Scenarios\WordBagTest.cs (18)
19new TextData(){ Text = "This is an example to compute bag-of-word features." },
20new TextData(){ Text = "ML.NET's ProduceWordBags API produces bag-of-word features from input text." },
21new TextData(){ Text = "It does so by first tokenizing text/string into words/tokens then " },
22new TextData(){ Text = "computing n-grams and their numeric values." },
23new TextData(){ Text = "Each position in the output vector corresponds to a particular n-gram." },
24new TextData(){ Text = "The value at each position corresponds to," },
25new TextData(){ Text = "the number of times n-gram occurred in the data (Tf), or" },
26new TextData(){ Text = "the inverse of the number of documents contain the n-gram (Idf)," },
27new TextData(){ Text = "or compute both and multiply together (Tf-Idf)." },
56new TextData(){ Text = "This is an example to compute bag-of-word features." },
57new TextData(){ Text = "ML.NET's ProduceWordBags API produces bag-of-word features from input text." },
58new TextData(){ Text = "It does so by first tokenizing text/string into words/tokens then " },
59new TextData(){ Text = "computing n-grams and their numeric values." },
60new TextData(){ Text = "Each position in the output vector corresponds to a particular n-gram." },
61new TextData(){ Text = "The value at each position corresponds to," },
62new TextData(){ Text = "the number of times n-gram occurred in the data (Tf), or" },
63new TextData(){ Text = "the inverse of the number of documents contain the n-gram (Idf)," },
64new TextData(){ Text = "or compute both and multiply together (Tf-Idf)." },