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Self-attention is an attention architecture where all of keys, values, and queries come from the input sentence itself. Your data might be fine but the code that passes the input to the net might be broken. We aim to make this library the home of best-in-class optimizations for these functions, and recent optimizations have shown significant performance improvements — 15x or more over equivalent OpenCV functions. For example, train with just 1 or 2 examples and see if your network can learn to differentiate these. Arm NN:

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You could pre-tokenize Chinese sentences using word-tokenization tools such as jieba or Stanford Chinese word segmentor. Accuracy and Efficiency in Language Understanding Neural networks usually process language by generating fixed- or variable-length vector-space representations. Never miss a story from Slav , when you sign up for Medium. One of some advantages of self-attention is that it's easier to capture longer range dependency between words.

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