Embed sentence in the template in a few clicks

Aug 6th, 2022
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How to embed sentence in the template

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foreign [Music] ER from cohere AI this video is about word and sentence embeddings word and sentence embeddings are the bread and butter of large language models why is this well the idea of language models is to get a computer to understand and process language however language is made by words whereas computers can only process numbers so word embeddings are a way to go from words to numbers so they associate each word with a list of numbers and sentence embeddings are the same thing they associate each sentence with a list of numbers but in a way that makes sense however I should clarify that this is not done by humans looking at the words and associating numbers that make sense no no this is done by a computer normally a neural network complicated model and what it does is that it looks at context so its two words appear a line in the same context it gets them closer and closer and closer same thing with sentences and at the end of the day you get some really really cool results l

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The sentence or document embedding models generate article representation for the whole document, and differ from the word embedding models, which are at the word level. Sent2Vec [28] and InferSent [27] are the two most outstanding models among all the sentence embedding models.
In natural language processing (NLP), a word embedding is a representation of a word. The embedding is used in text analysis. Typically, the representation is a real-valued vector that encodes the meaning of the word in such a way that the words that are closer in the vector space are expected to be similar in meaning.
What is Sentence Embedding? In NLP, sentence embedding refers to a numeric representation of a sentence in the form of a vector of real numbers, which encodes meaningful semantic information. It enables comparisons of sentence similarity by measuring the distance or similarity between these vectors.
Word embedding is often used in NLP tasks like translating languages, classifying texts, and answering questions. On the other hand, sentence embedding is a technique that represents a whole sentence or a group of words as a single fixed-length vector.
Usually, sentence embeddings are computed as the sum, mean or max of the masked word embeddings. It depends on your use case.
Applications of Word and Sentence Embeddings: The resulting model can then be used to classify new documents based on their content. Sentence embeddings can also be used in text classification by representing entire sentences as high-dimensional vectors and then feeding them into a classifier.
There two main categories of word embedding methods: Frequency-based embedding: Embedding methods that utilize the frequency of words to generate their vector representations. Prediction-based embeddings: Generated by models that learn to predict words from their neighboring words in sentences.

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