If you specifically wanted the numpy scalar type, use `np.float64` here. Doing this will not modify any behavior and is safe. To silence this warning, use `float` by itself. home/witiko/.virtualenvs/gensim4/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:1101: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. If you specifically wanted the numpy scalar type, use `np.float64` here.Įps=np.finfo(np.float).eps, copy_X=True, fit_path=True, home/witiko/.virtualenvs/gensim4/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:862: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. If you specifically wanted the numpy scalar type, use `np.float64` here.Įps=np.finfo(np.float).eps, copy_Gram=True, verbose=0, home/witiko/.virtualenvs/gensim4/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:284: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. home/witiko/.virtualenvs/gensim4/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:167: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. Method='lar', copy_X=True, eps=np.finfo(np.float).eps, home/witiko/.virtualenvs/gensim4/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:30: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. If you wish to review your current use, check the release note link for additional information.ĭeprecated in NumPy 1.20 for more details and guidance: `np.int64` or `np.int32` to specify the precision. When replacing `np.int`, you may wish to use e.g. To silence this warning, use `int` by itself. home/witiko/.virtualenvs/gensim4/lib/python3.7/site-packages/sklearn/feature_extraction/image.py:167: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. Vectors is derived from the angle between the word2vec embeddings of the The intution behind the method is that weĬompute standard cosine similarity assuming that the document vectors areĮxpressed in a non-orthogonal basis, where the angle between two basis The method also uses theīag-of-words vector representation of the documents (simply put, the word’sįrequencies in the documents). Measure the similarity between the two sentences. No words in common, but by modeling synonymy, SCM is able to accurately SCM is illustrated below for two very similar sentences. Similarity task in the context of community question answering. Outperform many of the state-of-the-art methods in the semantic text It uses a measure of similarity between words, which can be derived Soft Cosine Measure (SCM) is a method that allows us to assess the similarityīetween two documents in a meaningful way, even when they have no words inĬommon. The cosine similarity always belongs to the interval. It follows that the cosine similarity does not depend on the magnitudes of the vectors, but only on their angle. Cosine similarity is the cosine of the angle between the vectors that is, it is the dot product of the vectors divided by the product of their lengths. In data analysis, cosine similarity is a measure of similarity between two non-zero vectors defined in an inner product space.
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