11/29/2023 0 Comments Tagspace facebok![]() Use case: learn word embeddings in unsupervised way. ![]() At training time, it generates multiple training examples: each feature from input is picked as label, and other features surronding it (up to distance ws) are picked as input Use case: learning from multi-relational graphs.At training time, the first label from the collection will be picked as input and the second label will be picked as the label. Use case: learn pairwise similarity from collections of similar objects, e.g.At training time, two labels from the collection are randomly picked as the input and label. Each example contains a collection of labels.Use case: learning a mapping from an object to a set of objects, e.g.At training time, one label from the collection is randomly picked as the input, and the rest of the labels in the collection become the label. Use case: content-based or collaborative filtering-based recommendation, see pagespace example below.At training time, one label from the collection is randomly picked as the label, and the rest of the labels in the collection become the input. Use case: classification tasks, see tagspace example below.Each example contains both input and labels.StarSpace supports the following training modes (the default is the first one): In order to learn the embeddings in the more general case where each label consists of words, one needs to specify the -fileFormat flag to be 'labelDoc', as follows: $./starspace train -trainFile data.txt -model modelSaveFile -fileFormat labelDoc Įmbedding vectors will be learned for each word and label to group similar inputs and labels together. In the more general case, each label also consists of words: word_1 word_2. The binary file can be used later to compute entity embedding modelSaveFile is a binary file containing the parameters of the model along with the dictionary and all hyper parameters. modelSaveFile.tsv is a standard tsv format file containing the entityĮmbedding vectors, one per line. At the end of optimization the program will save two files: model and modelSaveFile.tsv. Where data.txt is a training file containing utf-8 encoded text. In order to learn the embeddings, do: $./starspace train -trainFile data.txt -model modelSaveFile It assumes by default that labels are words that are prefixed by the string _label_, and the prefix string can be This file format is the same as in fastText. Each line will be one input example, in the simplest case the input has k words, and each labels 1.r is a single word: word_1 word_2. StarSpace takes input files of the following format. In order to build StarSpace, use the following: git clone Optional: if one wishes to run the unit tests in src directory, google test is required and its path needs to be specified in 'TEST_INCLUDES' in the makefile. You need to install Boost library and specify the path of boost library in makefile in order to run StarSpace. ![]()
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