18 instantiations of TextData
Microsoft.ML.Tests (18)
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)." },
4 references to TextData
Microsoft.ML.Tests (4)
Scenarios\WordBagTest.cs (4)
17var samples = new List<TextData>() 39var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData, TransformedTextData>(textTransformer); 54var samples = new List<TextData>() 74var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData, TransformedTextData>(textTransformer);