4cd9ec0f00788d08c053b885b5591fe134666a65,examples/training/train_ner.py,,main,#Any#Any#Any#,35

Before Change

        for itn in range(n_iter):
            random.shuffle(TRAIN_DATA)
            losses = {}
            for text, annotations in TRAIN_DATA:
                nlp.update(
                    [text],  // batch of texts
                    [annotations],  // batch of annotations
                    drop=0.5,  // dropout - make it harder to memorise data
                    sgd=optimizer,  // callable to update weights
                    losses=losses)
            print(losses)

    // test the trained model
    for text, _ in TRAIN_DATA:

After Change

            random.shuffle(TRAIN_DATA)
            losses = {}
            // batch up the examples using spaCy"s minibatch
            batches = minibatch(TRAIN_DATA, size=compounding(4., 32., 1.001))
            for batch in batches:
                texts, annotations = zip(*batch)
                nlp.update(
                    texts,  // batch of texts
                    annotations,  // batch of annotations
                    drop=0.5,  // dropout - make it harder to memorise data
                    sgd=optimizer,  // callable to update weights
                    losses=losses)
            print("Losses", losses)

    // test the trained model
    for text, _ in TRAIN_DATA:

Instances