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The NER model performance on a particular text depends on which data it was trained with originally, and naturally the standard models (like en_core_web_sm) are trained with English data which doesn't contain a lot of names from non-US/UK origin (same for other kinds of entities like organizations or locations). Better performance can be achieved by training ...


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Keras' one_hot function has many limitations. The biggest issue is that the function does not actually do one hot encoding, it does the hashing trick. One possible fix is to use keras' hashing_trick function. It allows the hashing function to specified. If you pick a stable hashing function like md5, then the values will be consistent across runs. Here is an ...


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