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View details ↗DISCOVER & COMPARE
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Embeddings are the pooled last hidden state; the head is unused and left unquantised. Cosine similarity is scale-sensitive, so compare rankings rather than raw distances when validating this checkpoint. `WikipediaRetrievalMultilingual` from MTEB — retrieval over Wikipedia in 16 languages, scored by `ndcg_at_10`. Serving Qwen/Qwen3-Embedding-4B in bf16 leaves little room for KV cache on a small GPU: the weights take what the cache needs, and the context length has to be cut until it fits. Halving the weights gives that memory back — the same card serves a longer context without any other change.
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View details ↗Anita
DeepMount00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 ↗Ita-Search
DeepMount00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 ↗rubert-base-cased
DeepPavlovRuBERT \(Russian, cased, 12‑layer, 768‑hidden, 12‑heads, 180M parameters\) was trained on the Russian part of Wikipedia and news data. We used this training data to build a vocabulary of Russian subtokens and took a multilingual version of BERT‑base as an initialization for RuBERT\[1\]. \[1\]: Kuratov, Y., Arkhipov, M. \(2019\). Adaptation of Deep Bidirectional Multilingual Transformers for Russian Language. arXiv preprint arXiv:1905.07213.
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View details ↗rubert-base-cased-conversational
DeepPavlov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 ↗rubert-base-cased-sentence
DeepPavlovSentence RuBERT \(Russian, cased, 12-layer, 768-hidden, 12-heads, 180M parameters\) is a representation‑based sentence encoder for Russian. It is initialized with RuBERT and fine‑tuned on SNLI\[1\] google-translated to russian and on russian part of XNLI dev set\[2\]. Sentence representations are mean pooled token embeddings in the same manner as in Sentence‑BERT\[3\]. \[1\]: S. R. Bowman, G. Angeli, C. Potts, and C. D. Manning. \(2015\) A large annotated corpus for learning natural language inference. arXiv preprint arXiv:1508.05326 \[2\]: Williams A., Bowman S. \(2018\) XNLI: Evaluating Cross-lingual Sentence Representations. arXiv preprint arXiv:1809.05053
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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 ↗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 ↗CoCoSoDa
DeepSoftwareAnalytics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 ↗deberta-v1-base
deepvk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 ↗USER-base
deepvk**U**niversal **S**entence **E**ncoder for **R**ussian (USER) is a sentence-transformer model for extracting embeddings exclusively for Russian language. It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. This model is initialized from `deepvk/deberta-v1-base` and trained to work exclusively with the Russian language. Its quality on other languages was not evaluated. Using this model becomes easy when you have `sentence-transformers` installed:
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View details ↗USER-bge-m3
deepvk**U**niversal **S**entence **E**ncoder for **R**ussian (USER) is a sentence-transformer model for extracting embeddings exclusively for Russian language. It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search. This model is initialized from `TatonkaHF/bge-m3_en_ru` which is shrinked version of `baai/bge-m3` model and trained to work mainly with the Russian language. Its quality on other languages was not evaluated. Using this model becomes easy when you have `sentence-transformers` installed:
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View details ↗USER2-small
deepvk**USER2** is a new generation of the **U**niversal **S**entence **E**ncoder for **R**ussian, designed for sentence representation with long-context support of up to 8,192 tokens. The models are built on top of the `RuModernBERT` encoders and are fine-tuned for retrieval and semantic tasks. They also support Matryoshka Representation Learning (MRL) — a technique that enables reducing embedding size with minimal loss in representation quality. To evaluate the model, we measure quality on the `MTEB-rus` benchmark. Additionally, to measure long-context retrieval, we run Russian subset of MultiLongDocRetrieval (MLDR) task.
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View details ↗Javne_Nabavke_embedding_1000
DejanX13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 ↗Poverenik_embedding_doc_2000
DejanX13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 ↗lt-wikidata-comp-en
dell-research-harvard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 ↗indoSBERT-large
denaya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-0.6B-GGUF
dengcao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 ↗dac_16khz
descriptThis is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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View details ↗dac_24khz
descript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 ↗dac_44khz
descriptThis is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed]
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View details ↗Desearch-Embedding-4B
desearch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 ↗gbert-large-paraphrase-cosine
deutsche-telekomThis is a sentence-transformers model. It maps sentences & paragraphs (text) into a 1024 dimensional dense vector space. The model is intended to be used together with SetFit to improve German few-shot text classification. It has a sibling model called deutsche-telekom/gbert-large-paraphrase-euclidean. **Loss Function**\ We have used MultipleNegativesRankingLoss with cosine similarity as the loss function. **Training Data**\ The model is trained on a carefully filtered dataset of deutsche-telekom/ger-backtrans-paraphrase. We deleted the following pairs of sentences: - `min_char_len` less than 15 - `jaccard_similarity` greater than 0.3 - `de_token_count` greater than 30 - `en_de_token_count` greater than 30 - `cos_sim` less than 0.85
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