/ Модель

Характеристики

Контекст
512 токенов
Параметры
137 млн
Архитектура
bert · dense
Базовая модель
GIST-Embedding-v0
Библиотека
sentence-transformers
Опубликована
29 июн 2024

ecommerce_text_embedding

ЭмбеддингиТекст → embedding3 профессии

Дообученная BERT-модель создаёт 768-мерные эмбеддинги для семантического поиска и сравнения текстов, в том числе в сценариях электронной коммерции.

122скачиваний за 30 дней0лайковвеса обновлены29 июн 2024

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

wjunwei/ecommerce_text_embedding — дообученная модель Sentence Transformers на базе avsolatorio/GIST-Embedding-v0 для представления предложений и абзацев в виде плотных текстовых эмбеддингов. Она использует BERT с пуллингом CLS-токена и нормализацией векторов; размерность эмбеддинга — 768, максимальная длина входа — 512 токенов. Для сравнения векторов подходит косинусное сходство.

Модель предназначена для семантического поиска и сравнения текстов, поиска перефразировок, кластеризации и классификации текстов. Название и обучающий набор указывают на связь с электронной коммерцией, но карточка не уточняет конкретные товарные сценарии или языковую специализацию. Доступна в формате safetensors через библиотеку sentence-transformers; размер модели — 136,7 млн параметров. Лицензия в предоставленных данных не указана.

Сценарии применения

  • семантический поиск товаров по текстовому запросу
  • сравнение описаний товаров по смыслу
  • поиск перефразированных запросов покупателей
  • кластеризация текстов отзывов или карточек товаров
  • классификация текстов на основе эмбеддингов

Ограничения

Максимальная длина входа ограничена 512 токенами. Языковая специализация и лицензия в предоставленных данных не указаны.

Для каких профессий подходит3

Разделы каталога профессий, в задачах которых используется эта модель

Все профессии

Карточка модели

README от автора на Hugging Face, как он его опубликовал

Открыть на Hugging Face

SentenceTransformer based on avsolatorio/GIST-Embedding-v0

This is a sentence-transformers model finetuned from avsolatorio/GIST-Embedding-v0. It maps sentences & paragraphs to a 768-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
  • Base model: avsolatorio/GIST-Embedding-v0
  • Maximum Sequence Length: 512 tokens
  • Output Dimensionality: 768 tokens
  • Similarity Function: Cosine Similarity

Model Sources

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)

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("wjunwei/ecommerce_text_embedding")
# Run inference
sentences = [
    'magic the gathering modern horizons  bundle gift edition  deluxe bundle with  collector booster  play boosters fullart lands  exclusive accessories give the gift of magicthe perfect present for any magic fan this gift bundle is full of exclusive accessories and the coolest cards from modern horizons  including a collector booster packed with rare cards shiny foils and altart altframe cards moderns never been more marvelousintroducing a heaping helping of exciting cards for modern one of magics most celebrated formats plus the return of competitive favorites theres something for every fan to love in modern horizons  a collector booster full of treasuresboth powerful and flashy the included collector booster is packed with only the coolest cards and is the only place fans may find foiletched cards textured foil cards or serialized cards printed with a unique number boosters for playing  building decksplay boosters are the best packs for playing magic with friends and are fun to open with a possibility of multiple rares and at least  shining foil card in every pack fullart lands  special alternateart cardevery mh bundle gift edition also comes with  traditional foil promo card featuring bundleexclusive alternate art and  of the  included land cards feature stunning fullart  foil  nonfoil exclusive accessorieseach gift bundle also comes with exclusive accessories including a special die to track your life total as you play and a sturdy box to store everything in bundle gift edition contents collector booster  play boosters  traditional foil altart card  land cards  foil  nonfoil  spindown life counter  card storage box and  reference cards',
    'backdrop stand xft photo video studio adjustable backdrop stand for parties wedding photography advertising display note balloons colorful decorations are not included meaningful family gathering cheerful and memorable birthday party romantic and warm wedding professional photography attractive advertising display and other uses just get  ft backdrop stands adjustable stable backdrop stand loading capacity up to lbkg good flexibility with width ftft  height  ft ft heavy duty spring clamp and backdrop elastic string clip to holds curtains canvas muslin projector screen or seamless paper prevent background slippage photography weight sandbag to stabilize the backdrop support system note this bag comes empty its capacity is about lbub the kit includes  pcs crossbar parts   pcs tripod bracket   pcs spring clamp   pcs backdrop elastic string clip   pcs sand bag   pc carry bag',
    'burts bees baby baby girls mittens noscratch mitts  organic cotton set of  ',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

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

Training Details

Training Dataset

Unnamed Dataset

  • Size: 5,864 training samples
  • Columns: sentence_0, sentence_1, and label
  • Approximate statistics based on the first 1000 samples:
    sentence_0sentence_1label
    typestringstringint
    detailsmin: 5 tokensmean: 144.3 tokensmax: 512 tokensmin: 5 tokensmean: 143.08 tokensmax: 512 tokens0: ~87.50%1: ~12.50%
  • Samples:
    sentence_0sentence_1label
    philips norelco shaver rechargeable electric shaver with popup trimmer s comfortcut blades get a clean shave thats comfortable on your skin rounded blade caps shield selfsharpening blades to gently cut hair just above skin level and help the shaver glide smoothly over your skin experience a convenient clean shave with heads that flex and float in directions the head adjusts to the curves of your face ensuring smooth contact with your skin without a lot of pressure d flex heads follow your faces contours for a clean shave popup trimmer for mustache and sideburns finish your look with the builtin trimmer ideal for maintaining your mustache and trimming your sideburns onetouch open for easy cleaning experience a convenient clean shave with heads that flex and float in directions the head adjusts to the curves of your face ensuring smooth contact with your skin without a lot of pressure minutes of cordless shaving from an hour charge thats about shaves or plug it in for instant continuous powerphilips norelco shaver rechargeable electric shaver with popup trimmer s comfortcut blades get a clean shave thats comfortable on your skin rounded blade caps shield selfsharpening blades to gently cut hair just above skin level and help the shaver glide smoothly over your skin experience a convenient clean shave with heads that flex and float in directions the head adjusts to the curves of your face ensuring smooth contact with your skin without a lot of pressure d flex heads follow your faces contours for a clean shave popup trimmer for mustache and sideburns finish your look with the builtin trimmer ideal for maintaining your mustache and trimming your sideburns onetouch open for easy cleaning experience a convenient clean shave with heads that flex and float in directions the head adjusts to the curves of your face ensuring smooth contact with your skin without a lot of pressure minutes of cordless shaving from an hour charge thats about shaves or plug it in for instant continuous power1
    speedo girls swimsuit one piece thin straps lace tulle flower girl long dress for wedding oneck princess dresses long sleeve pageant party gown 0
    pyrex blue cup rectangular plastic cover pc pack original genuine pyrex made in the usa genuine pyrex replacement lid nonporous surface does not absorb food odors flavors or stains refrigerator microwave and toprack dishwasher safe will not fit anchor hocking products included pyrex pc blue cup rectangle plastic lids lids only containers not includedkpywzer vintage leather sling bag bags for men women backpack shoulder messenger crossbody outdoor travel hiking camping tactical chest pack daypack brown 0
  • Loss: ContrastiveTensionLoss

Training Hyperparameters

Non-Default Hyperparameters

  • num_train_epochs: 5
  • multi_dataset_batch_sampler: round_robin

All Hyperparameters

  • overwrite_output_dir: False
  • do_predict: False
  • eval_strategy: no
  • prediction_loss_only: True
  • per_device_train_batch_size: 8
  • 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
  • learning_rate: 5e-05
  • weight_decay: 0.0
  • adam_beta1: 0.9
  • adam_beta2: 0.999
  • adam_epsilon: 1e-08
  • max_grad_norm: 1
  • num_train_epochs: 5
  • max_steps: -1
  • lr_scheduler_type: linear
  • lr_scheduler_kwargs: {}
  • warmup_ratio: 0.0
  • 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: 42
  • data_seed: None
  • jit_mode_eval: False
  • use_ipex: False
  • bf16: False
  • 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: 0
  • dataloader_prefetch_factor: None
  • past_index: -1
  • disable_tqdm: False
  • remove_unused_columns: True
  • label_names: None
  • load_best_model_at_end: False
  • 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: False
  • hub_always_push: False
  • gradient_checkpointing: False
  • gradient_checkpointing_kwargs: None
  • include_inputs_for_metrics: False
  • 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
  • batch_sampler: batch_sampler
  • multi_dataset_batch_sampler: round_robin

Training Logs

EpochStepTraining Loss
0.68215007.4403
1.364310004.8536
2.046415003.8646
2.728520003.8877
3.410625003.8789
4.092830003.8052
4.774935003.8385

Framework Versions

  • Python: 3.10.12
  • Sentence Transformers: 3.0.1
  • Transformers: 4.41.2
  • PyTorch: 2.3.0+cu121
  • Accelerate: 0.31.0
  • Datasets: 2.20.0
  • Tokenizers: 0.19.1

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",
}

ContrastiveTensionLoss

@inproceedings{carlsson2021semantic,
    title={Semantic Re-tuning with Contrastive Tension},
    author={Fredrik Carlsson and Amaru Cuba Gyllensten and Evangelia Gogoulou and Erik Ylip{"a}{"a} Hellqvist and Magnus Sahlgren},
    booktitle={International Conference on Learning Representations},
    year={2021},
    url={https://openreview.net/forum?id=Ov_sMNau-PF}
}

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
dc4821e

0 моделей