small fixes

examples/training/create_kb.py

@@ -30,7 +30,7 @@ ENTITIES = {"Q2146908": ("American golfer", 342), "Q7381115": ("publisher", 17)}
     model=("Model name, should have pretrained word embeddings", "positional", None, str),
     output_dir=("Optional output directory", "option", "o", Path),
 )
-def main(model=None, output_dir=None):
+def main(model, output_dir=None):
     """Load the model and create the KB with pre-defined entity encodings.
     If an output_dir is provided, the KB will be stored there in a file 'kb'.
     The updated vocab will also be written to a directory in the output_dir."""

spacy/gold/corpus.py

@@ -14,11 +14,11 @@ class Corpus:
     """
 
     def __init__(self, train_loc, dev_loc, limit=0):
-        """Create a GoldCorpus.
+        """Create a Corpus.
 
         train (str / Path): File or directory of training data.
         dev (str / Path): File or directory of development data.
-        RETURNS (GoldCorpus): The newly created object.
+        RETURNS (Corpus): The newly created object.
         """
         self.train_loc = train_loc
         self.dev_loc = dev_loc

spacy/gold/example.pyx

@@ -1,3 +1,5 @@
+import warnings
+
 import numpy
 
 from ..tokens import Token
@@ -204,24 +206,23 @@ def _annot2array(vocab, tok_annot, doc_annot):
     values = []
 
     for key, value in doc_annot.items():
-        if key == "entities":
-            if value:
+        if value:
+            if key == "entities":
                 words = tok_annot["ORTH"]
                 spaces = tok_annot["SPACY"]
                 ent_iobs, ent_types = _parse_ner_tags(value, vocab, words, spaces)
                 tok_annot["ENT_IOB"] = ent_iobs
                 tok_annot["ENT_TYPE"] = ent_types
-        elif key == "links":
-            if value:
+            elif key == "links":
                 entities = doc_annot.get("entities", {})
                 if value and not entities:
                     raise ValueError(Errors.E981)
                 ent_kb_ids = _parse_links(vocab, tok_annot["ORTH"], value, entities)
                 tok_annot["ENT_KB_ID"] = ent_kb_ids
-        elif key == "cats":
-            pass
-        else:
-            raise ValueError(f"Unknown doc attribute: {key}")
+            elif key == "cats":
+                pass
+            else:
+                raise ValueError(f"Unknown doc attribute: {key}")
 
     for key, value in tok_annot.items():
         if key not in IDS:
@@ -298,6 +299,7 @@ def _fix_legacy_dict_data(example_dict):
     if "HEAD" in token_dict and "SENT_START" in token_dict:
         # If heads are set, we don't also redundantly specify SENT_START.
         token_dict.pop("SENT_START")
+        warnings.warn("Ignoring annotations for sentence starts, as dependency heads are set")
     return {
         "token_annotation": token_dict,
         "doc_annotation": doc_dict

spacy/ml/models/multi_task.py

@@ -48,9 +48,7 @@ def build_masked_language_model(vocab, wrapped_model, mask_prob=0.15):
     def mlm_forward(model, docs, is_train):
         mask, docs = _apply_mask(docs, random_words, mask_prob=mask_prob)
         mask = model.ops.asarray(mask).reshape((mask.shape[0], 1))
-        output, backprop = model.get_ref("wrapped-model").begin_update(
-            docs
-        )  # drop=drop
+        output, backprop = model.get_ref("wrapped-model").begin_update(docs)
 
         def mlm_backward(d_output):
             d_output *= 1 - mask

spacy/ml/models/tok2vec.py

@@ -147,7 +147,7 @@ def hash_char_embed_bilstm_v1(
 
 @registry.architectures.register("spacy.LayerNormalizedMaxout.v1")
 def LayerNormalizedMaxout(width, maxout_pieces):
-    return Maxout(nO=width, nP=maxout_pieces, dropout=0.0, normalize=True,)
+    return Maxout(nO=width, nP=maxout_pieces, dropout=0.0, normalize=True)
 
 
 @registry.architectures.register("spacy.MultiHashEmbed.v1")

spacy/tests/parser/test_ner.py

@@ -7,10 +7,10 @@ from spacy.pipeline.defaults import default_ner
 from spacy.pipeline import EntityRecognizer, EntityRuler
 from spacy.vocab import Vocab
 from spacy.syntax.ner import BiluoPushDown
+from spacy.gold import Example
 from spacy.tokens import Doc
 
 from ..util import make_tempdir
-from ...gold import Example
 
 TRAIN_DATA = [
     ("Who is Shaka Khan?", {"entities": [(7, 17, "PERSON")]}),

spacy/tests/test_gold.py

@@ -596,8 +596,6 @@ def test_split_sents(merged_dict):
     assert token_annotation_2["sent_starts"] == [1, 0, 0, 0]
 
 
-# This fails on some None value? Need to look into that.
-@pytest.mark.xfail  # TODO
 def test_tuples_to_example(vocab, merged_dict):
     cats = {"TRAVEL": 1.0, "BAKING": 0.0}
     merged_dict = dict(merged_dict)
@@ -607,6 +605,6 @@ def test_tuples_to_example(vocab, merged_dict):
     assert words == merged_dict["words"]
     tags = [token.tag_ for token in ex.reference]
     assert tags == merged_dict["tags"]
-    sent_starts = [token.is_sent_start for token in ex.reference]
+    sent_starts = [bool(token.is_sent_start) for token in ex.reference]
     assert sent_starts == [bool(v) for v in merged_dict["sent_starts"]]
-    ex.reference.cats == cats
+    assert ex.reference.cats == cats