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Google translate itself uses Deep learning to translate sentences which can be seen here. You can translate sentences across languages for which you need two things : A large dataset which has pairs of translations ( like English-French ). You can find such a dataset from here. A sequence-to-sequence RNN model. They have Encoder-Decoder architecture which ...


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The Fı-score is preferred to simple classification accuracy in order to counter the problem of imbalanced datasets; if the thing you are looking for occurs only rarely anyway then a naive classifier can always say no and appear to be working very well! A variant on Fı is Fß, where Fß = (1+ß²) × [ (P × R) ÷ ( (ß² × P) + R ) ] Vary ...


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T5 is in fact a sequence-to-sequence model, it has an encoder that generates some hidden states representing the input and a decoder that generates the output. When you fine-tune the model you can happily ignore how the model was pre-trained and only train for your specific task as schematically shown in the original Google blog post. For fine-tuning, you ...


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This is area of NLG . You can use template based text generation techniques, wherein you have defined structure of output text and fill in required blank areas based on keywords. This technique is used in reports generation. An example is narrative science company. Other approach can be to use OpenAI GPT . Example is Generate Text using OpenAIGPT2 in ...


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