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For example, my first approach to the pet sentences would be to label all sentences within a respective text corpus containing according information for either yes or no. You would then convert this to a tertiary tag set, something like ["pet allowed", "pet not allowed", "irrelevant"]. You could then try out a model based on SentenceBert, other sentence-level embeddings/language models or 1D CNNs for this. flairNLP (https://github.com/flairNLP/flair) is a small, little framework which provides comfortable high-level access to different common language models which integrates perfectly with pyTorch.
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