/ Модель

Характеристики

Контекст
1 024 токена
Параметры
50,3 млн
Архитектура
bert · dense
Библиотека
sentence-transformers
Опубликована
23 мар 2025

Bioformer-16L-UMLS-Pubmed_PMC-Forward_TCE-Epoch-3-MSMARCO-Epoch-5

ЭмбеддингиТекст → embedding9 профессий

BERT-модель Sentence Transformer создаёт 384-мерные эмбеддинги для семантического поиска и сравнения текстов.

110скачиваний за 30 дней0лайковвеса обновлены23 мар 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 «Эмбеддинги» — 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

Это модель Sentence Transformer на базе BERT, опубликованная pankajrajdeo. Она преобразует предложения и абзацы в плотные векторы размерностью 384, пригодные для сравнения по косинусному сходству. Архитектура использует усреднение токенных представлений; максимальная длина входа составляет 1024 токена. В метаданных указано 50,3 млн параметров, библиотека sentence-transformers и формат safetensors.

Модель предназначена для поиска семантически близких текстов, оценки сходства предложений, поиска перефразировок, кластеризации и классификации текста на основе эмбеддингов. Карточка сообщает об обучении на парах anchor-positive и использует MultipleNegativesRankingLoss. Название репозитория указывает на биомедицинскую тематику обучения, в том числе UMLS, PubMed и PMC, однако описание датасета в README приводит также примеры общего характера.

Языки и лицензия в предоставленных данных не указаны. Для работы с моделью подходит библиотека Sentence Transformers; результатом являются векторы текста, а не готовые ответы или метки. Качество на конкретных языках и предметных областях следует проверять отдельно.

Сценарии применения

  • поиск документов по смыслу запроса
  • ранжирование текстов по семантическому сходству
  • поиск перефразировок
  • кластеризация предложений и абзацев
  • классификация текста на основе эмбеддингов

Ограничения

В предоставленных данных не подтверждены поддерживаемые языки и качество на отдельных доменах; проверьте пригодность модели на целевых текстах. Она выдаёт эмбеддинги, поэтому для получения ответов или меток требуется отдельный компонент.

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

SentenceTransformer

This is a sentence-transformers model trained on the parquet dataset. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

  • Model Type: Sentence Transformer
  • Maximum Sequence Length: 1024 tokens
  • Output Dimensionality: 384 dimensions
  • Similarity Function: Cosine Similarity
  • Training Dataset:
    • parquet

Model Sources

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 1024, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)

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 SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("pankajrajdeo/Bioformer-16L-UMLS-Pubmed_PMC-Forward_TCE-Epoch-3-MSMARCO-Epoch-5")
# Run inference
sentences = [
    'does the columbus zoo sell beer',
    'No glass and/or alcohol are permitted at the Columbus Zoo. This means that they do not sell alcoholic beverages.',
    'Eviction law allows landlords to still ask you to move out, but you must be afforded some extra protections. First, for eviction notices without cause, the landlord must give you a longer period of notice to vacate, generally 30 or 60 days. This lengthened time period is designed to allow you to find another place to live.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Training Details

Training Dataset

parquet

  • Dataset: parquet
  • Size: 39,780,704 training samples
  • Columns: anchor and positive
  • Approximate statistics based on the first 1000 samples:
    anchorpositive
    typestringstring
    detailsmin: 4 tokensmean: 9.85 tokensmax: 38 tokensmin: 15 tokensmean: 87.54 tokensmax: 246 tokens
  • Samples:
    anchorpositive
    is a little caffeine ok during pregnancyWe don’t know a lot about the effects of caffeine during pregnancy on you and your baby. So it’s best to limit the amount you get each day. If you’re pregnant, limit caffeine to 200 milligrams each day. This is about the amount in 1½ 8-ounce cups of coffee or one 12-ounce cup of coffee.
    what fruit is native to australiaPassiflora herbertiana. A rare passion fruit native to Australia. Fruits are green-skinned, white fleshed, with an unknown edible rating. Some sources list the fruit as edible, sweet and tasty, while others list the fruits as being bitter and inedible.assiflora herbertiana. A rare passion fruit native to Australia. Fruits are green-skinned, white fleshed, with an unknown edible rating. Some sources list the fruit as edible, sweet and tasty, while others list the fruits as being bitter and inedible.
    how large is the canadian militaryThe Canadian Armed Forces. 1 The first large-scale Canadian peacekeeping mission started in Egypt on November 24, 1956. 2 There are approximately 65,000 Regular Force and 25,000 reservist members in the Canadian military. 3 In Canada, August 9 is designated as National Peacekeepers’ Day.
  • Loss: MultipleNegativesRankingLoss with these parameters:
    {
        "scale": 20.0,
        "similarity_fct": "cos_sim"
    }
    

Evaluation Dataset

parquet

  • Dataset: parquet
  • Size: 39,780,704 evaluation samples
  • Columns: anchor and positive
  • Approximate statistics based on the first 1000 samples:
    anchorpositive
    typestringstring
    detailsmin: 4 tokensmean: 9.97 tokensmax: 28 tokensmin: 28 tokensmean: 85.19 tokensmax: 228 tokens
  • Samples:
    anchorpositive
    chemical weathering definitionChemical weathering is the process where rocks and minerals, which originally formed deep underground at much higher temperatures and pressures, gradually transform into different chemical compounds once they are exposed to air and water at the surface.
    what is the difference between breathe and breath• The word breath is used as noun. • On the other hand, the word breathe is used as verb. This is the main difference between the two words. • The word breath is used in the sense of ‘air taken in and out during breathing’. • On the other hand, the word breathe is used in the sense of ‘take air into the lungs and then let it out’. • The word breathe is sometimes used with the expression ‘his/her last’, and it gives the meaning of ‘die.’ This is used for both breath and breathe. His last breath, breathed her last.
    what is natural neck tighteningUse Sunscreen: One of the best, and a natural method for tightening skin includes applying sunscreen on the face and neck area. This will help to protect against UV rays that can be harmful and help to prevent the premature aging of your skin.
  • Loss: MultipleNegativesRankingLoss with these parameters:
    {
        "scale": 20.0,
        "similarity_fct": "cos_sim"
    }
    

Training Hyperparameters

Non-Default Hyperparameters

  • eval_strategy: steps
  • per_device_train_batch_size: 128
  • learning_rate: 2e-05
  • num_train_epochs: 5
  • max_steps: 1476235
  • log_level: info
  • fp16: True
  • dataloader_num_workers: 16
  • load_best_model_at_end: True
  • resume_from_checkpoint: True

All Hyperparameters

  • overwrite_output_dir: False
  • do_predict: False
  • eval_strategy: steps
  • prediction_loss_only: True
  • per_device_train_batch_size: 128
  • per_device_eval_batch_size: 8
  • 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: 5
  • max_steps: 1476235
  • lr_scheduler_type: linear
  • lr_scheduler_kwargs: {}
  • warmup_ratio: 0.0
  • warmup_steps: 0
  • log_level: info
  • 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: True
  • 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: 16
  • 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: True
  • 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 LossValidation Loss
0.000010.739-
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0.006820000.2813-
0.010230000.22-
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0.1219360000.1038-
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Framework Versions

  • Python: 3.11.11
  • Sentence Transformers: 3.4.1
  • Transformers: 4.48.2
  • PyTorch: 2.6.0+cu124
  • Accelerate: 1.5.2
  • 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",
}

MultipleNegativesRankingLoss

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

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
BertModel
Ревизия весов
831812b

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