LUAR-MUD-sentence-transformers
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embeddinggemma-300m-GGUF
gaianet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 ↗nomic-embed-text-v1.5-GGUF
gaianet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 ↗coder_eng
GanjinZero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 ↗UMLSBert_ENG
GanjinZero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 ↗text2vec-large-chinese
GanymedeNil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 ↗geevec-embeddings-1.0-lite
geevec-ai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 ↗med-ruri-v3-310m-v2-from-med
genshiai-daichi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 ↗gety-embed-v0
gety-ai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-Q8_0-GGUF
ggml-orgThis model was converted to GGUF format from `BAAI/bge-m3` using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.
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View details ↗bge-small-en-v1.5-Q8_0-GGUF
ggml-orgThis model was converted to GGUF format from `BAAI/bge-small-en-v1.5` using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model. Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well. Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
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View details ↗embeddinggemma-2-GGUF
ggml-org> [!IMPORTANT] > This model is automatically converted using https://github.com/ggml-org/convert
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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 ↗Then the endpoint can be accessed at http://localhost:8080/embedding, for example using `curl`: When a model uses pooling, or the pooling method is specified using `--pooling`, the normalization can be controlled by the `embd_normalize` parameter. The default value is `2` which means that the embeddings are normalized using the Euclidean norm (L2). Other options are: * -1 No normalization * 0 Max absolute * 1 Taxicab * 2 Euclidean/L2 * \>2 P-Norm
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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 ↗Nomic-Embed-Text-V2-GGUF
ggml-org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 ↗omniASR-LLM-300M-encoder
giangndmThis repository contains the standalone **bfloat16 `safetensors` Audio Encoder** extracted from `ziywang50/omniASR-LLM-300M` / `facebook/omniASR-LLM-300M`. It is $100\%$ natively compatible with the Hugging Face `transformers` library (`AutoModel` / `Wav2Vec2Model` / `AutoFeatureExtractor`).
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View details ↗This is a sentence-transformers model finetuned from sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 on the csv 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 Type:** Sentence Transformer - **Base model:** sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 - **Maximum Sequence Length:** 128 tokens - **Output Dimensionality:** 384 dimensions - **Similarity Function:** Cosine Similarity - **Training Dataset:** - csv - **Documentation:** Sentence Transformers Documentation - **Repository:** Sentence Transformers on GitHub - **Hugging Face:** Sentence Transformers on Hugging Face
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View details ↗kobart-base-v1
gogamza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 ↗kobart-base-v2
gogamza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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canine-c
GooglePretrained CANINE model on 104 languages using a masked language modeling (MLM) objective. It was introduced in the paper CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation and first released in this repository. What's special about CANINE is that it doesn't require an explicit tokenizer (such as WordPiece or SentencePiece) as other models like BERT and RoBERTa. Instead, it directly operates at a character level: each character is turned into its Unicode code point. This means that input processing is trivial and can typically be accomplished as:
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View details ↗canine-s
GooglePretrained CANINE model on 104 languages using a masked language modeling (MLM) objective. It was introduced in the paper CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation and first released in this repository. What's special about CANINE is that it doesn't require an explicit tokenizer (such as WordPiece or SentencePiece) as other models like BERT and RoBERTa. Instead, it directly operates at a character level: each character is turned into its Unicode code point. Disclaimer: The team releasing CANINE did not write a model card for this model so this model card has been written by the Hugging Face team.
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View details ↗embeddinggemma-2
GoogleHugging Face | GitHub | Launch Blog | Documentation | Technical Report **EmbeddingGemma 2** is an open multimodal embedding model built by Google DeepMind which maps text (incl. code), images, video, and audio inputs—and combinations thereof—into a single, unified 768-dimensional vector space. The model has 740M total parameters, combining a 270M parameter text model with modular vision (170M) and audio (300M) encoders. Designed to run on consumer hardware such as mobile devices and laptops, EmbeddingGemma 2 delivers low-latency semantic representations for on-device applications, like search, retrieval-augmented generation (RAG), classification, and clustering.
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