giga-embeddings-0826-3b-gguf
ai-babai基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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giga-embeddings-0826-480m-gguf
ai-babai基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗FRIDA
ai-foreverFRIDA is a full-scale finetuned general text embedding model inspired by denoising architecture based on T5. The model is based on the encoder part of FRED-T5 model and continues research of text embedding models (ruMTEB, ru-en-RoSBERTa). It has been pre-trained on a Russian-English dataset and fine-tuned for improved performance on the target task. For more model details please refer to our article (RU). The model's results are presented on the MTEB and rusBEIR leaderboards. The model can be used as is with prefixes. It is recommended to use CLS pooling. The choice of prefix and pooling depends on the task.
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View details ↗ru-en-RoSBERTa
ai-foreverThe ru-en-RoSBERTa is a general text embedding model for Russian. The model is based on ruRoBERTa and fine-tuned with ~4M pairs of supervised, synthetic and unsupervised data in Russian and English. Tokenizer supports some English tokens from RoBERTa tokenizer. For more model details please refer to our article. The model can be used as is with prefixes. It is recommended to use CLS pooling. The choice of prefix and pooling depends on the task.
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View details ↗sbert_large_mt_nlu_ru
ai-foreverFor better quality, use mean token embeddings. You can use the model directly from the model repository to compute sentence embeddings: + SberDevices Team. + Aleksandr Abramov: HF profile, Github, Kaggle Competitions Master; + Denis Antykhov: Github;
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View details ↗sbert_large_nlu_ru
ai-foreverThe model is described in this article For better quality, use mean token embeddings. You can use the model directly from the model repository to compute sentence embeddings: + SberDevices Team. + Aleksandr Abramov: HF profile, Github, Kaggle Competitions Master; + Denis Antykhov: Github; + Ibragim Badertdinov: Github
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View details ↗PatentSBERTa
AI-Growth-LabThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. Using this model becomes easy when you have sentence-transformers installed: Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
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View details ↗Giga-Embeddings-instruct
ai-sage- Base Decoder-only LLM: GigaChat-3b - Pooling Type: Latent-Attention - Embedding Dimension: 2048 Для получения более подробной информации о технических деталях, пожалуйста, обратитесь к нашей статье. Для достижения более точных результатов при работе с эмбеддингами, особенно в задачах поиска и извлечения информации (retrieval), рекомендуется добавлять инструкцию на естественном языке перед текстовым запросом (query). Это помогает модели лучше понять контекст и цель запроса, что положительно сказывается на качестве результатов. Важно отметить, что инструкцию нужно добавлять только перед запросом, а не перед документом.
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Следующая итерация серии Giga-Embeddings. Модель текстовых эмбеддингов на основе архитектуры Qwen3, адаптированная под **двунаправленное (encoder-style) внимание** и обученная с контрастивной функцией потерь (InfoNCE). Модель строит плотные эмбеддинги предложений/абзацев для задач поиска (retrieval), семантического сравнения, классификации и кластеризации, показывая высокое качество на русском и английском языках. Модель обучалась с **mean pooling + L2-нормализацией**. Использование CLS/last-token пулинга даст неверные результаты. Если вы используете `transformers` напрямую, необходимо самостоятельно усреднить (mean-pool) по не-паддинговым токенам и затем применить L2-нормализацию (см. пример ниже). В примерах для `sentence-transformers` и vLLM это делается автоматически. Сравнивайте эмбеддинги через косинусную близость (скалярное произведение нормированных векторов). Модель обучалась в **инструктивном стиле**: для retrieval и других асимметричных задач необходимо добавлять инструкцию к **запросу** (query), а документы кодируются как есть, без инструкции. Формат:
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View details ↗Следующая итерация серии Giga-Embeddings. Модель текстовых эмбеддингов на основе архитектуры Qwen3, адаптированная под **двунаправленное (encoder-style) внимание** и обученная с контрастивной функцией потерь (InfoNCE). Модель строит плотные эмбеддинги предложений/абзацев для задач поиска (retrieval), семантического сравнения, классификации и кластеризации, показывая высокое качество на русском и английском языках. Модель обучалась с **mean pooling + L2-нормализацией**. Использование CLS/last-token пулинга даст неверные результаты. Если вы используете `transformers` напрямую, необходимо самостоятельно усреднить (mean-pool) по не-паддинговым токенам и затем применить L2-нормализацию (см. пример ниже). В примерах для `sentence-transformers` и vLLM это делается автоматически. Сравнивайте эмбеддинги через косинусную близость (скалярное произведение нормированных векторов). Модель обучалась в **инструктивном стиле**: для retrieval и других асимметричных задач необходимо добавлять инструкцию к **запросу** (query), а документы кодируются как есть, без инструкции. Формат:
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View details ↗Prime_690M
AI4Protein基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗This is a fine-tuned version of `paraphrase-multilingual-mpnet-base-v2` from sentence-transformers model with Semantic Textual Similarity Benchmark extended to 15 languages: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering, semantic search and measuring the similarity between two sentences. This model is fine-tuned version of `paraphrase-multilingual-mpnet-base-v2` for semantic textual similarity with multilingual data. The dataset used for this fine-tuning is STSb extended to 15 languages with Google Translator. For mantaining data quality the sentence pairs with a confidence value below 0.7 were dropped. The extended dataset is available at GitHub. The languages included in the extended version are: ar, cs, de, en, es, fr, hi, it, ja, nl, pl, pt, ru, tr, zh-CN, zh-TW. The pooling operation used to condense the word embeddings into a sentence embedding is mean pooling (more info below). Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
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View details ↗star
AIDA-UPM基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Vietnamese_Embedding
AITeamVNVietnamese_Embedding is an embedding model fine-tuned from the BGE-M3 model (https://huggingface.co/BAAI/bge-m3) to enhance retrieval capabilities for Vietnamese. Although the score on the legal domain drops a bit on Vietnamese_Embedding_v2, since this phase data is much larger, it is very good for other domains. You can reproduce the evaluation result by running code python evaluation_model.py (data downloaded from Kaggle).
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View details ↗Vietnamese_Embedding_v2
AITeamVNVietnamese_Embedding_v2 is an embedding model fine-tuned from the BGE-M3 model (https://huggingface.co/BAAI/bge-reranker-v2-m3) to enhance retrieval capabilities for Vietnamese. Although the score on the legal domain drops a bit on Vietnamese_Embedding (Phase 2), since this phase data is much larger, it is good for other domains. You can reproduce the evaluation result by running code python evaluation_model.py (data downloaded from Kaggle).
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View details ↗Vietnamese_Reranker
AITeamVNVietnamese_Reranker is an reranker model fine-tuned from the bge-reranker-v2-m3 model (https://huggingface.co/BAAI/bge-reranker-v2-m3) to enhance retrieval capabilities for Vietnamese. Although the score on the legal domain drops a bit on Vietnamese_Embedding_v2 (Phase 2), since this phase data is much larger, it is good for other domains.
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View details ↗NoRefER
aixplain基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Arabic-SBERT-100K
akhooliThis is a sentence-transformers model finetuned from aubmindlab/bert-base-arabertv02. 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. This model is trained on 100K samples filtered from the akhooli/arabic-triplets-1m-curated-sims-len dataset with 75K training and 25K validation. Trained for 5 epochs, with final training loss of 0.133 (using MatryoshkaLoss).
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View details ↗bhasha-embed-v0
AkshitaS基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗bge-m3-italian
albertobarnabo基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗antiberta2-cssp
alchemab基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Qwen3-Embedding-8B-FP8-DYNAMIC
alexliap基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Qwen3-Embedding-8B-NVFP4
alexliapThis is a quantized version of Qwen/Qwen3-Embedding-8B using **NVFP4** (NVIDIA FP4) quantization via llmcompressor. The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. Building upon the dense foundational models of the Qwen3 series, it provides a comprehensive range of text embeddings and reranking models in various sizes (0.6B, 4B, and 8B). This series inherits the exceptional multilingual capabilities, long-text understanding, and reasoning skills of its foundational model. The Qwen3 Embedding series represents significant advancements in multiple text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bitext mining. **Exceptional Versatility**: The embedding model has achieved state-of-the-art performance across a wide range of downstream application evaluations. The 8B size embedding model ranks **No.1** in the MTEB multilingual leaderboard (as of June 5, 2025, score **70.58**), while the reranking model excels in various text retrieval scenarios.
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