/ Модель

Характеристики

Контекст
8 192 токена
Параметры
149 млн
Лицензия
Apache 2.0
Архитектура
modernbert · dense
Базовая модель
ModernBERT-base
Библиотека
sentence-transformers
Опубликована
18 сен 2026

reranker-ModernBERT-base-gooaq-bce-50k

Ранжирование текстаТекст → score8 профессий

Англоязычный кросс-энкодер на базе ModernBERT для оценки релевантности пар текстов и повторного ранжирования результатов поиска.

25скачиваний за 30 дней0лайковвеса обновлены18 сен 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 «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

Англоязычный кросс-энкодер kwondw/reranker-ModernBERT-base-gooaq-bce-50k разработан на базе answerdotai/ModernBERT-base и дообучен на данных GooAQ с помощью Sentence Transformers. Модель совместно анализирует пару текстов и выдаёт оценку релевантности, подходящую для повторного ранжирования результатов поиска и семантического поиска.

Модель содержит 149 млн параметров, использует архитектуру ModernBertForSequenceClassification и обрабатывает текстовые входы с контекстом до 8192 токенов. В карточке приведены результаты оценки на GooAQ и наборах NanoMSMARCO, NanoNFCorpus, NanoNQ и NanoBEIR; показатели различаются между наборами, поэтому качество зависит от данных и сценария применения.

Лицензия — Apache-2.0. Это кросс-энкодер, который оценивает пары текстов; его можно применять для ранжирования уже отобранных кандидатов, но карточка не заявляет генерацию текста.

Сценарии применения

  • повторное ранжирование ответов на вопросы
  • сортировка документов по релевантности запросу
  • улучшение порядка результатов семантического поиска
  • оценка соответствия пары «вопрос — ответ»
  • переранжирование кандидатов в конвейере RAG

Ограничения

Модель рассчитана на английский язык и оценивает пары текстов, а не генерирует ответы. Результаты оценки в README заметно различаются между наборами данных, поэтому качество на конкретном поисковом корпусе следует проверять отдельно.

Для каких профессий подходит8

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

MordernBERT-base trained on GooAQ

This is a Cross Encoder model finetuned from answerdotai/ModernBERT-base using the sentence-transformers library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.

Model Details

Model Description

  • Model Type: Cross Encoder
  • Base model: answerdotai/ModernBERT-base
  • Maximum Sequence Length: 8192 tokens
  • Number of Output Labels: 1 label
  • Supported Modality: Text
  • Language: en
  • License: apache-2.0

Model Sources

Full Model Architecture

CrossEncoder(
  (0): Transformer({'transformer_task': 'sequence-classification', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'logits'}}, 'module_output_name': 'scores', 'architecture': 'ModernBertForSequenceClassification'})
)

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("kwondw/reranker-ModernBERT-base-gooaq-bce-50k")
# Get scores for pairs of inputs
pairs = [
    ['is corpse bride on hulu?', ". Watch Corpse Bride on Hulu either streaming online or on your favorite device. It's easy to watch Corpse Bride streaming online if you have either the Hulu basic or the Hulu Premium. ... HBO Max now has Corpse Bride streaming with a subscription."],
    ['is corpse bride on hulu?', 'When you put your day-of timeline together, try to keep these guidelines in mind: Makeup: 30–45 minutes per bridesmaid and mother of the bride, 60–90 minutes for the bride. Hair: 30–45 minutes per bridesmaid and mother of the bride, 60–90 minutes for the bride.'],
    ['is corpse bride on hulu?', "A dowry is a transfer of parental property, gifts, or money at the marriage of a daughter (bride). ... While bride price or bride service is a payment by the groom or his family to the bride's parents, dowry is the wealth transferred from the bride's family to the groom or his family, ostensibly for the bride."],
    ['is corpse bride on hulu?', 'The traditional wedding speech order goes father of the bride, groom, best man and other toasts. However, lots of brides now choose to give a speech, as do the bridesmaids or maid of honour. If you are including these, the order should go: father of the bride, groom, bride, bridesmaids/maid of honour, best man.'],
    ['is corpse bride on hulu?', "If the bride can afford to, it's a very thoughtful gesture for her to pay for the dress or a portion of the cost for each of her bridesmaids. ... Generally speaking, bridesmaids are expected to pay for their own dresses and accessories, as well as potentially hair and makeup appointments and transportation to the wedding."],
]
scores = model.predict(pairs)
print(scores)
# [9.9504e-01 4.7737e-03 1.2148e-04 1.8603e-03 2.1824e-04]

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'is corpse bride on hulu?',
    [
        ". Watch Corpse Bride on Hulu either streaming online or on your favorite device. It's easy to watch Corpse Bride streaming online if you have either the Hulu basic or the Hulu Premium. ... HBO Max now has Corpse Bride streaming with a subscription.",
        'When you put your day-of timeline together, try to keep these guidelines in mind: Makeup: 30–45 minutes per bridesmaid and mother of the bride, 60–90 minutes for the bride. Hair: 30–45 minutes per bridesmaid and mother of the bride, 60–90 minutes for the bride.',
        "A dowry is a transfer of parental property, gifts, or money at the marriage of a daughter (bride). ... While bride price or bride service is a payment by the groom or his family to the bride's parents, dowry is the wealth transferred from the bride's family to the groom or his family, ostensibly for the bride.",
        'The traditional wedding speech order goes father of the bride, groom, best man and other toasts. However, lots of brides now choose to give a speech, as do the bridesmaids or maid of honour. If you are including these, the order should go: father of the bride, groom, bride, bridesmaids/maid of honour, best man.',
        "If the bride can afford to, it's a very thoughtful gesture for her to pay for the dress or a portion of the cost for each of her bridesmaids. ... Generally speaking, bridesmaids are expected to pay for their own dresses and accessories, as well as potentially hair and makeup appointments and transportation to the wedding.",
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

Evaluation

Metrics

Cross Encoder Reranking

MetricValue
map0.5969 (+0.2482)
mrr@100.5964 (+0.2582)
ndcg@100.6408 (+0.2404)

Cross Encoder Reranking

  • Datasets: NanoMSMARCO_R100, NanoNFCorpus_R100 and NanoNQ_R100
  • Evaluated with CrossEncoderRerankingEvaluator with these parameters:
    {
        "at_k": 10,
        "always_rerank_positives": true
    }
    
MetricNanoMSMARCO_R100NanoNFCorpus_R100NanoNQ_R100
map0.4076 (-0.0820)0.3461 (+0.0851)0.4223 (+0.0027)
mrr@100.3912 (-0.0863)0.4815 (-0.0183)0.4446 (+0.0179)
ndcg@100.4703 (-0.0702)0.3600 (+0.0349)0.4739 (-0.0267)

Cross Encoder Nano BEIR

  • Dataset: NanoBEIR_R100_mean
  • Evaluated with CrossEncoderNanoBEIREvaluator with these parameters:
    {
        "dataset_names": [
            "msmarco",
            "nfcorpus",
            "nq"
        ],
        "dataset_id": "sentence-transformers/NanoBEIR-en",
        "rerank_k": 100,
        "at_k": 10,
        "always_rerank_positives": true
    }
    
MetricValue
map0.3920 (+0.0019)
mrr@100.4391 (-0.0289)
ndcg@100.4347 (-0.0207)

Training Details

Training Dataset

Unnamed Dataset

  • Size: 2,758,533 training samples
  • Columns: question, answer, and label
  • Approximate statistics based on the first 100 samples:
    questionanswerlabel
    typestringstringint
    modalitytexttext
    detailsmin: 9 tokensmean: 12.59 tokensmax: 18 tokensmin: 18 tokensmean: 58.07 tokensmax: 135 tokens0: ~81.73%1: ~18.27%
  • Samples:
    questionanswerlabel
    is corpse bride on hulu?. Watch Corpse Bride on Hulu either streaming online or on your favorite device. It's easy to watch Corpse Bride streaming online if you have either the Hulu basic or the Hulu Premium. ... HBO Max now has Corpse Bride streaming with a subscription.1
    is corpse bride on hulu?When you put your day-of timeline together, try to keep these guidelines in mind: Makeup: 30–45 minutes per bridesmaid and mother of the bride, 60–90 minutes for the bride. Hair: 30–45 minutes per bridesmaid and mother of the bride, 60–90 minutes for the bride.0
    is corpse bride on hulu?A dowry is a transfer of parental property, gifts, or money at the marriage of a daughter (bride). ... While bride price or bride service is a payment by the groom or his family to the bride's parents, dowry is the wealth transferred from the bride's family to the groom or his family, ostensibly for the bride.0
  • Loss: BinaryCrossEntropyLoss with these parameters:
    {
        "activation_fn": "torch.nn.modules.linear.Identity",
        "pos_weight": 5
    }
    

Training Hyperparameters

Non-Default Hyperparameters

  • per_device_train_batch_size: 64
  • per_device_eval_batch_size: 64
  • learning_rate: 2e-05
  • num_train_epochs: 1
  • warmup_steps: 0.1
  • seed: 12
  • fp16: True
  • dataloader_num_workers: 2
  • load_best_model_at_end: True
  • ddp_find_unused_parameters: False
  • dataloader_persistent_workers: True

All Hyperparameters

  • do_predict: False
  • prediction_loss_only: True
  • per_device_train_batch_size: 64
  • per_device_eval_batch_size: 64
  • 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: None
  • warmup_ratio: None
  • warmup_steps: 0.1
  • 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: 12
  • data_seed: None
  • bf16: False
  • fp16: True
  • bf16_full_eval: False
  • fp16_full_eval: False
  • tf32: None
  • local_rank: -1
  • ddp_backend: None
  • debug: []
  • dataloader_drop_last: True
  • dataloader_num_workers: 2
  • 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: False
  • ddp_bucket_cap_mb: None
  • ddp_broadcast_buffers: False
  • dataloader_pin_memory: True
  • dataloader_persistent_workers: True
  • 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 Lossgooaq-dev_ndcg@10NanoMSMARCO_R100_ndcg@10NanoNFCorpus_R100_ndcg@10NanoNQ_R100_ndcg@10NanoBEIR_R100_mean_ndcg@10
0.000011.1154-----
0.00932001.2221-----
0.01864001.2006-----
0.02786001.1757-----
0.03718001.0125-----
0.046410000.8074-----
0.055712000.7453-----
0.065014000.7043-----
0.074216000.6840-----
0.083518000.6631-----
0.092820000.6615-----
0.10002156-0.5889 (+0.1884)0.4139 (-0.1265)0.3892 (+0.0642)0.5487 (+0.0481)0.4506 (-0.0048)
0.102122000.6360-----
0.111424000.6156-----
0.120626000.6007-----
0.129928000.5962-----
0.139230000.6028-----
0.148532000.5925-----
0.157834000.5591-----
0.167036000.5705-----
0.176338000.5607-----
0.185640000.5744-----
0.194942000.5625-----
0.20014312-0.6086 (+0.2081)0.4460 (-0.0944)0.4204 (+0.0953)0.4800 (-0.0207)0.4488 (-0.0066)
0.204244000.5490-----
0.213446000.5529-----
0.222748000.5494-----
0.232050000.5420-----
0.241352000.5184-----
0.250654000.5416-----
0.259856000.5144-----
0.269158000.5260-----
0.278460000.5048-----
0.287762000.5353-----
0.297064000.5199-----
0.30016468-0.6113 (+0.2109)0.4895 (-0.0509)0.3925 (+0.0675)0.4662 (-0.0345)0.4494 (-0.0060)
0.306366000.5116-----
0.315568000.5180-----
0.324870000.5109-----
0.334172000.5095-----
0.343474000.5009-----
0.352776000.5016-----
0.361978000.4985-----
0.371280000.5046-----
0.380582000.4984-----
0.389884000.4969-----
0.399186000.5002-----
0.40028624-0.6300 (+0.2296)0.4809 (-0.0595)0.3848 (+0.0597)0.4699 (-0.0308)0.4452 (-0.0102)
0.408388000.4910-----
0.417690000.5025-----
0.426992000.4854-----
0.436294000.4911-----
0.445596000.4874-----
0.454798000.4895-----
0.4640100000.4803-----
0.4733102000.4876-----
0.4826104000.4893-----
0.4919106000.4774-----
0.500210780-0.6352 (+0.2347)0.5559 (+0.0154)0.3819 (+0.0568)0.5335 (+0.0329)0.4904 (+0.0351)
0.5011108000.4688-----
0.5104110000.4723-----
0.5197112000.4752-----
0.5290114000.4711-----
0.5383116000.4702-----
0.5475118000.4680-----
0.5568120000.4739-----
0.5661122000.4529-----
0.5754124000.4709-----
0.5847126000.4518-----
0.5939128000.4726-----
0.600312936-0.6405 (+0.2400)0.5069 (-0.0335)0.3581 (+0.0331)0.5042 (+0.0036)0.4564 (+0.0010)
0.6032130000.4698-----
0.6125132000.4627-----
0.6218134000.4562-----
0.6311136000.4566-----
0.6403138000.4775-----
0.6496140000.4532-----
0.6589142000.4660-----
0.6682144000.4488-----
0.6775146000.4482-----
0.6867148000.4515-----
0.6960150000.4596-----
0.700315092-0.6413 (+0.2409)0.4668 (-0.0736)0.3518 (+0.0268)0.4538 (-0.0469)0.4241 (-0.0312)
0.7053152000.4418-----
0.7146154000.4499-----
0.7239156000.4542-----
0.7331158000.4636-----
0.7424160000.4371-----
0.7517162000.4344-----
0.7610164000.4535-----
0.7703166000.4429-----
0.7795168000.4435-----
0.7888170000.4398-----
0.7981172000.4441-----
0.800317248-0.6410 (+0.2406)0.5221 (-0.0183)0.3324 (+0.0074)0.4941 (-0.0065)0.4496 (-0.0058)
0.8074174000.4501-----
0.8167176000.4501-----
0.8259178000.4228-----
0.8352180000.4395-----
0.8445182000.4383-----
0.8538184000.4414-----
0.8631186000.4315-----
0.8723188000.4306-----
0.8816190000.4249-----
0.8909192000.4356-----
0.9002194000.4326-----
0.900419404-0.6408 (+0.2404)0.4703 (-0.0702)0.3600 (+0.0349)0.4739 (-0.0267)0.4347 (-0.0207)
0.9095196000.4431-----
0.9188198000.4199-----
0.9280200000.4409-----
0.9373202000.4351-----
0.9466204000.4261-----
0.9559206000.4245-----
0.9652208000.4292-----
0.9744210000.4300-----
0.9837212000.4298-----
0.9930214000.4263-----
  • The bold row denotes the saved checkpoint.

Training Time

  • Training: 3.8 hours
  • Evaluation: 26.3 minutes
  • Total: 4.2 hours

Framework Versions

  • Python: 3.12.13
  • Sentence Transformers: 6.0.1
  • Transformers: 5.0.0
  • PyTorch: 2.10.0+cu128
  • Accelerate: 1.13.0
  • Datasets: 5.0.0
  • Tokenizers: 0.22.2

Additional Resources

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
e120ffb

0 моделей