Using a technique like word embeddings (e.g., Word2Vec, GloVe), we can represent the text as a dense vector. Here is a possible vector representation ( note that this is a fictional example and actual values would depend on the specific model and training data):
[0.2, 0.1, 0.4, 0.3, 0.05, 0.01, 0.005, 0.001, ...] This vector has a high-dimensionality (e.g., 128, 256, or 512 dimensions) and captures the semantic relationships between the words in the text.
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📌 如不希望原有海棠幣受半年效期限制，建議先行使用完既有餘額後再進行儲值。 Using a technique like word embeddings (e
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Using a technique like word embeddings (e.g., Word2Vec, GloVe), we can represent the text as a dense vector. Here is a possible vector representation ( note that this is a fictional example and actual values would depend on the specific model and training data):
[0.2, 0.1, 0.4, 0.3, 0.05, 0.01, 0.005, 0.001, ...] This vector has a high-dimensionality (e.g., 128, 256, or 512 dimensions) and captures the semantic relationships between the words in the text.
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