Large-scale language model embedding, or LLM embedding, is a powerful approach to capture semantically rich information in text and exploiting it to harness it like other machine learning models, such as those trained using SCIKIT-LEARN, in tasks that require deep contextual understanding of the text, such as intentional recognition and sentiment analysis.
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