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The maximum input length is a limitation of the model by construction. That number defines the length of the positional embedding table, so you cannot provide a longer input, because it is not possible for the model to index the positional embedding for positions greater than the maximum. This limitation, nevertheless, is not arbitrary, but has a deeper ...


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This problem is multiple choice answering question. I can see you have already tried gensim, doc2vec etc. You can try pytorch based transformer solution. Here is the link: multiple-choice . You can create your data in swag format and remove --do_train in below code for prediction on your dataset. It has been trained on swag dataset and has given decent ...


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Chatbots and Q&A systems differ in their complexity as well as use cases. Let's discuss each of them separately. Chatbots: They can answer various questions asked during an interactive conversation. Interactive conversion means the system keeps a track of questions asked earlier and can engage in longer conversations. They have a sought of memory which ...


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Question-answering (QA) is sometimes used to refer to the task where the input to the system is a question and a list of possible answers (normally only a handful) or a paragraph where the answer is supposed to be found, and the expected answer is the index of the correct answer or the start/end positions where the answer located within the text. In ...


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Maybe this article will help you How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddings. Talks about contextual word embeddings like BERT and GPT how they can capture various polysemous concepts rather than the static word embeddings which create a single representation for each word, such as GloVe.


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A recent PR changed the location of the scripts you are looking for to examples/legacy/question-answering


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Question answering (QA) is a complex problem and an active field of research. There are probably some academic prototypes around, but I doubt there's any general-purpose ready-to-use QA library. However there are probably state of the art implementations for closed QA, i.e. QA restricted to a specific domain (I'm not aware of any specific library though). ...


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The ranking of the answers is part of the ML process, i.e. a system should be trained to rank the answers according to their relevance. Heuristic measures such as the ones mentioned in your question may offer decent approximations, but as you noticed they are very limited. You may be interested in datasets and methods used in shared tasks about QA, for ...


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For Question Answering, you need 2 logits : one for the start position, one for the end position. Based on these 2 logits, you have an answer span (denoted by the start/end position). In the source code, you have : pooled_output = self.pooler(sequence_output) If you take a look at the pooler, there is a comment : # We "pool" the model by simply taking the ...


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