07/26/2024: Release preprint [[2407.18887] Embedding And Clustering Your Data Can Improve Contrastive Pretraining](https://arxiv.org/abs/2407.18887) on arXiv. 07/18/2024: Release of `snowflake-arctic-embed-m-v1.5`, capable of producing highly compressible embedding vectors that preserve quality even when squished as small as 128 bytes per vector. Details about the development of this model are available in the launch post on the Snowflake engineering blog. This model is an updated version of snowflake-arctic-embed-m designed to improve embedding vector compressibility. This model achieves a slightly higher performance overall without compression, and it is additionally capable of retaining most of its retrieval quality even down to 128 byte embedding vectors through a combination of Matryoshka Representation Learning (MRL) and uniform scalar quanitization.
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View details ↗Snowflake arctic-embed-m-v2.0 is the newest addition to the suite of embedding models Snowflake has released optimizing for retrieval performance and inference efficiency. Arctic Embed 2.0 introduces a new standard for multilingual embedding models, combining high-quality multilingual text retrieval without sacrificing performance in English. Released under the permissive Apache 2.0 license, Arctic Embed 2.0 is ideal for applications that demand reliable, enterprise-grade multilingual search and retrieval at scale. 1. Multilingual without compromise: Excels in English and non-English retrieval, outperforming leading open-source and proprietary models on benchmarks like MTEB Retrieval, CLEF, and MIRACL. 2. Inference efficiency: Its 113m non-embedding parameters inference is fast and efficient for any scale.
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View details ↗News | Models | Usage | Evaluation | Contact | FAQ License | Acknowledgement 12/04/2024: Release of snowflake-arctic-embed-l-v2.0 and snowflake-arctic-embed-m-v2.0 our newest models with multilingual workloads in mind. These models outperform prior versions of Arctic Embed and we suggest these replace prior versions! 07/26/2024: Release preprint [[2407.18887] Embedding And Clustering Your Data Can Improve Contrastive Pretraining](https://arxiv.org/abs/2407.18887) on arXiv.
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View details ↗News | Models | Usage | Evaluation | Contact | FAQ License | Acknowledgement 12/04/2024: Release of snowflake-arctic-embed-l-v2.0 and snowflake-arctic-embed-m-v2.0 our newest models with multilingual workloads in mind. These models outperform prior versions of Arctic Embed and we suggest these replace prior versions! 07/26/2024: Release preprint [[2407.18887] Embedding And Clustering Your Data Can Improve Contrastive Pretraining](https://arxiv.org/abs/2407.18887) on arXiv.
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View details ↗This 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 ↗This repository hosts the **all-MiniLM-L6-v2** models exported for the React Native ExecuTorch library as ExecuTorch `.pte` programs, ready to run on device. Upstream model: all-MiniLM-L6-v2 `xnnpack/all_minilm_l6_v2_xnnpack_int8.pte` is 8da8w (int8 dynamic activations, int8 per-channel weights) on the Linear layers, with an int8 word-embedding table. SQuAD v1.1 dev passage retrieval (2000 questions against their 315 paragraphs), dot product:
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View details ↗This repository hosts the **paraphrase-multilingual-MiniLM-L12-v2** models exported for the React Native ExecuTorch library as ExecuTorch `.pte` programs, ready to run on device. These files are published for the **ExecuTorch v1.4.1** runtime. ExecuTorch gives no forward compatibility guarantee, so an older runtime may fail to load them. To use them in React Native ExecuTorch, pass the model constant shipped in the library's model registry to the corresponding task pipeline. See the documentation.
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View details ↗This 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. We follow a **teacher-student** transfer learning approach to train an `bertin-roberta-base-spanish` model using parallel EN-ES sentence pairs. 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 ↗バージョン1よりも良いロス関数であるMultipleNegativesRankingLossを用いて学習した改良版です。 手元の非公開データセットでは、バージョン1よりも1.5〜2ポイントほど精度が高い結果が得られました。
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View details ↗日本語Sentence-BERTモデルと同一のデータセットと設定で学習しました。 手元の非公開データセットでは、日本語Sentence-BERTモデルと比べて定量的な精度が同等〜0.5pt程度高く、定性的な精度は本モデルの方が高い結果でした。
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View details ↗This is a SPLADE Sparse Encoder model finetuned from almanach/camembert-base on the msmarco-qwen3-reranker-0.6_b-french dataset using the sentence-transformers library. It maps sentences & paragraphs to a 32005-dimensional sparse vector space and can be used for semantic search and sparse retrieval.
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View details ↗This is a SPLADE Sparse Encoder model trained on the natural-questions dataset using the sentence-transformers library. It maps sentences & paragraphs to a 30522-dimensional sparse vector space and can be used for semantic search and sparse retrieval. - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 30522 dimensions - **Similarity Function:** Dot Product - **Training Dataset:** - natural-questions - **Language:** en - **License:** apache-2.0 - **Documentation:** Sentence Transformers Documentation - **Documentation:** Sparse Encoder Documentation - **Repository:** Sentence Transformers on GitHub - **Hugging Face:** Sparse Encoders on Hugging Face
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