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You need character embeddings. I assume you are already familiar with word2vec technology. Its goal it to make a model "learn" the relative meaning of words, placing them into a highly dimensional space. The same can be done with single characters, instead of whole words. The preprocessing steps you need will be a little bit different, but the ...


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SOTA is changing so rapidly in NLP that even Data Science professionists struggle to cope with it. I have two main sources that I constantly check to gain some insights on SOTA: NLP Progress from Sebastian Ruder. It contains updates on NLP on a whole lot of subfields, NER and POST included. Paper with code contains a section on NLP. That's a great website ...


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It seems they use a shared RNN which process each row sequentially on the sequence of concatenated channels of individual pixels. From the paper Implementation with channels last Let the output of the ConvNet be of size (batch_size, height, width, channels). The RNN expects an input of size (batch_size, sequence_length, input_size)`. So you have to reshape ...


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As rightly pointed out by @erwan, it is a bad idea to use data augmentation with 'text data' The problem of 'training with less data' can be approached in many ways, here I enlist two ways which helped me with significant impact: (a) One approach would be to use semi-supervised approach. There are open sourced language models trained on insanely massive ...


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So the questions asks for how to compute similarity between the organisation description and project titles. One initial thought would be to use a Doc2Vec model (concept, implementation), which will take the organisation descriptions and project titles as input and output a n-dimensional vector in semantic space for the given text. From this, you can at ...


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So the question is concerned about understanding the self-attention mechanism in greater detail, in particular how this idea of multi-head self-attention is used to compute strength of relations between tokens. I think it's best you look through this great tutorial on self-attention and see if this helps in your understanding of multi-head self-attention: ...


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There are no common libraries that support high quality named entity recognition for Chinese. Other options include Information-Extraction-Chinese on GitHub or adapting a paper with code.


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They are meant for different purposes and they are hardly comparable. RoBERTa is meant for text classification and tagging tasks. The idea is that you take a pretrained RoBERTa model and finetune it on your (potentially small) classification or tagging dataset. Some examples of tasks where RoBERTa is useful are sentiment classification, part-of-speech (POS) ...


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