public static async Task Main(string[] args)
{
Console.OutputEncoding = Encoding.UTF8;
ApplicationLogging.SetLoggerFactory(LoggerFactory.Create(lb => lb.AddConsole()));
//Need to register the languages we want to use first
Catalyst.Models.English.Register();
//This example shows the two language detection models available on Catalyst.
//The first is derived from the Chrome former language detection code Compact Language Detector 2 (https://github.com/CLD2Owners/cld2)
//and the newer model is derived from Facebook's FastText language detection dataset (see: https://fasttext.cc/blog/2017/10/02/blog-post.html)
//Configures the model storage to use the local folder ./catalyst-models/
Storage.Current = new DiskStorage("catalyst-models");
var cld2LanguageDetector = await LanguageDetector.FromStoreAsync(Language.Any, Version.Latest, "");
var fastTextLanguageDetector = await FastTextLanguageDetector.FromStoreAsync(Language.Any, Version.Latest, "");
//We show bellow the detection on short and longer samples. You can expect lower precision on shorter texts, as there is less information for the model to work with
//It's also interesting to see the kind of mistakes these models make, such as detecting Welsh as Gaelic_Scottish_Gaelic
foreach (var(lang, text) in Data.ShortSamples)
{
var doc = new Document(text);
fastTextLanguageDetector.Process(doc);
var doc2 = new Document(text);
cld2LanguageDetector.Process(doc2);
Console.WriteLine(text);
Console.WriteLine($"Actual:\t{lang}\nFT:\t{doc.Language}\nCLD2\t{doc2.Language}");
Console.WriteLine();
}
foreach (var(lang, text) in Data.LongSamples)
{
var doc = new Document(text);
fastTextLanguageDetector.Process(doc);
var doc2 = new Document(text);
cld2LanguageDetector.Process(doc2);
Console.WriteLine(text);
Console.WriteLine($"Actual:\t{lang}\nFT:\t{doc.Language}\nCLD2\t{doc2.Language}");
Console.WriteLine();
}
// You can also access all predictions via the Predict method:
var allPredictions = fastTextLanguageDetector.Predict(new Document(Data.LongSamples[Language.Spanish]));
Console.WriteLine($"\n\nTop 10 predictions and scores for the Spanish sample:");
foreach (var kv in allPredictions.OrderByDescending(kv => kv.Value).Take(10))
{
Console.WriteLine($"{kv.Key.ToString().PadRight(40)}\tScore: {kv.Value:n2}");
}
}
public static void Test(string pathToSentences)
{
var vectorizer = FastTextLanguageDetector.FromStoreAsync(Language.Any, 0, null).WaitResult();
var pipe = new Pipeline();
pipe.Add(new SpaceTokenizer());
var docs = File.ReadAllLines(pathToSentences).Shuffle()
.Where(txt => !string.IsNullOrWhiteSpace(txt))
.Select(txt => txt.Split('\t'))
.Where(s => s.Length == 3 && Languages.IsValid3LetterCode(s[1]))
//.GroupBy(l => l[1])
//.Where(g => g.Count() > 10_000)
//.SelectMany(g => g)
.Select(s =>
{
var doc = new Document(s[2]);
doc.Labels.Add(Languages.EnumToCode(Languages.ThreeLetterCodeToEnum(s[1])));
return(doc as IDocument);
});
docs = pipe.Process(docs).WithCaching(Language.Any, 0, "language-detector-corpus", 100_000).ToList();
int TP = 0, FP = 0, FN = 0;
int k = 0;
var sw = Stopwatch.StartNew();
Parallel.ForEach(docs, (doc) =>
{
k++;
vectorizer.Process(doc);
if (doc.Language == Languages.CodeToEnum(doc.Labels.First()))
{
Interlocked.Increment(ref TP);
}
else
{
Interlocked.Increment(ref FP);
Interlocked.Increment(ref FN);
}
});
sw.Stop();
var precision = (double)TP / (double)(TP + FN);
var recall = (double)TP / (double)(TP + FN);
var f1 = 2 * (precision * recall) / (precision + recall);
Console.WriteLine($"F1= {f1 * 100:0.0}% P= {precision * 100:0.0}% R={recall * 100:0.0}% in {sw.Elapsed.TotalSeconds:0.00}s or {k / sw.Elapsed.TotalSeconds} doc/s");
}
