Embed sentence in the Maintenance Request in a few clicks

Aug 6th, 2022
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  4. Find the tool from the top toolbar to embed sentence in Maintenance Request and apply it.
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How to embed sentence in the Maintenance Request

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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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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.
One way sentence embeddings are evaluated is using the Semantic Textual Similarity (STS) task. The idea of STS is that a good sentence representation should encode the semantic information of a sentence in order to be able to differentiate between similar sentences and dissimilar ones.
For example, if the prompt is query: , then the sentence What is the capital of France? will be encoded as query: What is the capital of France? because the sentence is appended to the prompt.
Sentence embeddings offer better context and semantic understanding compared to word embeddings, making them more suitable for tasks requiring document-level representations in multilingual NLP.
Unlike word embeddings, which represent individual words, sentence embeddings represent entire sentences. One popular approach to creating sentence embeddings is through the use of pre-trained models, such as the Universal Sentence Encoder (USE) from Google.
The geometry of an embedding space should have a good spread. Generally speaking, a smaller set of more frequent, unrelated words should be evenly distributed throughout the space while a larger set of rare words should cluster around frequent words.
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.

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