/ Модель

Характеристики

Контекст
4 096 токенов
Лицензия
Apache 2.0
Архитектура
t5gemma2_encoder · dense
Библиотека
sentence-transformers
Опубликована
26 июл 2026

t5gemma-2-1b-multimodal-embedding-indo-v6

ЭмбеддингиТекст → embedding9 профессий

Мультимодальная модель эмбеддингов для семантического поиска и сравнения текстов и изображений на индонезийском и английском языках.

32скачиваний за 30 дней0лайковвеса обновлены26 июл 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

Это мультимодальная модель Sentence Transformers, дообученная daruokta на базе daruokta/t5gemma-2-1b-multimodal-embedding-indo-v6 для поиска и сопоставления предложений. Она кодирует текст и изображения в плотные векторы размерностью 1152; для сравнения векторов используется косинусное сходство. Максимальная длина последовательности — 4096 токенов. Модель обучалась на задачах поиска, семантического сходства, вывода и классификации и подходит для семантического поиска и сравнения текстов.

В карточке указаны индонезийский и английский языки. Лицензия — Apache-2.0. В опубликованных оценках приведены результаты для семантического сходства, поиска и бинарной классификации, однако они относятся к указанным наборам оценки и не гарантируют аналогичного качества на других данных. Карточка не описывает отдельную генерацию текста или извлечение признаков изображения помимо заявленной поддержки текстовых и визуальных входов.

Сценарии применения

  • семантический поиск по текстовым коллекциям
  • поиск релевантных документов по запросу
  • сравнение смысловой близости предложений
  • сопоставление текстов для классификации
  • поиск по тексту и изображениям

Ограничения

Заявленные оценки относятся к конкретным наборам семантического сходства, поиска и бинарной классификации; качество на других данных может отличаться. Карточка не приводит отдельных показателей качества для обработки изображений.

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

T5Gemma2 1B Embedding Stage 2

This is a sentence-transformers model finetuned from daruokta/t5gemma-2-1b-multimodal-embedding-indo-v6 on the retrieval, sts, nli and classification datasets. It maps sentences & paragraphs to a 1152-dimensional dense vector space and can be used for retrieval.

Model Details

Model Description

  • Model Type: Sentence Transformer
  • Base model: daruokta/t5gemma-2-1b-multimodal-embedding-indo-v6
  • Maximum Sequence Length: 4096 tokens
  • Output Dimensionality: 1152 dimensions
  • Similarity Function: Cosine Similarity
  • Supported Modalities: Text, Image
  • Training Datasets:
    • retrieval
    • sts
    • nli
    • classification
  • Languages: id, en
  • License: apache-2.0

Model Sources

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}, 'image': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'T5Gemma2Encoder'})
  (1): Pooling({'embedding_dimension': 1152, 'pooling_mode': 'mean', '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("sentence_transformers_model_id")
# Run inference
queries = [
    'Apa perbedaan antara 打关系 dan 走关系?',
]
documents = [
    '"Apa perbedaan antara "" dan "";"" serta "",""?',
    'Alat apa yang digunakan untuk membuat video ini?',
    'Jika teroris memasang bahan peledak palsu pada Anda, membungkam Anda, menempelkan pelatuk ke tangan Anda, dan meninggalkan Anda di tengah kota, bagaimana Anda bisa bertahan hidup?',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 1152] [3, 1152]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[0.3385, 0.0576, 0.1161]])

Evaluation

Metrics

Semantic Similarity

MetricValue
pearson_cosine0.8263
spearman_cosine0.939
pearson_euclidean0.8095
spearman_euclidean0.8568
pearson_manhattan0.8129
spearman_manhattan0.8543
pearson_dot-0.0296
spearman_dot0.0199
pearson_max0.8263
spearman_max0.939

Information Retrieval

MetricValue
cosine_accuracy@10.964
cosine_accuracy@30.999
cosine_accuracy@51.0
cosine_accuracy@101.0
cosine_precision@10.964
cosine_precision@30.333
cosine_precision@50.2
cosine_precision@100.1
cosine_recall@10.964
cosine_recall@30.999
cosine_recall@51.0
cosine_recall@101.0
cosine_ndcg@100.9857
cosine_mrr@100.9808
cosine_map@1000.9808

Binary Classification

MetricValue
cosine_accuracy0.79
cosine_accuracy_threshold0.87
cosine_f10.6973
cosine_f1_threshold0.8566
cosine_precision0.6898
cosine_recall0.7049
cosine_ap0.7627
cosine_mcc0.5196

Training Details

Training Datasets

retrieval

  • Dataset: retrieval
  • Size: 43,389 training samples
  • Columns: anchor and positive
  • Approximate statistics based on the first 100 samples:
    anchorpositive
    typestringstring
    modalitytexttext
    detailsmin: 4 tokensmean: 10.51 tokensmax: 20 tokensmin: 15 tokensmean: 131.98 tokensmax: 307 tokens
  • Samples:
    anchorpositive
    dimanakah letah Universitas Indonesia?Universitas IndonesiaUniversitas Indonesia disingkat sebagai UI, adalah sebuah perguruan tinggi di Indonesia. Kampus utamanya terletak di bagian Utara dari Depok, Jawa Barat tepat di perbatasan antara Depok dengan wilayah Jakarta Selatan, dan kampus utama lainnya terdapat di daerah Salemba di Jakarta Pusat. Universitas Indonesia adalah kampus modern, komprehensif, terbuka, multibudaya, dan humanis yang mencakup disiplin ilmu yang luas. Telah menghasilkan lebih dari 400.000 alumni. Secara umum dianggap sebagai salah satu dari tiga perguruan tinggi papan atas di Indonesia bersama dengan Universitas Gadjah Mada dan Institut Teknologi Bandung. Cikal-bakal terbentuknya Universitas Indonesia adalah ketika pemerintah kolonial Belanda mendirikan sebuah sekolah yang bertujuan untuk menghasilkan asisten dokter tambahan yang memegang sertifikat untuk melakukan perawatan-perawatan tingkat dasar serta mendapatkan gelar Dokter Jawa ("Javanese doctor"). Secara resmi Universitas Indonesia memulai keg...
    perintah terbaru pemerintah, upah harian karyawan, gaji di KeralaKomisi Gaji kemungkinan besar akan merekomendasikan 16.000 sebagai Upah Minimum dan 1 juta sebagai Maksimum untuk Pegawai Pemerintah di Kerala. oleh admin Tambahkan Komentar.
    kapankah Indonesia merdeka?Pengakuan tanggal kemerdekaan Indonesia oleh Belanda atau Pengakuan Kedaulatan Indonesia adalah peristiwa di mana Belanda akhirnya mengakui bahwa kemerdekaan Indonesia adalah tanggal 17 Agustus 1945 sesuai dengan proklamasi kemerdekaan Indonesia, bukan tanggal 27 Desember 1949 saat soevereiniteitsoverdracht (penyerahan kedaulatan) ditandatangani di Istana Dam, Amsterdam.
  • Loss: main.WeightedLoss

sts

  • Dataset: sts
  • Size: 19,464 training samples
  • Columns: sentence1, sentence2, and score
  • Approximate statistics based on the first 100 samples:
    sentence1sentence2score
    typestringstringfloat
    modalitytexttext
    detailsmin: 6 tokensmean: 16.54 tokensmax: 44 tokensmin: 5 tokensmean: 16.4 tokensmax: 62 tokensmin: 0.0mean: 0.49max: 1.0
  • Samples:
    sentence1sentence2score
    Apakah benar bahwa jika Anda tidak menggunakannya, Anda akan kehilangan itu?Apakah benar bahwa jika Anda tidak menggunakannya, Anda akan kehilangan itu?1.0
    bertindak, membentuk, atau berkembang sesuai dengan rencana atau idealmembentuk atau memengaruhi; memberikan arahan.0.45
    "Saya ingin mengetahui detail tentang Delta Charting Group, sebuah perusahaan di Tucson, Arizona, termasuk ulasan perdagangan mereka. Apakah ada yang bisa menjelaskan tentang perusahaan ini kepada saya?"Perusahaan penyedia layanan taksi dari Pune ke Mahabaleshwar mana yang terbaik di Pune?0.0
  • Loss: main.WeightedLoss

nli

  • Dataset: nli
  • Size: 22,022 training samples
  • Columns: premise, hypothesis, and label
  • Approximate statistics based on the first 100 samples:
    premisehypothesislabel
    typestringstringint
    modalitytexttext
    detailsmin: 5 tokensmean: 28.0 tokensmax: 123 tokensmin: 5 tokensmean: 13.48 tokensmax: 27 tokens0: ~40.38%1: ~31.73%2: ~27.88%
  • Samples:
    premisehypothesislabel
    Para pejuang dari gerakan Perlawanan bawah tanah itu gagah berani, tetapi mereka hanyalah minoritas kecil, beberapa di antaranya adalah patriot konservatif seperti de Gaulle, sebagian besar adalah sosialis dan komunis, serta sejumlah kecil pengungsi dari Eropa Timur.Hanya sejumlah kecil orang yang berpartisipasi dalam gerakan perlawanan.0
    Karena prinsip "wanita dan anak-anak harus dilindungi terlebih dahulu."Karena aturan lama "wanita dan anak-anak harus diprioritaskan"0
    Pedang melengkung tergantung di pinggang mereka, dan busur terletak di punggung mereka.Mereka membawa busur di punggung mereka.0
  • Loss: main.WeightedLoss

classification

  • Dataset: classification
  • Size: 22,000 training samples
  • Columns: sentence1, sentence2, and label
  • Approximate statistics based on the first 100 samples:
    sentence1sentence2label
    typestringstringint
    modalitytexttext
    detailsmin: 4 tokensmean: 39.38 tokensmax: 92 tokensmin: 3 tokensmean: 38.79 tokensmax: 91 tokens0: ~37.50%1: ~62.50%
  • Samples:
    sentence1sentence2label
    restoran ini menawarkan banyak sekali menu makanan dan minuman . dari makanan sunda , orang cina makanan , hingga makanan barat . harga nya pun tidak terlalu mahal . saya menyantap nasi liwet jambal roti , rasanya gurih dan enak .tempat steak yang legendaris dan terkenal . no wonder , karena rasa steak nya enak , dengan porsi yang cukup besar . harga nya juga sepadan dengan kualitas nya . steak nya cukup empuk dan kentang nya saya suka1
    [USERNAME] [USERNAME] [USERNAME] Denger-denger dari Pusat Hoax Nasional-nya [USERNAME] , ternyata FH gak muat dimasukin ke karung beras, biarpun karung jumbo ... Karena [USERNAME] jauh lebih besar dari partainya, [USERNAME] ...Yang menyedihkan lagi ada yang menganggap Via Vallen itu nggak merasa di lecehkan amat, malah mau naik pamor demi mencapai Keviralan dan itu diungkapkan oleh seorang W.A.N.I.T.A Addeh, udah jadi korban, sesama Wanita pula. Padahal normalnya harus saling dukung, saling empati [URL]1
    berada di sudut perempatan jalan naripan , bakmi naripan ini sangat terkenal dan selalu ramai oleh pengunjung . terlebih saat akhir pekan atau musim liburan , restoran ini tidak pernah sepi . menu pilihan paling favorit tentu saja bakmi yamin dan bakmi biasa . rasa kaldu nya pada kuah nya sungguh kental dan nikmat serta hangat .kasihan , padahal kalau tidak ada polling denny siregar cebong girang sekali , sayang , dipupus oleh kalah telak nya jokowi di polling denny .0
  • Loss: main.WeightedLoss

Training Hyperparameters

Non-Default Hyperparameters

  • per_device_train_batch_size: 32
  • num_train_epochs: 5
  • learning_rate: 2e-05
  • bf16: True
  • gradient_checkpointing: True

All Hyperparameters

  • per_device_train_batch_size: 32
  • num_train_epochs: 5
  • max_steps: -1
  • learning_rate: 2e-05
  • lr_scheduler_type: linear
  • lr_scheduler_kwargs: None
  • warmup_steps: 0
  • optim: adamw_torch_fused
  • optim_args: None
  • weight_decay: 0.0
  • adam_beta1: 0.9
  • adam_beta2: 0.999
  • adam_epsilon: 1e-08
  • optim_target_modules: None
  • gradient_accumulation_steps: 1
  • 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: None
  • gradient_checkpointing: True
  • 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: True
  • include_num_input_tokens_seen: no
  • log_level: passive
  • log_level_replica: warning
  • disable_tqdm: False
  • project: huggingface
  • trackio_space_id: None
  • trackio_bucket_id: None
  • trackio_static_space_id: None
  • per_device_eval_batch_size: 8
  • 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: False
  • hub_private_repo: None
  • hub_model_id: None
  • hub_strategy: every_save
  • hub_always_push: False
  • hub_revision: None
  • load_best_model_at_end: False
  • ignore_data_skip: False
  • restore_callback_states_from_checkpoint: False
  • full_determinism: False
  • seed: 42
  • data_seed: None
  • 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: 0
  • dataloader_pin_memory: True
  • dataloader_persistent_workers: False
  • dataloader_prefetch_factor: None
  • 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
  • warmup_ratio: None
  • local_rank: -1
  • prompts: None
  • batch_sampler: batch_sampler
  • multi_dataset_batch_sampler: proportional
  • router_mapping: {}
  • learning_rate_mapping: {}

Training Logs

EpochStepTraining Losseval-sts_spearman_maxeval-retrieval_cosine_ndcg@10eval-nli-binary_cosine_ap
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Training Time

  • Training: 2.3 hours
  • Evaluation: 7.9 minutes
  • Total: 2.4 hours

Framework Versions

  • Python: 3.13.11
  • Sentence Transformers: 5.6.1
  • Transformers: 5.14.1
  • PyTorch: 2.13.0+cu130
  • Accelerate: 1.14.0
  • Datasets: 5.0.0
  • Tokenizers: 0.22.2

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

Нельзя запустить

Модель не помещается в память одной GPU (80 ГБ).

Класс архитектуры
T5Gemma2Encoder
Ревизия весов
907c253

0 моделей