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spaCy used to recommended (archive link) that you use spaCy when you want production-grade performance but don't need to customize your architecture. They recommended that you use allenNLP when you want to explore different architectures or use the state-of-the-art models. They recommended against using allenNLP for production, though. Since spaCy 3.0, ...


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Following with the idea of building a classifier, one option is to use nltk library together with Keras-Tensorflow once you have a labeled dataset with the desired process categories. You can go on two main approaches: bag-of-words sequence-modeling As a quick resume of the steps to implement in a text classifier with the first approach, you could follow ...


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Finally it worked using this command: conda install torchvision==0.8.2 -c pytorch then: pip install allennlp==2.1.0 allennlp-models==2.1.0


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I can see in the link that there are versions 0.8.1 and 0.8.2 which satisfy your requirement. So just try installing with version - pip install torchvision==0.8.2 Better if you do it in a virtual environment.


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One way to understand how ELMo's character convolutions work is by directly inspecting the source code. There, in the forward method, you can see that the input to the network is a tensor of dimensions (batch_size, sequence_length, 50), where 50 is the maximum number of characters per word. Therefore, before passing the text to the network, it is segmented ...


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The API is a bit confusing. You pass a RegularizerApplicator instance to the model, which takes a list of tuples of the form (regex, regularizer). The regex matches against your model's parameter's name. For example, if you had layer called linear_relu_stack.0.bias, linear_relu_stack.0.weight, you could apply a single regularizer to both with the regex "...


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