various small fixes

examples/training/rehearsal.py

@@ -4,8 +4,10 @@ import random
 import warnings
 import srsly
 import spacy
+from spacy.gold import Example
 from spacy.util import minibatch, compounding
 
+# TODO: further fix & test this script for v.3 ? (read_gold_data is never called)
 
 LABEL = "ANIMAL"
 TRAIN_DATA = [
@@ -35,15 +37,13 @@ def read_raw_data(nlp, jsonl_loc):
 
 
 def read_gold_data(nlp, gold_loc):
-    docs = []
-    golds = []
+    examples = []
     for json_obj in srsly.read_jsonl(gold_loc):
         doc = nlp.make_doc(json_obj["text"])
         ents = [(ent["start"], ent["end"], ent["label"]) for ent in json_obj["spans"]]
-        gold = GoldParse(doc, entities=ents)
-        docs.append(doc)
-        golds.append(gold)
-    return list(zip(docs, golds))
+        example = Example.from_dict(doc, {"entities": ents})
+        examples.append(example)
+    return examples
 
 
 def main(model_name, unlabelled_loc):

examples/training/train_textcat.py

@@ -62,11 +62,10 @@ def main(config_path, output_dir=None, n_iter=20, n_texts=2000, init_tok2vec=Non
     train_examples = []
     for text, cats in zip(train_texts, train_cats):
         doc = nlp.make_doc(text)
-        gold = GoldParse(doc, cats=cats)
+        example = Example.from_dict(doc, {"cats": cats})
         for cat in cats:
             textcat.add_label(cat)
-        ex = Example.from_gold(gold, doc=doc)
-        train_examples.append(ex)
+        train_examples.append(example)
 
     with nlp.select_pipes(enable="textcat"):  # only train textcat
         optimizer = nlp.begin_training()

spacy/cli/train_from_config.py

@@ -467,7 +467,7 @@ def train_while_improving(
 
     Every iteration, the function yields out a tuple with:
 
-    * batch: A zipped sequence of Tuple[Doc, GoldParse] pairs.
+    * batch: A list of Example objects.
     * info: A dict with various information about the last update (see below).
     * is_best_checkpoint: A value in None, False, True, indicating whether this
         was the best evaluation so far. You should use this to save the model

spacy/tests/pipeline/test_textcat.py

@@ -11,6 +11,7 @@ from spacy.util import fix_random_seed
 
 from ..util import make_tempdir
 from spacy.pipeline.defaults import default_tok2vec
+from ...gold import Example
 
 TRAIN_DATA = [
     ("I'm so happy.", {"cats": {"POSITIVE": 1.0, "NEGATIVE": 0.0}}),
@@ -50,21 +51,20 @@ def test_textcat_learns_multilabel():
             cats = {letter: float(w2 == letter) for letter in letters}
             docs.append((Doc(nlp.vocab, words=["d"] * 3 + [w1, w2] + ["d"] * 3), cats))
     random.shuffle(docs)
-    model = TextCategorizer(nlp.vocab, width=8)
+    textcat = TextCategorizer(nlp.vocab, width=8)
     for letter in letters:
-        model.add_label(letter)
-    optimizer = model.begin_training()
+        textcat.add_label(letter)
+    optimizer = textcat.begin_training()
     for i in range(30):
         losses = {}
-        Ys = [GoldParse(doc, cats=cats) for doc, cats in docs]
-        Xs = [doc for doc, cats in docs]
-        model.update(Xs, Ys, sgd=optimizer, losses=losses)
+        examples = [Example.from_dict(doc, {"cats": cats}) for doc, cat in docs]
+        textcat.update(examples, sgd=optimizer, losses=losses)
         random.shuffle(docs)
     for w1 in letters:
         for w2 in letters:
             doc = Doc(nlp.vocab, words=["d"] * 3 + [w1, w2] + ["d"] * 3)
             truth = {letter: w2 == letter for letter in letters}
-            model(doc)
+            textcat(doc)
             for cat, score in doc.cats.items():
                 if not truth[cat]:
                     assert score < 0.5