/ Модель

Характеристики

Контекст
291 токен
Параметры
151 млн
Лицензия
Apache 2.0
Архитектура
ultrabert · dense
Базовая модель
UltraBERT-150M
Библиотека
sentence-transformers
Опубликована
14 окт 2026

ultrabert-150m-reranker-msmarco32k-3e-4-20261014

Ранжирование текстаТекст → score

Англоязычный cross-encoder на базе UltraBERT-150M, оценивающий релевантность пар запросов и текстов для повторного ранжирования поиска.

39скачиваний за 30 дней0лайковвеса обновлены14 окт 2026

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

UltraBERT-150M reranker MS MARCO pilot — англоязычный cross-encoder на базе UltraBERT-150M, дообученный автором tomaarsen с помощью библиотеки Sentence Transformers для оценки релевантности пар «запрос — фрагмент текста». Модель выдаёт один числовой балл для каждой пары; эти оценки можно использовать для повторного ранжирования результатов поиска и семантического поиска.

Модель содержит около 151 млн параметров, построена на архитектуре UltrabertForSequenceClassification и принимает текстовые пары с максимальной общей длиной последовательности 291 токен. Обучение проводилось на 65 536 примерах с запросами, фрагментами и двоичными метками релевантности; использовалась BinaryCrossEntropyLoss. Поддерживаемая модальность — текст, язык — английский.

Модель предназначена для ранжирования и оценки релевантности, а не для генерации ответов. В репозитории указана лицензия Apache-2.0.

Сценарии применения

  • повторное ранжирование результатов поисковой выдачи
  • оценка соответствия фрагментов текста поисковому запросу
  • отбор релевантных пассажей для семантического поиска

Ограничения

Максимальная длина входной последовательности составляет 291 токен, поэтому длинные пары текстов могут требовать предварительного сокращения. Модель рассчитана на английский язык и выдаёт оценку релевантности, а не готовый ответ.

Карточка модели

README от автора на Hugging Face, как он его опубликовал

Открыть на Hugging Face

UltraBERT-150M reranker MS MARCO pilot

This is a Cross Encoder model finetuned from dmis-lab/UltraBERT-150M 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: dmis-lab/UltraBERT-150M
  • Maximum Sequence Length: 291 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': 'UltrabertForSequenceClassification'})
)

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/ultrabert-150m-reranker-msmarco32k-3e-4-20261014")
# Get scores for pairs of inputs
pairs = [
    ['what clothing do people wear in denmark guys', 'While suits and other forms of Western fashion are ubiquitous in Japan, most people also wear some of the more traditional styles of Japanese clothing. For example, you often â\x80¦ will see people wearing traditional Japanese clothing at weddings or funerals, graduations and even for the summer fireworks festivals--hanabi. You can learn about some of the different styles of Japanese clothing and--if you dare--try some of them yourself.'],
    ['what clothing do people wear in denmark guys', 'Yet even the Democratic presidential candidates, who say they love or want to learn from those countries, donâ\x80\x99t seem know how they actually work. Why Weâ\x80\x99re Not Denmark Proof that they do work is delivered every year in data-rich evaluations by the United Nations and other international bodies.'],
    ['average salary healthcare administration', 'Healthcare Administrator Salary. (United States). A Healthcare Administrator earns an average salary of $64,821 per year. The highest paying skills associated with this job are Program Management, Budget Management, and Operations Management. Experience has a moderate effect on income for this job.ealthcare Administrator Salary. (United States). A Healthcare Administrator earns an average salary of $64,821 per year. The highest paying skills associated with this job are Program Management, Budget Management, and Operations Management. Experience has a moderate effect on income for this job.'],
    ['average salary healthcare administration', 'The five states with the highest average annual salaries for health care administrators, according to May 2011 data from the BLS, are: Massachusetts, $112,690; Rhode Island, $112,260; New York, $111,270; New Jersey, $110,100; and Washington, $108,620.'],
    ['what unique characteristics does bacillus megaterium have', 'Rare to none: Bacillus megaterium is a rod-shaped, gram-positive, endospore forming, aerotolerant species of bacteria used as a soil inoculant in agriculture and horticulture.Bacterium is arranged into the streptobacillus form.. Get help from a doctor now â\x80º. Rare to none: Bacillus megaterium is a rod-shaped, gram-positive, endospore forming, aerotolerant species of bacteria used as a soil inoculant in agriculture and horticulture. Bacterium is arranged into the streptobacillus form.'],
]
scores = model.predict(pairs)
print(scores)
# [-0.5898 -0.5977 -0.6016 -0.5977 -0.4219]

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'what clothing do people wear in denmark guys',
    [
        'While suits and other forms of Western fashion are ubiquitous in Japan, most people also wear some of the more traditional styles of Japanese clothing. For example, you often â\x80¦ will see people wearing traditional Japanese clothing at weddings or funerals, graduations and even for the summer fireworks festivals--hanabi. You can learn about some of the different styles of Japanese clothing and--if you dare--try some of them yourself.',
        'Yet even the Democratic presidential candidates, who say they love or want to learn from those countries, donâ\x80\x99t seem know how they actually work. Why Weâ\x80\x99re Not Denmark Proof that they do work is delivered every year in data-rich evaluations by the United Nations and other international bodies.',
        'Healthcare Administrator Salary. (United States). A Healthcare Administrator earns an average salary of $64,821 per year. The highest paying skills associated with this job are Program Management, Budget Management, and Operations Management. Experience has a moderate effect on income for this job.ealthcare Administrator Salary. (United States). A Healthcare Administrator earns an average salary of $64,821 per year. The highest paying skills associated with this job are Program Management, Budget Management, and Operations Management. Experience has a moderate effect on income for this job.',
        'The five states with the highest average annual salaries for health care administrators, according to May 2011 data from the BLS, are: Massachusetts, $112,690; Rhode Island, $112,260; New York, $111,270; New Jersey, $110,100; and Washington, $108,620.',
        'Rare to none: Bacillus megaterium is a rod-shaped, gram-positive, endospore forming, aerotolerant species of bacteria used as a soil inoculant in agriculture and horticulture.Bacterium is arranged into the streptobacillus form.. Get help from a doctor now â\x80º. Rare to none: Bacillus megaterium is a rod-shaped, gram-positive, endospore forming, aerotolerant species of bacteria used as a soil inoculant in agriculture and horticulture. Bacterium is arranged into the streptobacillus form.',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

Training Details

Training Dataset

Unnamed Dataset

  • Size: 65,536 training samples
  • Columns: query, passage, and label
  • Approximate statistics based on the first 100 samples:
    querypassagelabel
    typestringstringfloat
    modalitytexttext
    detailsmin: 5 tokensmean: 8.15 tokensmax: 16 tokensmin: 28 tokensmean: 86.58 tokensmax: 229 tokensmin: 0.0mean: 0.5max: 1.0
  • Samples:
    querypassagelabel
    what clothing do people wear in denmark guysWhile suits and other forms of Western fashion are ubiquitous in Japan, most people also wear some of the more traditional styles of Japanese clothing. For example, you often … will see people wearing traditional Japanese clothing at weddings or funerals, graduations and even for the summer fireworks festivals--hanabi. You can learn about some of the different styles of Japanese clothing and--if you dare--try some of them yourself.1.0
    what clothing do people wear in denmark guysYet even the Democratic presidential candidates, who say they love or want to learn from those countries, don’t seem know how they actually work. Why We’re Not Denmark Proof that they do work is delivered every year in data-rich evaluations by the United Nations and other international bodies.0.0
    average salary healthcare administrationHealthcare Administrator Salary. (United States). A Healthcare Administrator earns an average salary of $64,821 per year. The highest paying skills associated with this job are Program Management, Budget Management, and Operations Management. Experience has a moderate effect on income for this job.ealthcare Administrator Salary. (United States). A Healthcare Administrator earns an average salary of $64,821 per year. The highest paying skills associated with this job are Program Management, Budget Management, and Operations Management. Experience has a moderate effect on income for this job.1.0
  • Loss: BinaryCrossEntropyLoss with these parameters:
    {
        "activation_fn": "torch.nn.modules.linear.Identity",
        "pos_weight": 1.0
    }
    

Training Hyperparameters

Non-Default Hyperparameters

  • per_device_train_batch_size: 32
  • num_train_epochs: 1
  • learning_rate: 0.0003
  • warmup_steps: 0.05
  • optim: adamw_torch
  • weight_decay: 0.01
  • gradient_accumulation_steps: 4
  • bf16: True
  • tf32: True
  • logging_nan_inf_filter: False
  • disable_tqdm: True
  • per_device_eval_batch_size: 32
  • push_to_hub: True
  • hub_private_repo: False
  • hub_model_id: tomaarsen/ultrabert-150m-reranker-msmarco32k-3e-4-20261014
  • load_best_model_at_end: True
  • seed: 12
  • data_seed: 12
  • dataloader_num_workers: 2

All Hyperparameters

  • per_device_train_batch_size: 32
  • num_train_epochs: 1
  • max_steps: -1
  • learning_rate: 0.0003
  • lr_scheduler_type: linear
  • lr_scheduler_kwargs: None
  • warmup_steps: 0.05
  • optim: adamw_torch
  • optim_args: None
  • weight_decay: 0.01
  • adam_beta1: 0.9
  • adam_beta2: 0.999
  • adam_epsilon: 1e-08
  • optim_target_modules: None
  • gradient_accumulation_steps: 4
  • average_tokens_across_devices: True
  • max_grad_norm: 1.0
  • label_smoothing_factor: 0.0
  • bf16: True
  • fp16: False
  • bf16_full_eval: False
  • fp16_full_eval: False
  • tf32: True
  • gradient_checkpointing: False
  • gradient_checkpointing_kwargs: None
  • torch_compile: False
  • torch_compile_backend: None
  • torch_compile_mode: None
  • use_liger_kernel: False
  • liger_kernel_config: None
  • use_cache: False
  • neftune_noise_alpha: None
  • torch_empty_cache_steps: None
  • auto_find_batch_size: False
  • log_on_each_node: True
  • logging_nan_inf_filter: False
  • include_num_input_tokens_seen: no
  • log_level: passive
  • log_level_replica: warning
  • disable_tqdm: True
  • project: huggingface
  • trackio_space_id: None
  • trackio_bucket_id: None
  • trackio_static_space_id: None
  • per_device_eval_batch_size: 32
  • prediction_loss_only: True
  • eval_on_start: False
  • eval_do_concat_batches: True
  • eval_use_gather_object: False
  • eval_accumulation_steps: None
  • include_for_metrics: []
  • batch_eval_metrics: False
  • save_only_model: False
  • save_on_each_node: False
  • enable_jit_checkpoint: False
  • push_to_hub: True
  • hub_private_repo: False
  • hub_model_id: tomaarsen/ultrabert-150m-reranker-msmarco32k-3e-4-20261014
  • hub_strategy: every_save
  • hub_always_push: False
  • hub_revision: None
  • load_best_model_at_end: True
  • ignore_data_skip: False
  • restore_callback_states_from_checkpoint: False
  • full_determinism: False
  • seed: 12
  • data_seed: 12
  • use_cpu: 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
  • dataloader_drop_last: False
  • dataloader_num_workers: 2
  • dataloader_pin_memory: True
  • dataloader_persistent_workers: False
  • dataloader_prefetch_factor: None
  • dataloader_multiprocessing_context: None
  • dataloader_in_order: True
  • remove_unused_columns: True
  • label_names: None
  • train_sampling_strategy: random
  • length_column_name: length
  • ddp_find_unused_parameters: None
  • ddp_bucket_cap_mb: None
  • ddp_broadcast_buffers: False
  • ddp_static_graph: None
  • ddp_backend: None
  • ddp_timeout: 1800
  • fsdp: None
  • fsdp_config: None
  • deepspeed: None
  • debug: []
  • skip_memory_metrics: True
  • do_predict: False
  • resume_from_checkpoint: None
  • local_rank: -1
  • prompts: None
  • batch_sampler: batch_sampler
  • multi_dataset_batch_sampler: proportional
  • router_mapping: {}
  • learning_rate_mapping: {}
  • warmup_ratio: None

Training Logs

EpochStepTraining Loss
0.002011.2232
0.0195100.8604
0.0391200.7387
0.0586300.7557
0.0781400.7512
0.0977500.7275
0.1172600.7527
0.1367700.7213
0.1562800.7082
0.1758900.6967
0.19531000.7025
0.21481100.8037
0.23441200.7588
0.25128-

Training Time

  • Training: 52.3 seconds
  • Evaluation: 14.5 seconds
  • Total: 1.1 minutes

Framework Versions

  • Python: 3.12.12
  • Sentence Transformers: 6.2.0.dev0
  • Transformers: 5.16.1
  • PyTorch: 2.10.0+cu128
  • Accelerate: 1.15.0
  • Datasets: 5.1.0
  • Tokenizers: 0.23.3

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",
}

Controlled backbone comparison

Part of the encoder comparison. One epoch maximum on 32,768 MS MARCO triplets, seed 12. The best validation checkpoint was recovered from the repository history after a publication error. No training was repeated. See experiment.json for its original revision and validation score. Learning-rate selection uses validation only. Test data does not select checkpoints.

Dense and multi-vector use query length 32 and passage length 256 including special tokens. Rerankers use 32 query content tokens, 256 passage content tokens and a combined cap of 291. See the comparison scripts for matching input clipping. These are small-data research models.

Как запустить

На своей GPU в ailist. Цена — по текущим предложениям провайдеров

GPU в ailist

Универсальный рантайм · sentence-transformers

Нельзя запустить

Модели нужен код из репозитория; автор пока не в списке доверенных организаций.

Класс архитектуры
UltrabertForSequenceClassification
Ревизия весов
322729f

0 моделей