/ Модель

Характеристики

Контекст
8 192 токена
Параметры
149 млн
Лицензия
Apache 2.0
Архитектура
modernbert · dense
Базовая модель
ModernBERT-base
Библиотека
sentence-transformers
Опубликована
20 мар 2025

reranker-ModernBERT-base-gooaq-bce

Ранжирование текстаТекст → score8 профессий

Англоязычный cross-encoder на базе ModernBERT для переоценки релевантности пар текстов в поиске.

289скачиваний за 30 дней3лайкавеса обновлены25 мар 2025

Бенчмарки

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

Тесты пользователейпока нет

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  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

tomaarsen/reranker-ModernBERT-base-gooaq-bce — англоязычный cross-encoder, дообученный на данных GooAQ на базе answerdotai/ModernBERT-base с использованием Sentence Transformers. Модель получает пару текстов и оценивает их соответствие одним числовым баллом, который подходит для сортировки кандидатов в поисковой выдаче и семантическом поиске.

Модель содержит 149 млн параметров, использует архитектуру ModernBERT для классификации последовательностей и принимает последовательности длиной до 8192 токенов. В README приведены оценки на GooAQ и наборах NanoBEIR; результаты различаются между датасетами, поэтому качество на конкретной коллекции следует проверять отдельно.

Модель рассчитана на английский язык и распространяется по лицензии Apache-2.0. Как cross-encoder, она оценивает пары текстов, поэтому для масштабного поиска обычно требуется сначала получить ограниченный набор кандидатов, а затем ранжировать их этой моделью.

Сценарии применения

  • переранжирование результатов англоязычного поиска
  • сортировка ответов по релевантности вопросу
  • семантический поиск по коллекции документов
  • повышение качества выдачи после первичного поиска

Ограничения

Модель ориентирована на английский язык; перенос качества на другие языки не подтверждён. Результаты на разных тестовых наборах заметно различаются, поэтому качество для целевой коллекции следует оценивать 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

ModernBERT-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.

See training_gooaq_bce.py for the training script. This script is also described in the Cross Encoder > Training Overview documentation and the Training and Finetuning Reranker Models with Sentence Transformers v4 blogpost.

Model size vs NDCG for Rerankers on GooAQ

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
  • License: apache-2.0

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-gooaq-bce")
# Get scores for pairs of texts
pairs = [
    ['why are rye chips so good?', "It makes them taste that much better! The rye chips are tasty because they stand out--they're the saltiest thing in the bag. It's not because rye bread is inherently awesome. ... You could just buy a bag of rye chips."],
    ['why are rye chips so good?', 'There are no substantial technical, nutritional or performance issues associated with rye that would limit its use for pets. Rye is a fairly common ingredient in human foods and beverages. The most prevalent occurrence is in crackers and breads.'],
    ['why are rye chips so good?', 'Bread made wholly from rye flour is made in Germany and called pumpernickel. Rye is unique among grains for having a high level of fibre in its endosperm – not just in its bran. As such, the glycemic index (GI) of rye products is generally lower than products made from wheat and most other grains.'],
    ['why are rye chips so good?', 'KFC Chips – The salt mix on the seasoned chips and the actual chips do not contain any animal products. Our supplier/s of chips and seasoning have confirmed they are suitable for vegans.'],
    ['why are rye chips so good?', 'A study in the American Journal of Clinical Nutrition found that eating rye leads to better blood-sugar control compared to wheat. Rye bread is packed with magnesium, which helps control blood pressure and optimize heart health. Its high levels of soluble fibre can also reduce cholesterol.'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'why are rye chips so good?',
    [
        "It makes them taste that much better! The rye chips are tasty because they stand out--they're the saltiest thing in the bag. It's not because rye bread is inherently awesome. ... You could just buy a bag of rye chips.",
        'There are no substantial technical, nutritional or performance issues associated with rye that would limit its use for pets. Rye is a fairly common ingredient in human foods and beverages. The most prevalent occurrence is in crackers and breads.',
        'Bread made wholly from rye flour is made in Germany and called pumpernickel. Rye is unique among grains for having a high level of fibre in its endosperm – not just in its bran. As such, the glycemic index (GI) of rye products is generally lower than products made from wheat and most other grains.',
        'KFC Chips – The salt mix on the seasoned chips and the actual chips do not contain any animal products. Our supplier/s of chips and seasoning have confirmed they are suitable for vegans.',
        'A study in the American Journal of Clinical Nutrition found that eating rye leads to better blood-sugar control compared to wheat. Rye bread is packed with magnesium, which helps control blood pressure and optimize heart health. Its high levels of soluble fibre can also reduce cholesterol.',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

Evaluation

Metrics

Cross Encoder Reranking

MetricValue
map0.7308 (+0.1997)
mrr@100.7292 (+0.2052)
ndcg@100.7713 (+0.1801)

Cross Encoder Reranking

MetricValue
map0.7908 (+0.2597)
mrr@100.7890 (+0.2650)
ndcg@100.8351 (+0.2439)

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
map0.4579 (-0.0317)0.3414 (+0.0804)0.3932 (-0.0264)
mrr@100.4479 (-0.0296)0.5340 (+0.0342)0.3918 (-0.0349)
ndcg@100.5275 (-0.0130)0.3821 (+0.0571)0.4630 (-0.0377)

Cross Encoder Nano BEIR

  • Dataset: NanoBEIR_R100_mean
  • Evaluated with CrossEncoderNanoBEIREvaluator with these parameters:
    {
        "dataset_names": [
            "msmarco",
            "nfcorpus",
            "nq"
        ],
        "rerank_k": 100,
        "at_k": 10,
        "always_rerank_positives": true
    }
    
MetricValue
map0.3975 (+0.0074)
mrr@100.4579 (-0.0101)
ndcg@100.4575 (+0.0022)

Training Details

Training Dataset

Unnamed Dataset

  • Size: 578,402 training samples
  • Columns: question, answer, and label
  • Approximate statistics based on the first 1000 samples:
    questionanswerlabel
    typestringstringint
    detailsmin: 19 charactersmean: 45.14 charactersmax: 85 charactersmin: 65 charactersmean: 254.8 charactersmax: 379 characters0: ~82.90%1: ~17.10%
  • Samples:
    questionanswerlabel
    why are rye chips so good?It makes them taste that much better! The rye chips are tasty because they stand out--they're the saltiest thing in the bag. It's not because rye bread is inherently awesome. ... You could just buy a bag of rye chips.1
    why are rye chips so good?There are no substantial technical, nutritional or performance issues associated with rye that would limit its use for pets. Rye is a fairly common ingredient in human foods and beverages. The most prevalent occurrence is in crackers and breads.0
    why are rye chips so good?Bread made wholly from rye flour is made in Germany and called pumpernickel. Rye is unique among grains for having a high level of fibre in its endosperm – not just in its bran. As such, the glycemic index (GI) of rye products is generally lower than products made from wheat and most other grains.0
  • 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

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: batch_sampler
  • multi_dataset_batch_sampler: proportional

Training Logs

EpochStepTraining Lossgooaq-dev_ndcg@10NanoMSMARCO_R100_ndcg@10NanoNFCorpus_R100_ndcg@10NanoNQ_R100_ndcg@10NanoBEIR_R100_mean_ndcg@10
-1-1-0.1288 (-0.4624)0.0149 (-0.5255)0.2278 (-0.0972)0.0229 (-0.4777)0.0885 (-0.3668)
0.000111.0435-----
0.02212001.1924-----
0.04434001.1531-----
0.06646000.9371-----
0.08858000.6993-----
0.110610000.6690.7042 (+0.1130)0.4353 (-0.1051)0.3289 (+0.0039)0.4250 (-0.0757)0.3964 (-0.0590)
0.132812000.6257-----
0.154914000.6283-----
0.177016000.6014-----
0.199218000.5888-----
0.221320000.54930.7425 (+0.1513)0.4947 (-0.0457)0.3568 (+0.0318)0.4634 (-0.0373)0.4383 (-0.0171)
0.243422000.5479-----
0.265524000.5329-----
0.287726000.5208-----
0.309828000.5259-----
0.331930000.52210.7479 (+0.1567)0.5146 (-0.0258)0.3710 (+0.0460)0.4846 (-0.0160)0.4568 (+0.0014)
0.354132000.4977-----
0.376234000.4965-----
0.398336000.4985-----
0.420438000.4907-----
0.442640000.50580.7624 (+0.1712)0.5166 (-0.0238)0.3665 (+0.0415)0.4868 (-0.0138)0.4567 (+0.0013)
0.464742000.4885-----
0.486844000.495-----
0.509046000.4839-----
0.531148000.4983-----
0.553250000.47780.7603 (+0.1691)0.5110 (-0.0294)0.3540 (+0.0290)0.4809 (-0.0197)0.4487 (-0.0067)
0.575352000.4726-----
0.597554000.477-----
0.619656000.4613-----
0.641758000.4492-----
0.663960000.45060.7643 (+0.1731)0.5275 (-0.0129)0.3639 (+0.0389)0.4913 (-0.0094)0.4609 (+0.0055)
0.686062000.4618-----
0.708164000.463-----
0.730366000.4585-----
0.752468000.4612-----
0.774570000.46210.7649 (+0.1736)0.5105 (-0.0299)0.3688 (+0.0437)0.4552 (-0.0454)0.4448 (-0.0105)
0.796672000.4536-----
0.818874000.4515-----
0.840976000.4396-----
0.863078000.4542-----
0.885280000.43320.7669 (+0.1757)0.5247 (-0.0157)0.3794 (+0.0544)0.4370 (-0.0637)0.4470 (-0.0083)
0.907382000.447-----
0.929484000.4335-----
0.951586000.4179-----
0.973788000.4459-----
0.995890000.41960.7713 (+0.1801)0.5275 (-0.0130)0.3821 (+0.0571)0.4630 (-0.0377)0.4575 (+0.0022)
-1-1-0.7713 (+0.1801)0.5275 (-0.0130)0.3821 (+0.0571)0.4630 (-0.0377)0.4575 (+0.0022)
  • The bold row denotes the saved checkpoint.

Framework Versions

  • Python: 3.11.10
  • Sentence Transformers: 3.5.0.dev0
  • Transformers: 4.49.0
  • PyTorch: 2.5.1+cu124
  • Accelerate: 1.5.2
  • Datasets: 2.21.0
  • 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

Можно запустить

Подбираем GPU…

Класс архитектуры
ModernBertForSequenceClassification
Ревизия весов
5303a0f

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