L’exemple suivant obtient les propriétés de la version de l’objet actif dans le magasin.

// Get properties
var mlEncoder = sn_ml.EncoderStore.get('ml_incident_categorization');

gs.print(JSON.stringify(JSON.parse(mlEncoder.getActiveVersion().getProperties()), null, 2));

Sortie :

*** Script: {
  "datasetsProperties": [
    {
      "tableName": "incident",
      "fieldNames": [
        "assignment_group",
        "short_description",
        "description"
      ],
      "encodedQuery": "activeANYTHING"
    }
  ],
  "domainName": "global",
  "isActive": "true",
  "label": "my encoder definition",
  "name": "ml_x_snc_global_global_my_encoder_definition",
  "processingLanguage": "en",
  "stopwords": [
    "Default English Stopwords"
  ],
  "versionNumber": "1"
}

L’exemple suivant montre comment retourner un vecteur pour une seule phrase.

var myEncoderName = 'GloVe';

var myEncoder = sn_ml.EncoderStore.get(myEncoderName);

var input = ["I like to code."];

var vectors = myEncoder.getActiveVersion().getSentenceVectors(input);

gs.print(vectors);

Sortie :

*** Script: [-0.16243751347064972,0.30614474415779114,0.08489049971103668,
-0.48100000619888306,-0.170997753739357,0.08779674768447876,-0.07848624140024185,-0.15123701095581055,
-0.07843250036239624,-1.9505999088287354,0.3007825016975403,-0.07804800570011139,-0.04779449850320816,
0.04803549498319626,0.09848674386739731,0.2427891194820404,-0.41138750314712524,0.10880374908447266,
 … ,
0.21227750182151794,0.18478751182556152,-0.3113832473754883,-0.16560424864292145,0.09052124619483948]

L’exemple suivant montre comment obtenir des mots similaires à l’aide de l’encodeur GloVe .

var myEncoderName = 'GloVe';
var myEncoder = sn_ml.EncoderStore.get(myEncoderName);
var input =  ["apple"];
var options = {"topN":"5"};
gs.print(myEncoder.getActiveVersion().getSimilarWords(input, options));	

Sortie :

*** Script: [[["iphone",0.5987],["macintosh",0.5836],["ipod",0.5761],["microsoft",0.5664],["ipad",0.5628]]]

L’exemple suivant montre un résultat correct avec la formation terminée.

// Get status
var mlEncoder = sn_ml.EncoderStore.get('ml_incident_categorization');

gs.print(JSON.stringify(JSON.parse(mlEncoder.getActiveVersion().getStatus(true), null, 2)));

Sortie :

{
 "state":"solution_complete",
 "percentComplete":"100",
 "hasJobEnded":"true",
 "details":{"stepLabel":"Encoder Complete"} // This information is only returned if getStatus(true);
}

L’exemple suivant montre un résultat d’échec lorsque la formation est terminée.

// Get status
var encoderName = 'ml_x_snc_global_global_encoder';
var mlEncoder = sn_ml.EncoderStore.get(encoderName);
var trainingStatus = mlEncoder.getLatestVersion().getStatus();

gs.print(JSON.stringify(JSON.parse(trainingStatus), null, 2));

Sortie :

{
 "state":"solution_error",
 "percentComplete":"100",
 "hasJobEnded":"true"
}

L’exemple suivant montre comment obtenir un numéro de version.

// Get version number
var mlEncoder = sn_ml.EncoderStore.get('ml_incident_categorization');

gs.print("Version number: "+JSON.stringify(JSON.parse(mlEncoder.getActiveVersion().getVersionNumber()), null, 2));

Sortie :

Version number: 1

L’exemple suivant montre comment obtenir un vecteur à partir du mot hello.

var myEncoderName = 'GloVe';
var myEncoder = sn_ml.EncoderStore.get(myEncoderName);
var input =  ["hello"];

gs.print(myEncoder.getActiveVersion().getWordVectors(input));

Sortie :

*** Script: [[-0.337119996547699,-0.2169100046157837,-0.006636499892920256,
-0.41624999046325684,-1.2554999589920044,-0.0284659992903471,-0.7219499945640564,
-0.5288699865341187,0.0072085000574588776,0.3199700117111206,0.02942500077188015,
-0.013236000202596188,0.4351100027561188,0.2571600079536438,0.3899500072002411,
 … ,
0.3384299874305725,0.4055800139904022,0.18073000013828278,0.6424999833106995]]