cosine-similarity

来源： https://stackoverflow.com/questions/64442720/re-calculate-similarity-matrix-given-new-documents

来源： https://stackoverflow.com/questions/64442720/re-calculate-similarity-matrix-given-new-documents

来源： https://stackoverflow.com/questions/63690068/how-to-find-cosine-similarity-of-one-vector-vs-matrix

来源： https://stackoverflow.com/questions/42212423/python-tf-idf-fast-way-to-update-the-tf-idf-matrix

来源： https://stackoverflow.com/questions/42212423/python-tf-idf-fast-way-to-update-the-tf-idf-matrix

问题 I am trying to build a Fake news classifier and I am quite new in this field. I have a column "title_1_en" which has the title for fake news and another column called "title_2_en". There are 3 target labels; "agreed", "disagreed", and "unrelated" if the title of the news in column "title_2_en" agrees, disagrees or is unrelated to that in the first column. I have tried calculating basic cosine similarity between the two titles after converting the words of the sentences into vectors. This has

问题 I am trying to build a Fake news classifier and I am quite new in this field. I have a column "title_1_en" which has the title for fake news and another column called "title_2_en". There are 3 target labels; "agreed", "disagreed", and "unrelated" if the title of the news in column "title_2_en" agrees, disagrees or is unrelated to that in the first column. I have tried calculating basic cosine similarity between the two titles after converting the words of the sentences into vectors. This has

问题 I am trying to build a Fake news classifier and I am quite new in this field. I have a column "title_1_en" which has the title for fake news and another column called "title_2_en". There are 3 target labels; "agreed", "disagreed", and "unrelated" if the title of the news in column "title_2_en" agrees, disagrees or is unrelated to that in the first column. I have tried calculating basic cosine similarity between the two titles after converting the words of the sentences into vectors. This has

问题 I would like to apply fine-tuning Bert to calculate semantic similarity between sentences. I search a lot websites, but I almost not found downstream about this. I just found STS benchmark . I wonder if I can use STS benchmark dataset to train a fine-tuning bert model, and apply it to my task. Is it reasonable? As I know, there are a lot method to calculate similarity including cosine similarity, pearson correlation, manhattan distance, etc. How choose for semantic similarity? 回答1: As a

问题 I would like to apply fine-tuning Bert to calculate semantic similarity between sentences. I search a lot websites, but I almost not found downstream about this. I just found STS benchmark . I wonder if I can use STS benchmark dataset to train a fine-tuning bert model, and apply it to my task. Is it reasonable? As I know, there are a lot method to calculate similarity including cosine similarity, pearson correlation, manhattan distance, etc. How choose for semantic similarity? 回答1: As a