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"i always thought you needed a million followers, or a hundred thousand followers, to make money on social media," barclay told insider. "but that's really not true. there are so many ways that you can make it a business, even if it is part-time." from affiliate links:
"i always thought you needed a million followers, or a hundred thousand followers, to make money on social media," barclay told insider. "but that's really not true. there are so many ways that you can make it a business, even if it is part-time." from affiliate links:
"i always thought you needed a million followers, or a hundred thousand followers, to make money on social media," barclay told insider. "but that's really not true. there are so many ways that you can make it a business, even if it is part-time." from affiliate links:
other creators are finding their own lucrative niches in the live space.
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4) other neural network methods bert is a transformer-based pre-trained model to pre-train deep bidirectional representations from the unlabelled text by learning right and left word context [177]. bert is pretrained on english wikipedia text paragraphs of 2500 million words and books corpus with 800 million words. in contrast to the directional model, which read the text sequentially from right to left or left to right, bert read the entire sequence of words at once, which allows the model to learn the context of the word based on its surroundings (right and left of the word). in our work, we used the bert model consisted of 12 layered transformer blocks, where each block contained 12 self-attention layers and 768 hidden layers. one sentence at a time was fed into the model. the input sentences were divided into tokens and mapped with the bert library as input ids. at the beginning and end of each sentence, both the classification token and sep (separate segment token) were added. a fixed-length input mask of 0 was applied, indicating padded tokens, and 1 shows unpadded tokens. the token embedding lists were given to each transformer, and a feature vector of the same length was generated at the output. the cls output on the 12th transformer layer containing prediction probability vector transformations was used as a combined sequence representation from which classification was made.
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