fe498b3d5e1e29fb756c203d50e217e8f258e561,examples/training/train_new_entity_type.py,,main,#Any#Any#Any#Any#,70

Before Change

            losses = {}
            gold_parses = get_gold_parses(nlp.make_doc, TRAIN_DATA)
            for batch in minibatch(gold_parses, size=3):
                docs, golds = zip(*batch)
                nlp.update(docs, golds, losses=losses, sgd=optimizer,
                           drop=0.35)
            print(losses)

After Change

    with nlp.disable_pipes(*other_pipes):  // only train NER
        optimizer = nlp.begin_training()
        for itn in range(n_iter):
            random.shuffle(TRAIN_DATA)
            losses = {}
            for text, annotations in TRAIN_DATA:
                nlp.update([text], [annotations], sgd=optimizer, drop=0.35,
                           losses=losses)

Instances