/ 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
21 окт 2025

langcache-reranker-v2-bce

Ранжирование текстаТекст → score7 профессий

Англоязычный Cross-Encoder Redis оценивает соответствие пары текстов для семантического кэширования, классификации и переранжирования результатов поиска.

60скачиваний за 30 дней0лайковвеса обновлены24 окт 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 модель Cross-Encoder для оценки пар текстов в сценариях семантического кэширования LangCache. Она дообучена на Alibaba-NLP/gte-reranker-modernbert-base с использованием библиотеки sentence-transformers и датасета LangCache Sentence Pairs. Модель принимает пару текстов и возвращает оценку их соответствия, которую можно использовать для классификации пар и переранжирования результатов поиска.

Модель построена на ModernBERT, имеет 149 млн параметров и максимальную длину последовательности 8192 токена. Она рассчитана на английский язык. В README приведены результаты оценки классификации и переранжирования на тестовых наборах, однако результаты на наборах NanoQuoraRetrieval, NanoMSMARCO и NanoNQ ниже показателей базовой модели. Модель распространяется по лицензии Apache-2.0.

Сценарии применения

  • оценка сходства запроса и сохранённого ответа для семантического кэша
  • переранжирование документов по релевантности поисковому запросу
  • классификация пар предложений по степени соответствия
  • выбор наиболее подходящих фрагментов для ответа в RAG

Ограничения

Модель ориентирована на английский язык, а качество переранжирования в приведённых тестах ниже, чем у базовой модели. Оценка пары является скалярным баллом соответствия, а не готовым текстовым ответом.

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 (subsets=['all'], train+val=True) 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("redis/langcache-reranker-v2-modernbert-bce-eps0.5")
# 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

MetricValue
accuracy0.8232
accuracy_threshold0.0889
f10.8135
f1_threshold0.0728
precision0.7605
recall0.8745
average_precision0.8287

Cross Encoder Reranking

  • Datasets: NanoQuoraRetrieval_R25, NanoMSMARCO_R25 and NanoNQ_R25
  • Evaluated with CrossEncoderRerankingEvaluator with these parameters:
    {
        "at_k": 1,
        "always_rerank_positives": true
    }
    
MetricNanoQuoraRetrieval_R25NanoMSMARCO_R25NanoNQ_R25
map0.7453 (-0.0851)0.4030 (-0.0848)0.3781 (-0.0419)
mrr@10.6200 (-0.1800)0.2600 (-0.0800)0.2000 (-0.0400)
ndcg@10.6100 (-0.1900)0.2600 (-0.0800)0.2000 (-0.0400)

Cross Encoder Nano BEIR

  • Dataset: NanoBEIR_R25_mean
  • Evaluated with CrossEncoderNanoBEIREvaluator with these parameters:
    {
        "dataset_names": [
            "QuoraRetrieval",
            "MSMARCO",
            "NQ"
        ],
        "rerank_k": 25,
        "at_k": 1,
        "always_rerank_positives": true
    }
    
MetricValue
map0.5088 (-0.0706)
mrr@10.3600 (-0.1000)
ndcg@10.3567 (-0.1033)

Training Details

Training Dataset

LangCache Sentence Pairs (subsets=['all'], train+val=True)

  • Dataset: LangCache Sentence Pairs (subsets=['all'], train+val=True)
  • Size: 40,015,318 training samples
  • Columns: sentence1, sentence2, and label
  • Approximate statistics based on the first 1000 samples:
    sentence1sentence2label
    typestringstringfloat
    detailsmin: 27 charactersmean: 110.99 charactersmax: 199 charactersmin: 29 charactersmean: 110.74 charactersmax: 191 charactersmin: 0.25mean: 0.49max: 0.75
  • Samples:
    sentence1sentence2label
    The film was remade in Telugu with the same name in 1981 by Chandra Mohan , Jayasudha , Chakravarthy and S. P. Balasubrahmanyam starring K. Vasu .The film was written in Telugu with the same name in 1981 by Chandra Mohan , Jayasudha , Chakravarthy and S. P. Balasubrahmanyam with K. Vasu .0.75
    The Cauchy stress tensor has therefore the following formTherefore , the cauchy tensor has the following form0.75
    John Jairo Culma , sometimes spelled as Jhon Jairo Culma ( born 17 March 1981 ) is a Colombian footballer .John Jairo Culma , sometimes known as Jhon Jairo Culma ( born March 17 , 1981 ) , is a Colombian footballer .0.75
  • Loss: BinaryCrossEntropyLoss with these parameters:
    {
        "activation_fn": "torch.nn.modules.linear.Identity",
        "pos_weight": 0.1028856709599495
    }
    

Evaluation Dataset

LangCache Sentence Pairs (split=test)

  • Dataset: LangCache Sentence Pairs (split=test)
  • Size: 74,265 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": 0.1028856709599495
    }
    

Training Hyperparameters

Non-Default Hyperparameters

  • eval_strategy: steps
  • per_device_train_batch_size: 48
  • per_device_eval_batch_size: 48
  • learning_rate: 0.0002
  • weight_decay: 0.001
  • num_train_epochs: 5
  • warmup_ratio: 0.1
  • load_best_model_at_end: True
  • optim: adamw_torch
  • ddp_find_unused_parameters: False
  • push_to_hub: True
  • hub_model_id: redis/langcache-reranker-v2-modernbert-bce-eps0.5
  • eval_on_start: 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: 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: 0.001
  • adam_beta1: 0.9
  • adam_beta2: 0.999
  • adam_epsilon: 1e-08
  • max_grad_norm: 1.0
  • num_train_epochs: 5
  • 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: 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}
  • parallelism_config: 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: False
  • 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: redis/langcache-reranker-v2-modernbert-bce-eps0.5
  • 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: True
  • use_liger_kernel: False
  • liger_kernel_config: None
  • eval_use_gather_object: False
  • average_tokens_across_devices: True
  • prompts: None
  • batch_sampler: no_duplicates
  • multi_dataset_batch_sampler: proportional
  • router_mapping: {}
  • learning_rate_mapping: {}

Training Logs

EpochStepTraining LossValidation Losstest_cls_average_precisionNanoQuoraRetrieval_R25_ndcg@1NanoMSMARCO_R25_ndcg@1NanoNQ_R25_ndcg@1NanoBEIR_R25_mean_ndcg@1
00-1.23440.72330.8300 (+0.0300)0.5200 (+0.1800)0.6100 (+0.3700)0.6533 (+0.1933)
0.004810000.9284------
0.009620000.7996------
0.014430000.5011------
0.019240000.1883------
0.024050000.1839------
0.028860000.183------
0.033670000.1823------
0.038480000.1816------
0.043290000.1811------
0.0480100000.1804------
0.0528110000.1796------
0.0576120000.179------
0.0624130000.1781------
0.0672140000.1775------
0.0720150000.177------
0.0768160000.1766------
0.0816170000.1763------
0.0864180000.176------
0.0912190000.1757------
0.0960200000.17560.15120.68680.4400 (-0.3600)0.2800 (-0.0600)0.3800 (+0.1400)0.3667 (-0.0933)
0.1008210000.1754------
0.1056220000.1753------
0.1104230000.1751------
0.1152240000.175------
0.1200250000.175------
0.1248260000.1748------
0.1296270000.1747------
0.1343280000.1746------
0.1391290000.1746------
0.1439300000.1744------
0.1487310000.1743------
0.1535320000.1743------
0.1583330000.1742------
0.1631340000.1742------
0.1679350000.1741------
0.1727360000.1741------
0.1775370000.1741------
0.1823380000.174------
0.1871390000.174------
0.1919400000.17390.14060.71330.4400 (-0.3600)0.2600 (-0.0800)0.2200 (-0.0200)0.3067 (-0.1533)
0.1967410000.174------
0.2015420000.1739------
0.2063430000.1739------
0.2111440000.1738------
0.2159450000.1738------
0.2207460000.1738------
0.2255470000.1738------
0.2303480000.1737------
0.2351490000.1737------
0.2399500000.1737------
0.2447510000.1736------
0.2495520000.1737------
0.2543530000.1737------
0.2591540000.1736------
0.2639550000.1736------
0.2687560000.1735------
0.2735570000.1736------
0.2783580000.1735------
0.2831590000.1736------
0.2879600000.17350.13530.76810.4400 (-0.3600)0.3200 (-0.0200)0.2700 (+0.0300)0.3433 (-0.1167)
0.2927610000.1735------
0.2975620000.1735------
0.3023630000.1736------
0.3071640000.1734------
0.3119650000.1734------
0.3167660000.1735------
0.3215670000.1735------
0.3263680000.1734------
0.3311690000.1735------
0.3359700000.1734------
0.3407710000.1733------
0.3455720000.1734------
0.3503730000.1734------
0.3551740000.1733------
0.3599750000.1733------
0.3647760000.1734------
0.3695770000.1733------
0.3743780000.1733------
0.3791790000.1733------
0.3839800000.17330.13060.78690.4800 (-0.3200)0.2800 (-0.0600)0.2200 (-0.0200)0.3267 (-0.1333)
0.3887810000.1733------
0.3934820000.1732------
0.3982830000.1732------
0.4030840000.1733------
0.4078850000.1732------
0.4126860000.1732------
0.4174870000.1732------
0.4222880000.1732------
0.4270890000.1732------
0.4318900000.1732------
0.4366910000.1732------
0.4414920000.1732------
0.4462930000.1733------
0.4510940000.1731------
0.4558950000.1732------
0.4606960000.1733------
0.4654970000.1732------
0.4702980000.1732------
0.4750990000.1731------
0.47981000000.17320.12880.80250.5200 (-0.2800)0.2500 (-0.0900)0.2000 (-0.0400)0.3233 (-0.1367)
0.48461010000.1732------
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0.56621180000.1731------
0.57101190000.1731------
0.57581200000.17310.12630.80910.7000 (-0.1000)0.2200 (-0.1200)0.2100 (-0.0300)0.3767 (-0.0833)
0.58061210000.1731------
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0.66211380000.173------
0.66691390000.173------
0.67171400000.17310.12570.82310.6800 (-0.1200)0.2600 (-0.0800)0.2600 (+0.0200)0.4000 (-0.0600)
0.67651410000.1731------
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0.76771600000.1730.12490.81860.6700 (-0.1300)0.2800 (-0.0600)0.2200 (-0.0200)0.3900 (-0.0700)
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0.86371800000.1730.12400.81270.5800 (-0.2200)0.2600 (-0.0800)0.2200 (-0.0200)0.3533 (-0.1067)
0.86851810000.173------
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0.95962000000.1730.12510.82140.7000 (-0.1000)0.2400 (-0.1000)0.1600 (-0.0800)0.3667 (-0.0933)
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  • The bold row denotes the saved checkpoint.

Framework Versions

  • Python: 3.12.3
  • Sentence Transformers: 5.1.0
  • Transformers: 4.56.0
  • PyTorch: 2.8.0+cu128
  • Accelerate: 1.10.1
  • Datasets: 4.0.0
  • Tokenizers: 0.22.0

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
ModernBertForSequenceClassification
Ревизия весов
f59539f

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