/ Модель

Характеристики

Контекст
512 токенов
Параметры
40 млн
Лицензия
Apache 2.0
Архитектура
bert · dense
Базовая модель
bge-small-en
Библиотека
sentence-transformers
Опубликована
22 авг 2024

bge-small-matryoshka-fine-tuned

ЭмбеддингиТекст → embedding10 профессий

Компактная англоязычная BERT-модель, создающая текстовые эмбеддинги для семантического поиска и сравнения предложений и абзацев.

100скачиваний за 30 дней0лайковвеса обновлены22 авг 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

Rajatrc1705 дообучил англоязычную модель эмбеддингов BAAI/bge-small-en на датасете sentence-transformers/hotpotqa. Это Sentence Transformer на архитектуре BERT с 40 млн параметров, который преобразует предложения и абзацы в плотные векторы размерностью 384; для сравнения текстов используется косинусное сходство. Максимальная длина входа — 512 токенов.

Модель предназначена для семантического поиска и оценки сходства текстов, а также может использоваться для поиска перефразировок, кластеризации и классификации текстов. Обучение на HotpotQA ориентирует её на сопоставление вопросов с релевантными фрагментами текста. Доступны варианты представления размерностью 256, 128 и 64 с близкими результатами в приведённых в карточке тестах TripletEvaluator.

Модель принимает текст и выдаёт векторы признаков. Она рассчитана на английский язык и не заявлена как мультиязычная. Распространяется по лицензии Apache-2.0.

Сценарии применения

  • семантический поиск по англоязычным документам
  • поиск фрагментов, релевантных вопросу
  • сравнение сходства предложений
  • поиск перефразировок
  • кластеризация текстов по смыслу
  • классификация текстов на основе эмбеддингов

Ограничения

Поддерживает английский язык и ограничивает вход последовательностями до 512 токенов. Качество за пределами сценариев, близких к обучающему датасету HotpotQA, в карточке не оценено.

Для каких профессий подходит10

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

BGE-base-en-v1.5-Hotpotqa

This is a sentence-transformers model finetuned from BAAI/bge-small-en on the sentence-transformers/hotpotqa 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
  • Base model: BAAI/bge-small-en
  • Maximum Sequence Length: 512 tokens
  • Output Dimensionality: 384 tokens
  • Similarity Function: Cosine Similarity
  • Training Dataset:
  • Language: en
  • License: apache-2.0

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': 384, '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("sentence_transformers_model_id")
# Run inference
sentences = [
    'Red Velvet is a 2012 play by Lolita Chakrabarti, dealing with the biography of a 19th century actor born in which year ?',
    'Red Velvet (play) Red Velvet is a 2012 play by Lolita Chakrabarti, dealing with the biography of the 19th century actor Ira Aldridge and his taking the role of "Othello". It premiered at the Tricycle Theatre (directed by its new artistic director Indhu Rubasingham) from 11 October to 24 November 2012, with Aldridge played by Adrian Lester.',
    "Herbert Campbell Herbert Campbell (22 December 1844 – 19 July 1904) born Herbert Edward Story was an English comedian and actor who appeared in music hall, Victorian burlesques and musical comedies during the Victorian era. He was famous for starring, for forty years, in the Theatre Royal, Drury Lane's annual Christmas pantomimes, predominantly as a dame.",
]
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]

Evaluation

Metrics

Triplet

MetricValue
cosine_accuracy0.8699
dot_accuracy0.1301
manhattan_accuracy0.8738
euclidean_accuracy0.8699
max_accuracy0.8738

Triplet

MetricValue
cosine_accuracy0.8682
dot_accuracy0.1319
manhattan_accuracy0.8762
euclidean_accuracy0.8681
max_accuracy0.8762

Triplet

MetricValue
cosine_accuracy0.8663
dot_accuracy0.1387
manhattan_accuracy0.8742
euclidean_accuracy0.8657
max_accuracy0.8742

Triplet

MetricValue
cosine_accuracy0.8635
dot_accuracy0.1509
manhattan_accuracy0.8728
euclidean_accuracy0.8637
max_accuracy0.8728

Training Details

Training Dataset

sentence-transformers/hotpotqa

  • Dataset: sentence-transformers/hotpotqa at f07d3cd
  • Size: 76,064 training samples
  • Columns: anchor, positive, and negative
  • Approximate statistics based on the first 1000 samples:
    anchorpositivenegative
    typestringstringstring
    detailsmin: 8 tokensmean: 24.08 tokensmax: 95 tokensmin: 23 tokensmean: 100.12 tokensmax: 512 tokensmin: 15 tokensmean: 88.02 tokensmax: 512 tokens
  • Samples:
    anchorpositivenegative
    What nationality was the player named MVP in 2017 World Baseball Classic – Pool C ?2017 World Baseball Classic – Pool C Pool C of the First Round of the 2017 World Baseball Classic was held at Marlins Park, Miami, Florida, United States, from March 9 to 12, 2017, between Canada, Colombia, the Dominican Republic, and the United States. Pool C was a round-robin tournament. Each team played the other three teams once, with the top two teams – the Dominican Republic and the United States – advancing to Pool F, one of two second-round pools. Manny Machado of the Dominican Republic was named MVP for the first-round Pool C bracket of the WBC, after batting .357.2017 World Baseball Classic – Qualifier 2 Qualifier 2 of the Qualifying Round of the 2017 World Baseball Classic was held at Estadio B'Air, Mexicali, Mexico from March 17 to 20, 2016.
    Karl Kraepelin specialized in the study of what predatory arachnids?Karl Kraepelin Karl Matthias Friedrich Magnus Kraepelin (14 December 1848 Neustrelitz – 28 June 1915 Hamburg), was a German naturalist who specialised in the study of scorpions, centipedes, spiders and solfugids, and was noted for his monograph ""Scorpiones und Pedipalpi"" (Berlin) in 1899, which was an exhaustive survey of the taxonomy of the Order Scorpiones. From 1889–1914 he was Director of the "Naturhistorisches Museum Hamburg ", which was destroyed during World War II, and worked on myriapods from 1901–1916.Teuthology Teuthology (from Greek "τεῦθος" , "cuttlefish, squid", and -λογία , "-logia") is the study of cephalopods.
    Who directed the 1990 American crime film in which Vito Pick me played a bit part?Vito Picone Vito Picone (born March 20, 1941) is the lead singer of The Elegants, and along with Jimmy Mochella is a remaining original member. He has also played bit parts in "Goodfellas", "Analyze This", and "The Sopranos".The Rookie (1990 film) The Rookie is a 1990 American buddy cop film directed by Clint Eastwood and produced by Howard G. Kazanjian, Steven Siebert and David Valdes. It was written from a screenplay conceived by Boaz Yakin and Scott Spiegel. The film stars Charlie Sheen, Clint Eastwood, Raúl Juliá, Sônia Braga, Lara Flynn Boyle, and Tom Skerritt. Eastwood plays a veteran police officer teamed up with a younger detective played by Sheen ("the rookie"), whose intent is to take down a German crime lord in downtown Los Angeles following months of investigation into an exotic car theft ring.
  • Loss: MatryoshkaLoss with these parameters:
    {
        "loss": "TripletLoss",
        "matryoshka_dims": [
            384,
            256,
            128,
            64
        ],
        "matryoshka_weights": [
            1,
            1,
            1,
            1
        ],
        "n_dims_per_step": -1
    }
    

Evaluation Dataset

sentence-transformers/hotpotqa

  • Dataset: sentence-transformers/hotpotqa at f07d3cd
  • Size: 8,452 evaluation samples
  • Columns: anchor, positive, and negative
  • Approximate statistics based on the first 1000 samples:
    anchorpositivenegative
    typestringstringstring
    detailsmin: 10 tokensmean: 24.53 tokensmax: 114 tokensmin: 19 tokensmean: 103.87 tokensmax: 407 tokensmin: 17 tokensmean: 88.32 tokensmax: 356 tokens
  • Samples:
    anchorpositivenegative
    Which actress, known for her role as Harper Munroe on the MTV comedy series "Happyland", starred alongside Laura Marano, Parker Mack, Michelle Clunie and Kathleen Wilhoite in the film A Sort of Homecoming?A Sort of Homecoming (film) A Sort of Homecoming is an American drama directed by Maria Burton, her fifth feature film. The films stars Katherine McNamara, Laura Marano, Parker Mack, Michelle Clunie and Kathleen Wilhoite. The film premiered March 14, 2015 at the Omaha Film Festival.Nellie Bellflower Nellie Bellflower (born May 1, 1946 in Phoenix, Arizona) is an American actress and voice artist who provided the voice of Princess Ariel in the Ruby-Spears animated television series "Thundarr the Barbarian". She has also been in "The Last Unicorn" (voice), Rankin/Bass "The Return of the King", "Americathon", the miniseries "East of Eden", and guest roles on various TV shows such as "Barnaby Jones", "Barney Miller", "Starsky and Hutch", and "Happy Days" as Fonzie's ex-fiancée Maureen Johnson, a.k.a. "The Lone Stripper", in the Season 2 episode of the series titled "Fonzie's Getting Married" (episode #13). Nellie has been involved in movie production with three projects: "The Girl in Melanie Klein" (2008), "Miss Pettigrew Lives for a Day" (2008) and "Finding Neverland" (2004), for which she was nominated for an Academy Award as Producer for Best Picture. She is married to Michael Mislove.
    Between Pizza Fusion and Pizzeria Venti, which restaurant emphasizes organic ingredients and green building methods?Pizza Fusion Pizza Fusion is a Deerfield Beach, Florida-based pizza restaurant chain. Using mostly organic ingredients and emphasizing green building methods, the restaurants operate under the tagline Saving the Earth, One Pizza at a Time.Pizza Schmizza Pizza Schmizza is an American pizza chain with 23 locations throughout the Portland, Oregon area, and two in southern Oregon. Pizza Schmizza, primarily selling thin crust pizza by-the-slice.
    What company distributed the stop motion spin-off special "The Year Without a Santa Claus," which aired on December 10, 1974?A Miser Brothers' Christmas A Miser Brothers' Christmas is a stop motion spin-off special based on some of the characters from the 1974 Rankin-Bass special "The Year Without a Santa Claus". Distributed by Warner Bros. Animation under their Warner Premiere label (the rights holders of the post-1974 Rankin-Bass library) and Toronto-based Cuppa Coffee Studios, the one-hour special premiered on ABC Family on Saturday, December 13, 2008, during the network's annual The 25 Days of Christmas programming. Mickey Rooney and George S. Irving reprised their respective roles as Santa Claus and Heat Miser at ages 88 and 86. Snow Miser, originally portrayed by Dick Shawn who died in 1987, was voiced by Juan Chioran, while Mrs. Claus, voiced by Shirley Booth in the original, was portrayed by Catherine Disher (because Booth had died in 1992). The movie aimed to emulate the Rankin/Bass animation style. This is the last Christmas special to feature Mickey Rooney as Santa Claus, as he died in 2014, as well as the last time George Irving voiced Heat Miser, as he died in 2016.Holidaze: The Christmas That Almost Didn't Happen Holidaze: The Christmas That Almost Didn't Happen is an American stop-motion animated Christmas television special directed by David H. Brooks, that originally aired in 2006 and produced by BixPix Entertainment, Once Upon a Frog and Madison Street Entertainment. The show's plot has Rusty Reindeer (Fred Savage) the brother of Rudolph the Red Nosed Reindeer joining a support group for depressed holiday icons, and he and the other characters search for the meaning of Christmas and help a young boy (Dylan and Cole Sprouse) to get on Santa's nice list. Rusty's cohorts include Candie, the Easter Bunny (Gladys Knight); Mr. C, the grouchy cherub (Paul Rodriguez); Albert, the Thanksgiving Turkey (Harland Williams); And Trick and Treat (Brenda Song and Emily Osment) the teenage Halloween Ghosts.
  • Loss: MatryoshkaLoss with these parameters:
    {
        "loss": "TripletLoss",
        "matryoshka_dims": [
            384,
            256,
            128,
            64
        ],
        "matryoshka_weights": [
            1,
            1,
            1,
            1
        ],
        "n_dims_per_step": -1
    }
    

Training Hyperparameters

Non-Default Hyperparameters

  • eval_strategy: steps
  • per_device_train_batch_size: 32
  • per_device_eval_batch_size: 32
  • gradient_accumulation_steps: 16
  • learning_rate: 2e-05
  • num_train_epochs: 50
  • lr_scheduler_type: cosine
  • warmup_ratio: 0.1
  • bf16: True
  • tf32: True
  • load_best_model_at_end: True
  • optim: adamw_torch_fused
  • resume_from_checkpoint: bge-small-hotpotwa-matryoshka
  • 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: 32
  • per_device_eval_batch_size: 32
  • per_gpu_train_batch_size: None
  • per_gpu_eval_batch_size: None
  • gradient_accumulation_steps: 16
  • eval_accumulation_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: 50
  • max_steps: -1
  • lr_scheduler_type: cosine
  • 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: 42
  • 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: True
  • 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: 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_fused
  • 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: bge-small-hotpotwa-matryoshka
  • 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: no_duplicates
  • multi_dataset_batch_sampler: proportional

Training Logs

EpochStepTraining Losslossdim_128_cosine_accuracydim_256_cosine_accuracydim_384_cosine_accuracydim_64_cosine_accuracy
0.33665019.575819.39330.95520.96630.96680.9359
0.673110019.457319.09710.95710.96460.96530.9450
1.009715019.140918.40700.93850.94340.94730.9307
1.346220018.643117.32920.91260.91640.91840.9094
1.682825018.228816.87510.90630.90710.91000.9023
2.019430018.042516.69810.90200.90320.90450.8990
2.355935017.945816.61550.90370.90130.90220.8984
2.692540017.852516.55360.89780.89710.89740.8948
3.029045017.752916.51360.89800.89560.89530.8951
3.365650017.670916.48240.89320.89140.89280.8907
3.702155017.534816.46320.88630.88580.88590.8849
4.038760017.419816.46010.88520.88620.88590.8839
4.375365017.367316.44050.88540.88640.88650.8842
4.711870017.260316.43560.88350.88380.88380.8807
5.048475017.180716.44430.88500.88640.88590.8838
5.384980017.162916.42020.88480.88620.88670.8842
5.721585017.074716.41620.88540.88750.88690.8837
6.058190017.016116.41920.88520.88630.88560.8856
6.394695017.014616.40330.88490.88540.88560.8844
6.7312100016.939316.40530.88290.88390.88480.8835
7.0677105016.89916.41620.88260.88290.88330.8818
7.4043110016.911216.40510.88290.88350.88330.8820
7.7408115016.850816.40440.88220.88250.88300.8820
8.0774120016.810416.40630.88160.88160.88140.8817
8.4140125016.821216.40400.88350.88220.88220.8820
8.7505130016.774316.39340.88220.88240.88170.8810
9.0871135016.738316.39630.88100.88200.88070.8800
9.4236140016.74316.40670.88190.88220.88190.8798
9.7602145016.704716.39590.88040.88100.88100.8797
10.0968150016.678216.39860.87880.87910.87960.8784
10.4333155016.670816.40160.87940.87920.87970.8791
10.7699160016.648516.39630.87900.88010.87910.8781
11.1064165016.620516.40120.87790.87870.87930.8771
11.4430170016.609516.41310.87860.87900.87940.8791
11.7796175016.589116.40700.88070.88050.88100.8801
12.1161180016.561916.39630.87940.88000.87970.8780
12.4527185016.546716.39910.87960.88060.88040.8790
12.7892190016.539816.39700.87920.87980.88010.8788
13.1258195016.504716.39640.87960.88040.88040.8788
13.4623200016.498516.40250.87930.87980.88070.8790
13.7989205016.485216.41070.88010.88100.88000.8793
14.1355210016.452616.39290.87970.88010.88090.8779
14.4720215016.434316.40750.87880.87910.87970.8774
14.8086220016.424416.40270.88040.88190.88200.8809
15.1451225016.394716.41020.87910.87920.88030.8773
15.4817230016.382716.40420.88040.88130.88130.8781
15.8183235016.371916.40030.88010.88180.88200.8791
16.1548240016.340316.41320.87810.87910.87990.8767
16.4914245016.335716.41490.88040.88090.88070.8792
16.8279250016.320316.40810.88040.88140.88160.8791
17.1645255016.298616.41390.87980.88000.88200.8791
17.5011260016.292316.40620.87860.87920.87990.8768
17.8376265016.264916.41060.88000.88070.88140.8787
18.1742270016.250516.41880.87860.87930.88030.8771
18.5107275016.22616.41490.87710.87810.87800.8766
18.8473280016.210616.42300.87800.87990.87910.8767
19.1838285016.205216.43510.87700.87770.87850.8745
19.5204290016.18616.43310.87770.87930.87920.8762
19.8570295016.149616.43770.87740.87810.87800.8771
20.1935300016.15116.44070.87660.87800.87800.8751
20.5301305016.108116.44260.87590.87750.87740.8749
20.8666310016.086416.44120.87740.87810.87870.8746
21.2032315016.093416.45470.87680.87830.87940.8746
21.5398320016.038216.45890.87420.87520.87660.8723
21.8763325016.027916.46680.87520.87660.87730.8728
22.2129330016.032716.47370.87420.87680.87730.8727
22.5494335015.97916.46860.87400.87710.87710.8722
22.8860340015.962216.47360.87430.87600.87650.8721
23.2225345015.988116.48020.87430.87570.87550.8723
23.5591350015.948216.48210.87250.87610.87610.8710
23.8957355015.922816.49960.87260.87480.87510.8709
24.2322360015.941816.49730.87090.87280.87340.8699
24.5688365015.89616.49850.86960.87160.87270.8686
24.9053370015.878816.51720.86910.87150.87170.8662
25.2419375015.914716.50620.86770.87060.87120.8662
25.5785380015.85716.50580.86830.87170.87320.8663
25.9150385015.829116.52070.86740.87020.87060.8644
26.2516390015.880216.52330.86780.86970.87140.8664
26.5881395015.84616.51700.86860.87130.87170.8655
26.9247400015.801216.53360.86630.86820.86990.8635

Framework Versions

  • Python: 3.10.10
  • Sentence Transformers: 3.0.1
  • Transformers: 4.41.2
  • PyTorch: 2.1.2+cu121
  • Accelerate: 0.33.0
  • Datasets: 2.19.1
  • 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",
}

MatryoshkaLoss

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning}, 
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

TripletLoss

@misc{hermans2017defense,
    title={In Defense of the Triplet Loss for Person Re-Identification}, 
    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
    year={2017},
    eprint={1703.07737},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

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
1663fca

0 моделей