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bilingual-embedding-large

Lajavaness

Bilingual-embedding is the Embedding Model for bilingual language: french and english. This model is a specialized sentence-embedding trained specifically for the bilingual language, leveraging the robust capabilities of XLM-RoBERTa, a pre-trained language model based on the XLM-RoBERTa architecture. The model utilizes xlm-roberta to encode english-french sentences into a 1024-dimensional vector space, facilitating a wide range of applications from semantic search to text clustering. The embeddings capture the nuanced meanings of english-french sentences, reflecting both the lexical and contextual layers of the language. @article{conneau2019unsupervised, title={Unsupervised cross-lingual representation learning at scale}, author={Conneau, Alexis and Khandelwal, Kartikay and Goyal, Naman and Chaudhary, Vishrav and Wenzek, Guillaume and Guzm{\'a}n, Francisco and Grave, Edouard and Ott, Myle and Zettlemoyer, Luke and Stoyanov, Veselin}, journal={arXiv preprint arXiv:1911.02116}, year={2019} } @article{reimers2019sentence, title={Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks}, author={Nils Reimers, Iryna Gurevych}, journal={https://arxiv.org/abs/1908.10084}, year={2019} }

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bilingual-embedding-small

Lajavaness

Bilingual-embedding is the Embedding Model for bilingual language: french and english. This model is a specialized sentence-embedding trained specifically for the bilingual language, leveraging the robust capabilities of Multilingual-MiniLM-L12-H384, a pre-trained language model is built upon multilingual-e5 architecture. The model utilizes MiniLM to encode english-french sentences into a 384-dimensional vector space, facilitating a wide range of applications from semantic search to text clustering. The embeddings capture the nuanced meanings of english-french sentences, reflecting both the lexical and contextual layers of the language. @article{conneau2019unsupervised, title={Unsupervised cross-lingual representation learning at scale}, author={Conneau, Alexis and Khandelwal, Kartikay and Goyal, Naman and Chaudhary, Vishrav and Wenzek, Guillaume and Guzm{\'a}n, Francisco and Grave, Edouard and Ott, Myle and Zettlemoyer, Luke and Stoyanov, Veselin}, journal={arXiv preprint arXiv:1911.02116}, year={2019} } @article{reimers2019sentence, title={Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks}, author={Nils Reimers, Iryna Gurevych}, journal={https://arxiv.org/abs/1908.10084}, year={2019} }

Embeddings & similarity

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sentence-camembert-large

Lajavaness

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。

Embeddings & similarity

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sentence-flaubert-base

Lajavaness

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。

Embeddings & similarity

Input Text→ Output Embedding

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Explore AI models 4

bilingual-embedding-large

bilingual-embedding-small

sentence-camembert-large

sentence-flaubert-base

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