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GLuCoSE (General LUke-based COntrastive Sentence Embedding, "glucose") is a Japanese text embedding model based on LUKE. In order to create a general-purpose, user-friendly Japanese text embedding model, GLuCoSE has been trained on a mix of web data and various datasets associated with natural language inference and search. This model is not only suitable for sentence vector similarity tasks but also for semantic search tasks. - Maximum token count: 512 - Output dimension: 768 - Pooling: mean pooling - Supported language: Japanese You can use this model easily with sentence-transformers. Since the loss function used during training is cosine similarity, we recommend using cosine similarity for downstream tasks.

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This model is a general Japanese text embedding model, excelling in retrieval tasks. It can run on CPU and is designed to measure semantic similarity between sentences, as well as to function as a retrieval system for searching passages based on queries. Key features: - Specialized for retrieval tasks, it demonstrates the highest performance among similar size models in MIRACL and other tasks . - Optimized for Japanese text processing - Can run on CPU During inference, the prefix "query: " or "passage: " is required. Please check the Usage section for details.

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This model can be used for **semantic search** and **documents retrieval** to find relevant passages based on a query. It was trained on a machine translated **MSMARCO dataset** for _german_ with **hard negatives** and **Margin MSE loss**. Combining these elements results in a SOTA transformer for asymmetric search. Details are presented below. The model is based on training with samples from **MSMARCO Passage Ranking** dataset. It contains about 500.000 questions and 8.8 million passages. The training objective is to identify the relevant passages or answers for an input question. In terms of content, the texts deal with diverse domains. Questions are available as sentences but also keyword-based variants can be found. Consequently, models trained on MSMARCO can be used in a variety of domains. The dataset was originally published in English, but has been translated into other languages by researchers with the help of machine translation. To be more specific, **"mMARCO: A Multilingual Version of the MS MARCO Passage Ranking Dataset"** is used, which contains 13 Google based translations, German is one of them.

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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. 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. For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: https://seb.sbert.net

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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. 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. For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: https://seb.sbert.net

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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. 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. For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: https://seb.sbert.net

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Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. The model craters note in the Github Repository > We train unsupervised SimCSE on 106 randomly sampled sentences from English Wikipedia, and train supervised SimCSE on the combination of MNLI and SNLI datasets (314k).

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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. It has been trained over the SNLI, MNLI, SCINLI, SCITAIL, MEDNLI and STSB datasets for providing robust sentence embeddings. 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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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. It has been trained over the SNLI, MNLI, SCINLI, SCITAIL, MEDNLI and STSB datasets for providing robust sentence embeddings. 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. For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: https://seb.sbert.net

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