9402
5.6364310000.13970.2797-0.9370
5.8182320000.13950.2771-0.9433
6.0330000.13930.2883-0.9421
6.1818340000.12490.2822-0.9410
6.3636350000.1270.2739-0.9431
6.5455360000.130.2797-0.9409
6.7273370000.13040.2796-0.9414
6.9091380000.13060.2750-0.9455
7.0909390000.12360.2820-0.9417
7.2727400000.11850.2898-0.9434
7.4545410000.1190.2863-0.9393
7.6364420000.12060.2761-0.9412
7.8182430000.12160.2702-0.9433
8.0440000.12210.2771-0.9447
8.1818450000.10830.2836-0.9418
8.3636460000.11240.2889-0.9430
8.5455470000.11060.2755-0.9406
8.7273480000.11340.2853-0.9439
8.9091490000.1140.2845-0.9446
9.0909500000.10950.2852-0.9421
9.2727510000.10280.2880-0.9452
9.4545520000.1030.2796-0.9434
9.6364530000.10480.2794-0.9439
9.8182540000.1050.2838-0.9454
10.0550000.10860.2866-0.9436
10.1818560000.09380.2809-0.9436
10.3636570000.09690.3018-0.9421
10.5455580000.09750.2823-0.9436
10.7273590000.09710.2943-0.9403
10.9091600000.10020.2915-0.9425
11.0909610000.09390.2980-0.9433
11.2727620000.08690.2932-0.9460
11.4545630000.08880.2885-0.9442
11.6364640000.09150.2844-0.9431
11.8182650000.0920.3085-0.9444
12.0660000.09330.2833-0.9456
12.1818670000.07960.3031-0.9457
12.3636680000.0820.2934-0.9445
12.5455690000.08650.3030-0.9426
12.7273700000.08580.3028-0.9448
12.9091710000.08770.2930-0.9456
13.0909720000.07910.3105-0.9460
13.2727730000.07540.3189-0.9468
13.4545740000.07770.2985-0.9454
13.6364750000.07960.3170-0.9445
13.8182760000.07990.2851-0.9468
14.0770000.08160.3022-0.9468
14.1818780000.07060.3169-0.9449
14.3636790000.07270.3114-0.9464
14.5455800000.0730.3059-0.9464
14.7273810000.07450.3108-0.9427
14.9091820000.07410.3149-0.9447
15.0909830000.07040.3213-0.9441
15.2727840000.06490.3245-0.9450
15.4545850000.06840.3180-0.9452
15.6364860000.06940.3320-0.9425
15.8182870000.06810.3138-0.9449
16.0880000.06910.3158-0.9460
16.1818890000.06080.3317-0.9457
16.3636900000.06090.3253-0.9438
16.5455910000.06210.3298-0.9458
16.7273920000.06480.3246-0.9428
16.9091930000.06570.3229-0.9432
17.0909940000.05960.3327-0.9446
17.2727950000.05790.3186-0.9419
17.4545960000.05810.3272-0.9459
17.6364970000.05920.3344-0.9423
17.8182980000.060.3446-0.9409
18.0990000.05980.3280-0.9452
18.18181000000.05150.3577-0.9428
18.36361010000.05390.3418-0.9459
18.54551020000.05440.3365-0.9426
18.72731030000.0540.3294-0.9451
18.90911040000.05680.3420-0.9391
19.09091050000.05160.3650-0.9443
19.27271060000.04820.3546-0.9458
19.45451070000.04970.3338-0.9455
19.63641080000.04950.3524-0.9426
19.81821090000.0510.3556-0.9436
20.01100000.05120.3323-0.9412
20.18181110000.04330.3572-0.9423
20.36361120000.0450.3678-0.9431
20.54551130000.04660.3450-0.9437
20.72731140000.0470.3616-0.9406
20.90911150000.0480.3538-0.9453
21.09091160000.0440.3638-0.9454
21.27271170000.04170.3767-0.9448
21.45451180000.04280.3773-0.9455
21.63641190000.04210.3613-0.9453
21.81821200000.04420.3795-0.9426
22.01210000.04530.3758-0.9442
22.18181220000.03790.3819-0.9436
22.36361230000.03970.3665-0.9396
22.54551240000.0390.3871-0.9436
22.72731250000.03980.3752-0.9443
22.90911260000.04080.3755-0.9424
23.09091270000.03880.3698-0.9464
23.27271280000.03480.3828-0.9400
23.45451290000.03530.3814-0.9426
23.63641300000.03750.3907-0.9423
23.81821310000.03660.4085-0.9412
24.01320000.03880.3734-0.9350
24.18181330000.03210.4105-0.9442
24.36361340000.03290.4038-0.9433
24.54551350000.03350.4123-0.9431
24.72731360000.03510.3945-0.9431
24.90911370000.03470.3995-0.9446
25.09091380000.03220.4154-0.9442
25.27271390000.03120.3900-0.9432
25.45451400000.03010.4083-0.9417
25.63641410000.03180.4146-0.9445
25.81821420000.03210.4198-0.9437
26.01430000.0320.4168-0.9405
26.18181440000.02660.4293-0.9396
26.36361450000.02770.4234-0.9421
26.54551460000.02880.4309-0.9438
26.72731470000.02920.4215-0.9398
26.90911480000.02940.4020-0.9412
27.09091490000.02720.4342-0.9438
27.27271500000.0250.4434-0.9402
27.45451510000.0270.4178-0.9435
27.63641520000.02570.4396-0.9428
27.81821530000.0280.4099-0.9405
28.01540000.02750.4185-0.9443
28.18181550000.02360.4375-0.9456
28.36361560000.02370.4232-0.9409
28.54551570000.02370.4642-0.9430
28.72731580000.02490.4374-0.9447
28.90911590000.02580.4329-0.9451
29.09091600000.02190.4867-0.9454
29.27271610000.02160.4737-0.9435
29.45451620000.02180.4577-0.9449
29.63641630000.02230.4589-0.9424
29.81821640000.02230.4410-0.9452
30.01650000.02360.4477-0.9432
30.18181660000.02030.4798-0.9459
30.36361670000.020.4600-0.9453
30.54551680000.02060.4492-0.9419
30.72731690000.02030.4839-0.9435
30.90911700000.02120.4731-0.9438
31.09091710000.01960.4621-0.9434
31.27271720000.01780.4986-0.9441
31.45451730000.01770.4871-0.9431
31.63641740000.02010.4520-0.9445
31.81821750000.01910.4571-0.9429
32.01760000.02010.4871-0.9453
32.18181770000.01560.5061-0.9440
32.36361780000.01740.4704-0.9444
32.54551790000.01750.4900-0.9430
32.72731800000.01750.4861-0.9386
32.90911810000.01780.5005-0.9436
33.09091820000.01630.4934-0.9441
33.27271830000.01490.5065-0.9426
33.45451840000.01570.4973-0.9444
33.63641850000.01640.4993-0.9410
33.81821860000.01640.4904-0.9411
34.01870000.01670.5096-0.9432
34.18181880000.01360.4960-0.9399
34.36361890000.01420.5188-0.9447
34.54551900000.01440.5139-0.9434
34.72731910000.01490.4919-0.9416
34.90911920000.01490.4775-0.9453
35.09091930000.01270.5546-0.9437
35.27271940000.01240.5440-0.9444
35.45451950000.01260.5571-0.9435
35.63641960000.01310.5127-0.9428
35.81821970000.01340.5167-0.9415
36.01980000.01340.4939-0.9394
36.18181990000.0110.5279-0.9421
36.36362000000.01150.5336-0.9434
36.54552010000.01130.5626-0.9437
36.72732020000.0120.5316-0.9421
36.90912030000.01210.5222-0.9428
37.09092040000.01070.5618-0.9450
37.27272050000.01070.5508-0.9430
37.45452060000.01060.5414-0.9432
37.63642070000.01060.5522-0.9432
37.81822080000.01110.5524-0.9434
38.02090000.0120.5176-0.9434
38.18182100000.00870.5742-0.9432
38.36362110000.00920.5686-0.9441
38.54552120000.00990.5699-0.9437
38.72732130000.00940.5733-0.9435
38.90912140000.00970.5516-0.9431
39.09092150000.0090.5923-0.9425
39.27272160000.00780.5925-0.9427
39.45452170000.00860.5703-0.9433
39.63642180000.00870.5921-0.9444
39.81822190000.00850.5859-0.9436
40.02200000.00910.5577-0.9430
40.18182210000.00770.5844-0.9425
40.36362220000.00770.5691-0.9396
40.54552230000.0080.5794-0.9398
40.72732240000.00730.6036-0.9370
40.90912250000.00830.5754-0.9419
41.09092260000.00780.6141-0.9416
41.27272270000.00690.6332-0.9407
41.45452280000.0070.6220-0.9417
41.63642290000.00750.6110-0.9413
41.81822300000.0070.6248-0.9423
42.02310000.00720.5950-0.9396
42.18182320000.00590.6428-0.9363
42.36362330000.00650.6298-0.9410
42.54552340000.00650.6166-0.9424
42.72732350000.00660.5990-0.9424
42.90912360000.00650.6297-0.9405
43.09092370000.00570.6483-0.9376
43.27272380000.00580.6077-0.9407
43.45452390000.00560.6420-0.9376
43.63642400000.00590.6574-0.9400
43.81822410000.00510.6819-0.9372
44.02420000.00550.6567-0.9348
44.18182430000.00510.6697-0.9297
44.36362440000.0050.6459-0.9377
44.54552450000.00470.6693-0.9353
44.72732460000.00540.6589-0.9274
44.90912470000.00510.6886-0.9318
45.09092480000.00470.6886-0.9388
45.27272490000.00450.6959-0.9320
45.45452500000.00450.6827-0.9347
45.63642510000.00420.6706-0.9314
45.81822520000.00430.6858-0.9350
46.02530000.00450.6926-0.9310
46.18182540000.00410.7091-0.9341
46.36362550000.00380.711-0.932
46.54552560000.00390.6991-0.9277
46.72732570000.00380.7068-0.9213
46.90912580000.00430.7026-0.9295
47.09092590000.00410.7031-0.9390
47.27272600000.00370.6905-0.9388
47.45452610000.00350.7044-0.9386
47.63642620000.00380.7005-0.9378
47.81822630000.00390.7064-0.9398
48.02640000.00340.7112-0.9378
48.18182650000.00340.7092-0.9377
48.36362660000.00290.7286-0.9376
48.54552670000.00340.7270-0.9386
48.72732680000.00350.7101-0.9382
48.90912690000.00350.7127-0.9374
49.09092700000.00310.7228-0.9357
49.27272710000.00290.7289-0.9356
49.45452720000.0030.7309-0.9356
49.63642730000.0030.7308-0.9358
49.81822740000.00330.7293-0.9355
50.02750000.0030.7302-0.9357
  • The bold row denotes the saved checkpoint.

Framework Versions

  • Python: 3.12.3
  • Sentence Transformers: 5.1.0
  • Transformers: 4.55.0
  • PyTorch: 2.8.0+cu128
  • Accelerate: 1.10.0
  • Datasets: 4.0.0
  • Tokenizers: 0.21.4

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
ModernBertForSequenceClassification
Ревизия весов
9ad8897

0 моделей