/ Модель

Характеристики

Контекст
8 192 токена
Параметры
149 млн
Архитектура
modernbert · dense
Базовая модель
ModernBERT-base
Библиотека
sentence-transformers
Опубликована
18 фев 2025

reranker-ModernBERT-base-nq-bce

Классификация текстаТекст → label9 профессий

Англоязычный CrossEncoder на базе ModernBERT оценивает пары «запрос—текст» для переранжирования результатов поиска и оценки семантического сходства.

24скачиваний за 30 дней0лайковвеса обновлены18 фев 2025

Бенчмарки

Как модель справилась с задачами пользователей ailist и что о ней публикуют открытые лидерборды

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 англоязычный CrossEncoder, дообученный командой tomaarsen на answerdotai/ModernBERT-base с библиотекой Sentence Transformers. Модель совместно обрабатывает пару текстов и выдаёт оценку их соответствия; её основное применение — ранжирование найденных документов по запросу, а также оценка семантического сходства и классификация текстовых пар.

Модель содержит 149 млн параметров, использует архитектуру ModernBERT для классификации последовательностей и принимает последовательности длиной до 8192 токенов. Она обучалась на данных Natural Questions и оценивалась на задачах переранжирования, включая NQ и наборы NanoBEIR; карточка приводит метрики MAP, MRR@10 и NDCG@10. Модель рассчитана на английский язык. Лицензия в предоставленных сведениях не указана.

Сценарии применения

  • переранжирование документов по запросу в поисковой выдаче
  • оценка релевантности фрагментов для RAG
  • поиск ответов на вопросы по корпусу документов
  • оценка семантического сходства двух текстов
  • поиск перефразировок и близких по смыслу текстов

Ограничения

Модель предназначена для английского языка и оценивает пару текстов, а не генерирует ответ; её качество за пределами описанных поисковых и оценочных наборов не подтверждено предоставленными данными.

Для каких профессий подходит9

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

CrossEncoder based on answerdotai/ModernBERT-base

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 semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

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

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("tomaarsen/reranker-ModernBERT-base-nq-bce")
# Get scores for pairs of texts
pairs = [
    ['difference between russian blue and british blue cat', 'Russian Blue The coat is known as a "double coat", with the undercoat being soft, downy and equal in length to the guard hairs, which are an even blue with silver tips. However, the tail may have a few very dull, almost unnoticeable stripes. The coat is described as thick, plush and soft to the touch. The feeling is softer than the softest silk. The silver tips give the coat a shimmering appearance. Its eyes are almost always a dark and vivid green. Any white patches of fur or yellow eyes in adulthood are seen as flaws in show cats.[3] Russian Blues should not be confused with British Blues (which are not a distinct breed, but rather a British Shorthair with a blue coat as the British Shorthair breed itself comes in a wide variety of colors and patterns), nor the Chartreux or Korat which are two other naturally occurring breeds of blue cats, although they have similar traits.'],
    ['who played the little girl on mrs doubtfire', 'Mara Wilson Mara Elizabeth Wilson[2] (born July 24, 1987) is an American writer and former child actress. She is known for playing Natalie Hillard in Mrs. Doubtfire (1993), Susan Walker in Miracle on 34th Street (1994), Matilda Wormwood in Matilda (1996) and Lily Stone in Thomas and the Magic Railroad (2000). Since retiring from film acting, Wilson has focused on writing.'],
    ['what year did the movie the sound of music come out', 'The Sound of Music (film) The film was released on March 2, 1965 in the United States, initially as a limited roadshow theatrical release. Although critical response to the film was widely mixed, the film was a major commercial success, becoming the number one box office movie after four weeks, and the highest-grossing film of 1965. By November 1966, The Sound of Music had become the highest-grossing film of all-time—surpassing Gone with the Wind—and held that distinction for five years. The film was just as popular throughout the world, breaking previous box-office records in twenty-nine countries. Following an initial theatrical release that lasted four and a half years, and two successful re-releases, the film sold 283 million admissions worldwide and earned a total worldwide gross of $286,000,000.'],
    ['where was the movie dawn of the dead filmed', 'Dawn of the Dead (2004 film) The mall scenes and rooftop scenes were shot in the former Thornhill Square Shopping Centre in Thornhill, Ontario, and the other scenes were shot in the Aileen-Willowbrook neighborhood of Thornhill. The set for Ana and Luis\'s bedroom was constructed in a back room of the mall.[7] The mall was defunct, which is the reason the production used it; the movie crew completely renovated the structure, and stocked it with fictitious stores after Starbucks and numerous other corporations refused to let their names be used[7] (two exceptions to this are Roots and Panasonic). Most of the mall was demolished shortly after the film was shot. The fictitious stores include a coffee shop called Hallowed Grounds (a lyric from Johnny Cash\'s song "The Man Comes Around", which was used over the opening credits), and an upscale department store called Gaylen Ross (an in-joke reference to one of the stars of the original 1978 film).'],
    ['where is the 2018 nba draft being held', "2018 NBA draft The 2018 NBA draft was held on June 21, 2018, at Barclays Center in Brooklyn, New York. National Basketball Association (NBA) teams took turns selecting amateur United States college basketball players and other eligible players, including international players. It was televised nationally by ESPN. This draft was the last to use the original weighted lottery system that gives teams near the bottom of the NBA draft better odds at the top three picks of the draft while teams higher up had worse odds in the process; the rule was agreed upon by the NBA on September 28, 2017, but would not be implemented until the 2019 draft.[2] With the last year of what was, at the time, the most recent lottery system (with the NBA draft lottery being held in Chicago instead of in New York), the Phoenix Suns won the first overall pick on May 15, 2018, with the Sacramento Kings at the second overall pick and the Atlanta Hawks at third overall pick.[3] The Suns' selection is their first No. 1 overall selection in franchise history. They would use that selection on the Bahamian center DeAndre Ayton from the nearby University of Arizona."],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'difference between russian blue and british blue cat',
    [
        'Russian Blue The coat is known as a "double coat", with the undercoat being soft, downy and equal in length to the guard hairs, which are an even blue with silver tips. However, the tail may have a few very dull, almost unnoticeable stripes. The coat is described as thick, plush and soft to the touch. The feeling is softer than the softest silk. The silver tips give the coat a shimmering appearance. Its eyes are almost always a dark and vivid green. Any white patches of fur or yellow eyes in adulthood are seen as flaws in show cats.[3] Russian Blues should not be confused with British Blues (which are not a distinct breed, but rather a British Shorthair with a blue coat as the British Shorthair breed itself comes in a wide variety of colors and patterns), nor the Chartreux or Korat which are two other naturally occurring breeds of blue cats, although they have similar traits.',
        'Mara Wilson Mara Elizabeth Wilson[2] (born July 24, 1987) is an American writer and former child actress. She is known for playing Natalie Hillard in Mrs. Doubtfire (1993), Susan Walker in Miracle on 34th Street (1994), Matilda Wormwood in Matilda (1996) and Lily Stone in Thomas and the Magic Railroad (2000). Since retiring from film acting, Wilson has focused on writing.',
        'The Sound of Music (film) The film was released on March 2, 1965 in the United States, initially as a limited roadshow theatrical release. Although critical response to the film was widely mixed, the film was a major commercial success, becoming the number one box office movie after four weeks, and the highest-grossing film of 1965. By November 1966, The Sound of Music had become the highest-grossing film of all-time—surpassing Gone with the Wind—and held that distinction for five years. The film was just as popular throughout the world, breaking previous box-office records in twenty-nine countries. Following an initial theatrical release that lasted four and a half years, and two successful re-releases, the film sold 283 million admissions worldwide and earned a total worldwide gross of $286,000,000.',
        'Dawn of the Dead (2004 film) The mall scenes and rooftop scenes were shot in the former Thornhill Square Shopping Centre in Thornhill, Ontario, and the other scenes were shot in the Aileen-Willowbrook neighborhood of Thornhill. The set for Ana and Luis\'s bedroom was constructed in a back room of the mall.[7] The mall was defunct, which is the reason the production used it; the movie crew completely renovated the structure, and stocked it with fictitious stores after Starbucks and numerous other corporations refused to let their names be used[7] (two exceptions to this are Roots and Panasonic). Most of the mall was demolished shortly after the film was shot. The fictitious stores include a coffee shop called Hallowed Grounds (a lyric from Johnny Cash\'s song "The Man Comes Around", which was used over the opening credits), and an upscale department store called Gaylen Ross (an in-joke reference to one of the stars of the original 1978 film).',
        "2018 NBA draft The 2018 NBA draft was held on June 21, 2018, at Barclays Center in Brooklyn, New York. National Basketball Association (NBA) teams took turns selecting amateur United States college basketball players and other eligible players, including international players. It was televised nationally by ESPN. This draft was the last to use the original weighted lottery system that gives teams near the bottom of the NBA draft better odds at the top three picks of the draft while teams higher up had worse odds in the process; the rule was agreed upon by the NBA on September 28, 2017, but would not be implemented until the 2019 draft.[2] With the last year of what was, at the time, the most recent lottery system (with the NBA draft lottery being held in Chicago instead of in New York), the Phoenix Suns won the first overall pick on May 15, 2018, with the Sacramento Kings at the second overall pick and the Atlanta Hawks at third overall pick.[3] The Suns' selection is their first No. 1 overall selection in franchise history. They would use that selection on the Bahamian center DeAndre Ayton from the nearby University of Arizona.",
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

Evaluation

Metrics

Cross Encoder Reranking

Metricnq-devNanoMSMARCONanoNFCorpusNanoNQ
map0.7329 (+0.0308)0.5611 (+0.0716)0.3497 (+0.0793)0.6827 (+0.2620)
mrr@100.7315 (+0.0325)0.5546 (+0.0771)0.5310 (+0.0312)0.6992 (+0.2725)
ndcg@100.7913 (+0.0313)0.6363 (+0.0959)0.3956 (+0.0706)0.7285 (+0.2279)

Cross Encoder Nano BEIR

MetricValue
map0.5312 (+0.1376)
mrr@100.5949 (+0.1269)
ndcg@100.5868 (+0.1315)

Training Details

Training Dataset

Unnamed Dataset

  • Size: 583,872 training samples
  • Columns: query, response, and label
  • Approximate statistics based on the first 1000 samples:
    queryresponselabel
    typestringstringint
    detailsmin: 29 charactersmean: 47.27 charactersmax: 98 charactersmin: 26 charactersmean: 615.6 charactersmax: 3146 characters1: 100.00%
  • Samples:
    queryresponselabel
    who plays harry in the amazing spiderman 2Dane DeHaan Dane William DeHaan (/dəˈhɑːn/ də-HAHN; born February 6, 1986[2][3][4]) is an American actor. His roles include Andrew Detmer in Chronicle (2012), Harry Osborn in The Amazing Spider-Man 2 (2014), Lockhart in Gore Verbinski's A Cure for Wellness (2016), and the title character in Luc Besson's Valerian and the City of a Thousand Planets (2017). He has also appeared in several advertisements for Prada.1
    when was the united federation of planets foundedUnited Federation of Planets In the series Star Trek: Enterprise, Earth Minister Nathan Samuels advocated the Coalition of Planets and invited other alien species, initially the Vulcans, Andorians and Tellarites, to become a part of this. The formation of the Coalition seems to have been the event that provoked the xenophobic Terra Prime incident in the episodes "Demons" and "Terra Prime". After Terra Prime leader John Frederick Paxton exploited the xenophobia on Earth, many of the aliens were unnerved and nearly abandoned the idea of a coalition. However, they were convinced by a speech from Captain Jonathan Archer to give the idea of a united organization of worlds a chance. Six years later in 2161,[8] the United Federation of Planets was organized.1
    who plays flores in orange is the new blackLaura Gómez (actress) Laura Gómez (born 1979) is a Dominican actress, speaker, writer, and director. She belongs to SAG-AFTRA and lives in New York City. Gómez is best known for her portrayal of the character Blanca Flores, an astute and disheveled prison inmate in the award-winning Netflix series Orange Is The New Black. In the fall of 2012 she won the NYU Technisphere Award for her short film To Kill a Roach.1
  • Loss: BinaryCrossEntropyLoss with these parameters:
    {
        "activation_fct": "torch.nn.modules.linear.Identity",
        "pos_weight": 5
    }
    

Evaluation Dataset

natural-questions

  • Dataset: natural-questions at f9e894e
  • Size: 100,231 evaluation samples
  • Columns: query, response, and label
  • Approximate statistics based on the first 1000 samples:
    queryresponselabel
    typestringstringint
    detailsmin: 27 charactersmean: 47.03 charactersmax: 96 charactersmin: 26 charactersmean: 608.17 charactersmax: 2639 characters1: 100.00%
  • Samples:
    queryresponselabel
    difference between russian blue and british blue catRussian Blue The coat is known as a "double coat", with the undercoat being soft, downy and equal in length to the guard hairs, which are an even blue with silver tips. However, the tail may have a few very dull, almost unnoticeable stripes. The coat is described as thick, plush and soft to the touch. The feeling is softer than the softest silk. The silver tips give the coat a shimmering appearance. Its eyes are almost always a dark and vivid green. Any white patches of fur or yellow eyes in adulthood are seen as flaws in show cats.[3] Russian Blues should not be confused with British Blues (which are not a distinct breed, but rather a British Shorthair with a blue coat as the British Shorthair breed itself comes in a wide variety of colors and patterns), nor the Chartreux or Korat which are two other naturally occurring breeds of blue cats, although they have similar traits.1
    who played the little girl on mrs doubtfireMara Wilson Mara Elizabeth Wilson[2] (born July 24, 1987) is an American writer and former child actress. She is known for playing Natalie Hillard in Mrs. Doubtfire (1993), Susan Walker in Miracle on 34th Street (1994), Matilda Wormwood in Matilda (1996) and Lily Stone in Thomas and the Magic Railroad (2000). Since retiring from film acting, Wilson has focused on writing.1
    what year did the movie the sound of music come outThe Sound of Music (film) The film was released on March 2, 1965 in the United States, initially as a limited roadshow theatrical release. Although critical response to the film was widely mixed, the film was a major commercial success, becoming the number one box office movie after four weeks, and the highest-grossing film of 1965. By November 1966, The Sound of Music had become the highest-grossing film of all-time—surpassing Gone with the Wind—and held that distinction for five years. The film was just as popular throughout the world, breaking previous box-office records in twenty-nine countries. Following an initial theatrical release that lasted four and a half years, and two successful re-releases, the film sold 283 million admissions worldwide and earned a total worldwide gross of $286,000,000.1
  • Loss: BinaryCrossEntropyLoss with these parameters:
    {
        "activation_fct": "torch.nn.modules.linear.Identity",
        "pos_weight": 5
    }
    

Training Hyperparameters

Non-Default Hyperparameters

  • eval_strategy: steps
  • per_device_train_batch_size: 64
  • per_device_eval_batch_size: 64
  • learning_rate: 2e-05
  • num_train_epochs: 1
  • warmup_ratio: 0.1
  • seed: 12
  • bf16: True
  • dataloader_num_workers: 4
  • 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: 64
  • per_device_eval_batch_size: 64
  • 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: 12
  • 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: 4
  • 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 Lossnq-dev_ndcg@10NanoMSMARCO_ndcg@10NanoNFCorpus_ndcg@10NanoNQ_ndcg@10NanoBEIR_mean_ndcg@10
-1-1--0.1432 (-0.6168)0.0574 (-0.4830)0.2881 (-0.0369)0.0121 (-0.4886)0.1192 (-0.3362)
0.000111.1359------
0.02192001.164------
0.04384000.9314------
0.06586000.403------
0.08778000.2647------
0.109610000.26510.94830.7421 (-0.0179)0.5703 (+0.0298)0.3594 (+0.0344)0.6641 (+0.1635)0.5313 (+0.0759)
0.131512000.2159------
0.153514000.185------
0.175416000.1925------
0.197318000.1814------
0.219220000.1640.98160.7700 (+0.0100)0.5722 (+0.0318)0.3713 (+0.0462)0.7361 (+0.2354)0.5599 (+0.1045)
0.241122000.159------
0.263124000.1429------
0.285026000.1611------
0.306928000.1464------
0.328830000.14890.84110.7819 (+0.0219)0.6213 (+0.0809)0.3578 (+0.0328)0.7051 (+0.2045)0.5614 (+0.1061)
0.350832000.1352------
0.372734000.1179------
0.394636000.1328------
0.416538000.1237------
0.438540000.12161.24620.7888 (+0.0288)0.6164 (+0.0760)0.3792 (+0.0542)0.7152 (+0.2146)0.5703 (+0.1149)
0.460442000.1174------
0.482344000.1129------
0.504246000.1252------
0.526148000.1131------
0.548150000.1210.80000.7849 (+0.0249)0.6144 (+0.0740)0.3792 (+0.0541)0.7235 (+0.2228)0.5723 (+0.1170)
0.570052000.1201------
0.591954000.1245------
0.613856000.1007------
0.635858000.1065------
0.657760000.1171.04590.7868 (+0.0268)0.5901 (+0.0497)0.3675 (+0.0425)0.7425 (+0.2419)0.5667 (+0.1114)
0.679662000.1128------
0.701564000.1002------
0.723466000.1049------
0.745468000.1071------
0.767370000.09550.94400.7937 (+0.0337)0.6168 (+0.0764)0.3771 (+0.0521)0.7289 (+0.2283)0.5743 (+0.1189)
0.789272000.0839------
0.811174000.1071------
0.833176000.0977------
0.855078000.1013------
0.876980000.10470.82500.7893 (+0.0293)0.6071 (+0.0667)0.3946 (+0.0695)0.7216 (+0.2210)0.5744 (+0.1191)
0.898882000.0956------
0.920784000.0879------
0.942786000.091------
0.964688000.0952------
0.986590000.0781.04370.7913 (+0.0313)0.6363 (+0.0959)0.3956 (+0.0706)0.7285 (+0.2279)0.5868 (+0.1315)
-1-1--0.7913 (+0.0313)0.6363 (+0.0959)0.3956 (+0.0706)0.7285 (+0.2279)0.5868 (+0.1315)
  • The bold row denotes the saved checkpoint.

Framework Versions

  • Python: 3.11.10
  • Sentence Transformers: 3.5.0.dev0
  • Transformers: 4.49.0.dev0
  • PyTorch: 2.6.0.dev20241112+cu121
  • Accelerate: 1.2.0
  • 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 · text-classification

Можно запустить

Подбираем GPU…

Класс архитектуры
ModernBertForSequenceClassification
Ревизия весов
dc36518

0 моделей