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LongEmbed: Extending Embedding Models for Long Context Retrieval. Dawei Zhu, Liang Wang, Nan Yang, Yifan Song, Wenhao Wu, Furu Wei, Sujian Li, arxiv 2024. Github Repo for LongEmbed: https://github.com/dwzhu-pku/LongEmbed. Please refer to our paper at https://arxiv.org/abs/2404.12096.pdf. Note that E5-Base-4k simply expands the position embedding matrix to allow for 4,096 position ids. The embedding vectors for the original pids {0,1,2,...,511} is mapped to represent {0,8,16,...,4088}. Embedding vectors for other pids are trained. So for inputs not exceeding 512 tokens, please multiply the position ids by 8 to maintain the original behavior, as shown in the code above.

Embeddings & similarity
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This 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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This is a the sentence-transformers version of the intfloat/e5-large-v2 model: It maps sentences & paragraphs to a 1024 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: You can use the embaas API to encode your input. Get your free API key from embaas.io

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This is a sentence-transformers model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search. For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: https://seb.sbert.net

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EmbeddingGemma-Scandi-300m is finetuned on the Scandinavian (Danish, Norwegian, Swedish) text embeddings dataset from DDSC (nordic-embedding-training-data) using google/embeddinggemma-300m as the base model via the sentence-transformers framework. 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. - **Model Type:** Sentence Transformer - **Base model:** google/embeddinggemma-300m - **Maximum Sequence Length:** 2048 tokens - **Output Dimensionality:** 768 dimensions - **Similarity Function:** Cosine Similarity - **Training Dataset:** - nordic-embedding-training-data - **Languages:** da, no, sw, sv - **License:** apache-2.0

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mmBERTscandi-base-embedding is finetuned on the Scandinavian (Danish, Norwegian, Swedish) text embeddings dataset from DDSC (nordic-embedding-training-data) using jonasaise/scandmmBERT-base-scandinavian/jhu-clsp/mmBERT-base as the base model via the sentence-transformers framework. 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.

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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. The model was trained on Turkish machine translated versions of NLI and STS-b datasets, using example training scripts from sentence-transformers GitHub repository. 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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