public async Task <bool> Predict(Agent agent, NlpDoc doc, PipeModel meta)
{
string modelFileName = Path.Combine(Settings.ModelDir, meta.Model);
string predictFileName = Path.Combine(Settings.TempDir, "fasttext.txt");
File.WriteAllText(predictFileName, doc.Sentences[0].Text);
var output = CmdHelper.Run(Path.Combine(Settings.AlgorithmDir, "fasttext"), $"predict-prob {modelFileName}.bin {predictFileName}");
File.Delete(predictFileName);
doc.Sentences[0].Intent = new TextClassificationResult
{
Label = output.Split(' ')[0].Split(new string[] { "__label__" }, StringSplitOptions.None)[1],
Confidence = decimal.Parse(output.Split(' ')[1])
};
return(true);
}
public async Task <bool> Train(Agent agent, NlpDoc doc, PipeModel meta)
{
meta.Model = "classification-fasttext.model";
string parsedTrainingDataFileName = Path.Combine(Settings.TempDir, $"classification-fasttext.parsed.txt");
string modelFileName = Path.Combine(Settings.ModelDir, meta.Model);
// assemble corpus
StringBuilder corpus = new StringBuilder();
agent.Corpus.UserSays.ForEach(x => corpus.AppendLine($"__label__{x.Intent} {x.Text}"));
File.WriteAllText(parsedTrainingDataFileName, corpus.ToString());
var output = CmdHelper.Run(Path.Combine(Settings.AlgorithmDir, "fasttext"), $"supervised -input \"{parsedTrainingDataFileName}\" -output \"{modelFileName}\"", false);
Console.WriteLine($"Saved model to {modelFileName}");
meta.Meta = new JObject();
meta.Meta["compiled at"] = "Aug 3, 2018";
return(true);
}
