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Category/embeddings
Embeddings are a crucial concept in AI and NLP, used to represent categorical data as continuous vectors in a high-dimensional space. They are particularly useful for working with text data, capturing the semantic meanings, syntax, and context of words and phrases. By converting sparse one-hot encoded vectors into dense vectors, embeddings help machine learning models learn meaningful relationships between categories.
Embeddings are not limited to text data; they can also be used for other types of categorical data such as images, audio, and video. Using embeddings for these data types captures relationships between different objects or sounds, improving machine learning models' performance.
Incorporating embeddings into AI models can significantly enhance their accuracy, making them an essential tool for data scientists and AI practitioners. By understanding embeddings, you can unlock new possibilities in your AI projects and create more sophisticated and accurate models.
embeddings
The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer three different sizes of models, including GTE-large, GTE-base, and GTE-small. The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including information retrieval, semantic textual similarity, text reranking, etc.
