Make factories top-level functions in registrations.py

spacy/registrations.py

@@ -19,8 +19,6 @@ from .pipeline.entityruler import EntityRuler
 from .pipeline.span_finder import SpanFinder
 from .pipeline.ner import EntityRecognizer
 from .pipeline._parser_internals.transition_system import TransitionSystem
-from .pipeline.ner import EntityRecognizer
-from .pipeline.dep_parser import DependencyParser
 from .pipeline.dep_parser import DependencyParser
 from .pipeline.tagger import Tagger
 from .pipeline.multitask import MultitaskObjective
@@ -169,6 +167,420 @@ def register_factories() -> None:
     if FACTORIES_REGISTERED:
         return
 
+    # Register factories using the same pattern as Language.factory decorator
+    # We use Language.factory()() pattern which exactly mimics the decorator
+
+    # attributeruler
+    Language.factory(
+        "attribute_ruler",
+        default_config={
+            "validate": False,
+            "scorer": {"@scorers": "spacy.attribute_ruler_scorer.v1"},
+        },
+    )(make_attribute_ruler)
+
+    # entity_linker
+    Language.factory(
+        "entity_linker",
+        requires=["doc.ents", "doc.sents", "token.ent_iob", "token.ent_type"],
+        assigns=["token.ent_kb_id"],
+        default_config={
+            "model": DEFAULT_NEL_MODEL,
+            "labels_discard": [],
+            "n_sents": 0,
+            "incl_prior": True,
+            "incl_context": True,
+            "entity_vector_length": 64,
+            "get_candidates": {"@misc": "spacy.CandidateGenerator.v1"},
+            "get_candidates_batch": {"@misc": "spacy.CandidateBatchGenerator.v1"},
+            "generate_empty_kb": {"@misc": "spacy.EmptyKB.v2"},
+            "overwrite": True,
+            "scorer": {"@scorers": "spacy.entity_linker_scorer.v1"},
+            "use_gold_ents": True,
+            "candidates_batch_size": 1,
+            "threshold": None,
+        },
+        default_score_weights={
+            "nel_micro_f": 1.0,
+            "nel_micro_r": None,
+            "nel_micro_p": None,
+        },
+    )(make_entity_linker)
+
+    # entity_ruler
+    Language.factory(
+        "entity_ruler",
+        assigns=["doc.ents", "token.ent_type", "token.ent_iob"],
+        default_config={
+            "phrase_matcher_attr": None,
+            "matcher_fuzzy_compare": {"@misc": "spacy.levenshtein_compare.v1"},
+            "validate": False,
+            "overwrite_ents": False,
+            "ent_id_sep": DEFAULT_ENT_ID_SEP,
+            "scorer": {"@scorers": "spacy.entity_ruler_scorer.v1"},
+        },
+        default_score_weights={
+            "ents_f": 1.0,
+            "ents_p": 0.0,
+            "ents_r": 0.0,
+            "ents_per_type": None,
+        },
+    )(make_entity_ruler)
+
+    # lemmatizer
+    Language.factory(
+        "lemmatizer",
+        assigns=["token.lemma"],
+        default_config={
+            "model": None,
+            "mode": "lookup",
+            "overwrite": False,
+            "scorer": {"@scorers": "spacy.lemmatizer_scorer.v1"},
+        },
+        default_score_weights={"lemma_acc": 1.0},
+    )(make_lemmatizer)
+
+    # textcat
+    Language.factory(
+        "textcat",
+        assigns=["doc.cats"],
+        default_config={
+            "threshold": 0.0,
+            "model": DEFAULT_SINGLE_TEXTCAT_MODEL,
+            "scorer": {"@scorers": "spacy.textcat_scorer.v2"},
+        },
+        default_score_weights={
+            "cats_score": 1.0,
+            "cats_score_desc": None,
+            "cats_micro_p": None,
+            "cats_micro_r": None,
+            "cats_micro_f": None,
+            "cats_macro_p": None,
+            "cats_macro_r": None,
+            "cats_macro_f": None,
+            "cats_macro_auc": None,
+            "cats_f_per_type": None,
+        },
+    )(make_textcat)
+
+    # token_splitter
+    Language.factory(
+        "token_splitter",
+        default_config={"min_length": 25, "split_length": 10},
+        retokenizes=True,
+    )(make_token_splitter)
+
+    # doc_cleaner
+    Language.factory(
+        "doc_cleaner",
+        default_config={"attrs": {"tensor": None, "_.trf_data": None}, "silent": True},
+    )(make_doc_cleaner)
+
+    # tok2vec
+    Language.factory(
+        "tok2vec",
+        assigns=["doc.tensor"],
+        default_config={"model": DEFAULT_TOK2VEC_MODEL},
+    )(make_tok2vec)
+
+    # senter
+    Language.factory(
+        "senter",
+        assigns=["token.is_sent_start"],
+        default_config={
+            "model": DEFAULT_SENTER_MODEL,
+            "overwrite": False,
+            "scorer": {"@scorers": "spacy.senter_scorer.v1"},
+        },
+        default_score_weights={"sents_f": 1.0, "sents_p": 0.0, "sents_r": 0.0},
+    )(make_senter)
+
+    # morphologizer
+    Language.factory(
+        "morphologizer",
+        assigns=["token.morph", "token.pos"],
+        default_config={
+            "model": DEFAULT_MORPH_MODEL,
+            "overwrite": True,
+            "extend": False,
+            "scorer": {"@scorers": "spacy.morphologizer_scorer.v1"},
+            "label_smoothing": 0.0,
+        },
+        default_score_weights={
+            "pos_acc": 0.5,
+            "morph_acc": 0.5,
+            "morph_per_feat": None,
+        },
+    )(make_morphologizer)
+
+    # spancat
+    Language.factory(
+        "spancat",
+        assigns=["doc.spans"],
+        default_config={
+            "threshold": 0.5,
+            "spans_key": DEFAULT_SPANS_KEY,
+            "max_positive": None,
+            "model": DEFAULT_SPANCAT_MODEL,
+            "suggester": {"@misc": "spacy.ngram_suggester.v1", "sizes": [1, 2, 3]},
+            "scorer": {"@scorers": "spacy.spancat_scorer.v1"},
+        },
+        default_score_weights={"spans_sc_f": 1.0, "spans_sc_p": 0.0, "spans_sc_r": 0.0},
+    )(make_spancat)
+
+    # spancat_singlelabel
+    Language.factory(
+        "spancat_singlelabel",
+        assigns=["doc.spans"],
+        default_config={
+            "spans_key": DEFAULT_SPANS_KEY,
+            "model": DEFAULT_SPANCAT_SINGLELABEL_MODEL,
+            "negative_weight": 1.0,
+            "suggester": {"@misc": "spacy.ngram_suggester.v1", "sizes": [1, 2, 3]},
+            "scorer": {"@scorers": "spacy.spancat_scorer.v1"},
+            "allow_overlap": True,
+        },
+        default_score_weights={"spans_sc_f": 1.0, "spans_sc_p": 0.0, "spans_sc_r": 0.0},
+    )(make_spancat_singlelabel)
+
+    # future_entity_ruler
+    Language.factory(
+        "future_entity_ruler",
+        assigns=["doc.ents"],
+        default_config={
+            "phrase_matcher_attr": None,
+            "validate": False,
+            "overwrite_ents": False,
+            "scorer": {"@scorers": "spacy.entity_ruler_scorer.v1"},
+            "ent_id_sep": "__unused__",
+            "matcher_fuzzy_compare": {"@misc": "spacy.levenshtein_compare.v1"},
+        },
+        default_score_weights={
+            "ents_f": 1.0,
+            "ents_p": 0.0,
+            "ents_r": 0.0,
+            "ents_per_type": None,
+        },
+    )(make_future_entity_ruler)
+
+    # span_ruler
+    Language.factory(
+        "span_ruler",
+        assigns=["doc.spans"],
+        default_config={
+            "spans_key": SPAN_RULER_DEFAULT_SPANS_KEY,
+            "spans_filter": None,
+            "annotate_ents": False,
+            "ents_filter": {"@misc": "spacy.first_longest_spans_filter.v1"},
+            "phrase_matcher_attr": None,
+            "matcher_fuzzy_compare": {"@misc": "spacy.levenshtein_compare.v1"},
+            "validate": False,
+            "overwrite": True,
+            "scorer": {
+                "@scorers": "spacy.overlapping_labeled_spans_scorer.v1",
+                "spans_key": SPAN_RULER_DEFAULT_SPANS_KEY,
+            },
+        },
+        default_score_weights={
+            f"spans_{SPAN_RULER_DEFAULT_SPANS_KEY}_f": 1.0,
+            f"spans_{SPAN_RULER_DEFAULT_SPANS_KEY}_p": 0.0,
+            f"spans_{SPAN_RULER_DEFAULT_SPANS_KEY}_r": 0.0,
+            f"spans_{SPAN_RULER_DEFAULT_SPANS_KEY}_per_type": None,
+        },
+    )(make_span_ruler)
+
+    # trainable_lemmatizer
+    Language.factory(
+        "trainable_lemmatizer",
+        assigns=["token.lemma"],
+        requires=[],
+        default_config={
+            "model": DEFAULT_EDIT_TREE_LEMMATIZER_MODEL,
+            "backoff": "orth",
+            "min_tree_freq": 3,
+            "overwrite": False,
+            "top_k": 1,
+            "scorer": {"@scorers": "spacy.lemmatizer_scorer.v1"},
+        },
+        default_score_weights={"lemma_acc": 1.0},
+    )(make_edit_tree_lemmatizer)
+
+    # textcat_multilabel
+    Language.factory(
+        "textcat_multilabel",
+        assigns=["doc.cats"],
+        default_config={
+            "threshold": 0.5,
+            "model": DEFAULT_MULTI_TEXTCAT_MODEL,
+            "scorer": {"@scorers": "spacy.textcat_multilabel_scorer.v2"},
+        },
+        default_score_weights={
+            "cats_score": 1.0,
+            "cats_score_desc": None,
+            "cats_micro_p": None,
+            "cats_micro_r": None,
+            "cats_micro_f": None,
+            "cats_macro_p": None,
+            "cats_macro_r": None,
+            "cats_macro_f": None,
+            "cats_macro_auc": None,
+            "cats_f_per_type": None,
+        },
+    )(make_multilabel_textcat)
+
+    # span_finder
+    Language.factory(
+        "span_finder",
+        assigns=["doc.spans"],
+        default_config={
+            "threshold": 0.5,
+            "model": DEFAULT_SPAN_FINDER_MODEL,
+            "spans_key": DEFAULT_SPANS_KEY,
+            "max_length": 25,
+            "min_length": None,
+            "scorer": {"@scorers": "spacy.span_finder_scorer.v1"},
+        },
+        default_score_weights={
+            f"spans_{DEFAULT_SPANS_KEY}_f": 1.0,
+            f"spans_{DEFAULT_SPANS_KEY}_p": 0.0,
+            f"spans_{DEFAULT_SPANS_KEY}_r": 0.0,
+        },
+    )(make_span_finder)
+
+    # ner
+    Language.factory(
+        "ner",
+        assigns=["doc.ents", "token.ent_iob", "token.ent_type"],
+        default_config={
+            "moves": None,
+            "update_with_oracle_cut_size": 100,
+            "model": DEFAULT_NER_MODEL,
+            "incorrect_spans_key": None,
+            "scorer": {"@scorers": "spacy.ner_scorer.v1"},
+        },
+        default_score_weights={
+            "ents_f": 1.0,
+            "ents_p": 0.0,
+            "ents_r": 0.0,
+            "ents_per_type": None,
+        },
+    )(make_ner)
+
+    # beam_ner
+    Language.factory(
+        "beam_ner",
+        assigns=["doc.ents", "token.ent_iob", "token.ent_type"],
+        default_config={
+            "moves": None,
+            "update_with_oracle_cut_size": 100,
+            "model": DEFAULT_NER_MODEL,
+            "beam_density": 0.01,
+            "beam_update_prob": 0.5,
+            "beam_width": 32,
+            "incorrect_spans_key": None,
+            "scorer": {"@scorers": "spacy.ner_scorer.v1"},
+        },
+        default_score_weights={
+            "ents_f": 1.0,
+            "ents_p": 0.0,
+            "ents_r": 0.0,
+            "ents_per_type": None,
+        },
+    )(make_beam_ner)
+
+    # parser
+    Language.factory(
+        "parser",
+        assigns=["token.dep", "token.head", "token.is_sent_start", "doc.sents"],
+        default_config={
+            "moves": None,
+            "update_with_oracle_cut_size": 100,
+            "learn_tokens": False,
+            "min_action_freq": 30,
+            "model": DEFAULT_PARSER_MODEL,
+            "scorer": {"@scorers": "spacy.parser_scorer.v1"},
+        },
+        default_score_weights={
+            "dep_uas": 0.5,
+            "dep_las": 0.5,
+            "dep_las_per_type": None,
+            "sents_p": None,
+            "sents_r": None,
+            "sents_f": 0.0,
+        },
+    )(make_parser)
+
+    # beam_parser
+    Language.factory(
+        "beam_parser",
+        assigns=["token.dep", "token.head", "token.is_sent_start", "doc.sents"],
+        default_config={
+            "moves": None,
+            "update_with_oracle_cut_size": 100,
+            "learn_tokens": False,
+            "min_action_freq": 30,
+            "beam_width": 8,
+            "beam_density": 0.0001,
+            "beam_update_prob": 0.5,
+            "model": DEFAULT_PARSER_MODEL,
+            "scorer": {"@scorers": "spacy.parser_scorer.v1"},
+        },
+        default_score_weights={
+            "dep_uas": 0.5,
+            "dep_las": 0.5,
+            "dep_las_per_type": None,
+            "sents_p": None,
+            "sents_r": None,
+            "sents_f": 0.0,
+        },
+    )(make_beam_parser)
+
+    # tagger
+    Language.factory(
+        "tagger",
+        assigns=["token.tag"],
+        default_config={
+            "model": DEFAULT_TAGGER_MODEL,
+            "overwrite": False,
+            "scorer": {"@scorers": "spacy.tagger_scorer.v1"},
+            "neg_prefix": "!",
+            "label_smoothing": 0.0,
+        },
+        default_score_weights={
+            "tag_acc": 1.0,
+            "pos_acc": 0.0,
+            "tag_micro_p": None,
+            "tag_micro_r": None,
+            "tag_micro_f": None,
+        },
+    )(make_tagger)
+
+    # nn_labeller
+    Language.factory(
+        "nn_labeller",
+        default_config={
+            "labels": None,
+            "target": "dep_tag_offset",
+            "model": DEFAULT_MT_MODEL,
+        },
+    )(make_nn_labeller)
+
+    # sentencizer
+    Language.factory(
+        "sentencizer",
+        assigns=["token.is_sent_start", "doc.sents"],
+        default_config={
+            "punct_chars": None,
+            "overwrite": False,
+            "scorer": {"@scorers": "spacy.senter_scorer.v1"},
+        },
+        default_score_weights={"sents_f": 1.0, "sents_p": 0.0, "sents_r": 0.0},
+    )(make_sentencizer)
+
+    # Set the flag to indicate that all factories have been registered
+    FACTORIES_REGISTERED = True
+
+
 # We can't have function implementations for these factories in Cython, because
 # we need to build a Pydantic model for them dynamically, reading their argument
 # structure from the signature. In Cython 3, this doesn't work because the 
@@ -605,415 +1017,4 @@ def register_factories() -> None:
         scorer=scorer
     )
 
-    # Register factories using the same pattern as Language.factory decorator
-    # We use Language.factory()() pattern which exactly mimics the decorator
 
-    # attributeruler
-    Language.factory(
-        "attribute_ruler",
-        default_config={
-            "validate": False,
-            "scorer": {"@scorers": "spacy.attribute_ruler_scorer.v1"},
-        },
-    )(make_attribute_ruler)
-
-    # entity_linker
-    Language.factory(
-        "entity_linker",
-        requires=["doc.ents", "doc.sents", "token.ent_iob", "token.ent_type"],
-        assigns=["token.ent_kb_id"],
-        default_config={
-            "model": DEFAULT_NEL_MODEL,
-            "labels_discard": [],
-            "n_sents": 0,
-            "incl_prior": True,
-            "incl_context": True,
-            "entity_vector_length": 64,
-            "get_candidates": {"@misc": "spacy.CandidateGenerator.v1"},
-            "get_candidates_batch": {"@misc": "spacy.CandidateBatchGenerator.v1"},
-            "generate_empty_kb": {"@misc": "spacy.EmptyKB.v2"},
-            "overwrite": True,
-            "scorer": {"@scorers": "spacy.entity_linker_scorer.v1"},
-            "use_gold_ents": True,
-            "candidates_batch_size": 1,
-            "threshold": None,
-        },
-        default_score_weights={
-            "nel_micro_f": 1.0,
-            "nel_micro_r": None,
-            "nel_micro_p": None,
-        },
-    )(make_entity_linker)
-
-    # entity_ruler
-    Language.factory(
-        "entity_ruler",
-        assigns=["doc.ents", "token.ent_type", "token.ent_iob"],
-        default_config={
-            "phrase_matcher_attr": None,
-            "matcher_fuzzy_compare": {"@misc": "spacy.levenshtein_compare.v1"},
-            "validate": False,
-            "overwrite_ents": False,
-            "ent_id_sep": DEFAULT_ENT_ID_SEP,
-            "scorer": {"@scorers": "spacy.entity_ruler_scorer.v1"},
-        },
-        default_score_weights={
-            "ents_f": 1.0,
-            "ents_p": 0.0,
-            "ents_r": 0.0,
-            "ents_per_type": None,
-        },
-    )(make_entity_ruler)
-
-    # lemmatizer
-    Language.factory(
-        "lemmatizer",
-        assigns=["token.lemma"],
-        default_config={
-            "model": None,
-            "mode": "lookup",
-            "overwrite": False,
-            "scorer": {"@scorers": "spacy.lemmatizer_scorer.v1"},
-        },
-        default_score_weights={"lemma_acc": 1.0},
-    )(make_lemmatizer)
-
-    # textcat
-    Language.factory(
-        "textcat",
-        assigns=["doc.cats"],
-        default_config={
-            "threshold": 0.0,
-            "model": DEFAULT_SINGLE_TEXTCAT_MODEL,
-            "scorer": {"@scorers": "spacy.textcat_scorer.v2"},
-        },
-        default_score_weights={
-            "cats_score": 1.0,
-            "cats_score_desc": None,
-            "cats_micro_p": None,
-            "cats_micro_r": None,
-            "cats_micro_f": None,
-            "cats_macro_p": None,
-            "cats_macro_r": None,
-            "cats_macro_f": None,
-            "cats_macro_auc": None,
-            "cats_f_per_type": None,
-        },
-    )(make_textcat)
-
-    # token_splitter
-    Language.factory(
-        "token_splitter",
-        default_config={"min_length": 25, "split_length": 10},
-        retokenizes=True,
-    )(make_token_splitter)
-
-    # doc_cleaner
-    Language.factory(
-        "doc_cleaner",
-        default_config={"attrs": {"tensor": None, "_.trf_data": None}, "silent": True},
-    )(make_doc_cleaner)
-
-    # tok2vec
-    Language.factory(
-        "tok2vec",
-        assigns=["doc.tensor"],
-        default_config={"model": DEFAULT_TOK2VEC_MODEL},
-    )(make_tok2vec)
-
-    # senter
-    Language.factory(
-        "senter",
-        assigns=["token.is_sent_start"],
-        default_config={
-            "model": DEFAULT_SENTER_MODEL,
-            "overwrite": False,
-            "scorer": {"@scorers": "spacy.senter_scorer.v1"},
-        },
-        default_score_weights={"sents_f": 1.0, "sents_p": 0.0, "sents_r": 0.0},
-    )(make_senter)
-
-    # morphologizer
-    Language.factory(
-        "morphologizer",
-        assigns=["token.morph", "token.pos"],
-        default_config={
-            "model": DEFAULT_MORPH_MODEL,
-            "overwrite": True,
-            "extend": False,
-            "scorer": {"@scorers": "spacy.morphologizer_scorer.v1"},
-            "label_smoothing": 0.0,
-        },
-        default_score_weights={
-            "pos_acc": 0.5,
-            "morph_acc": 0.5,
-            "morph_per_feat": None,
-        },
-    )(make_morphologizer)
-
-    # spancat
-    Language.factory(
-        "spancat",
-        assigns=["doc.spans"],
-        default_config={
-            "threshold": 0.5,
-            "spans_key": DEFAULT_SPANS_KEY,
-            "max_positive": None,
-            "model": DEFAULT_SPANCAT_MODEL,
-            "suggester": {"@misc": "spacy.ngram_suggester.v1", "sizes": [1, 2, 3]},
-            "scorer": {"@scorers": "spacy.spancat_scorer.v1"},
-        },
-        default_score_weights={"spans_sc_f": 1.0, "spans_sc_p": 0.0, "spans_sc_r": 0.0},
-    )(make_spancat)
-
-    # spancat_singlelabel
-    Language.factory(
-        "spancat_singlelabel",
-        assigns=["doc.spans"],
-        default_config={
-            "spans_key": DEFAULT_SPANS_KEY,
-            "model": DEFAULT_SPANCAT_SINGLELABEL_MODEL,
-            "negative_weight": 1.0,
-            "suggester": {"@misc": "spacy.ngram_suggester.v1", "sizes": [1, 2, 3]},
-            "scorer": {"@scorers": "spacy.spancat_scorer.v1"},
-            "allow_overlap": True,
-        },
-        default_score_weights={"spans_sc_f": 1.0, "spans_sc_p": 0.0, "spans_sc_r": 0.0},
-    )(make_spancat_singlelabel)
-
-    # future_entity_ruler
-    Language.factory(
-        "future_entity_ruler",
-        assigns=["doc.ents"],
-        default_config={
-            "phrase_matcher_attr": None,
-            "validate": False,
-            "overwrite_ents": False,
-            "scorer": {"@scorers": "spacy.entity_ruler_scorer.v1"},
-            "ent_id_sep": "__unused__",
-            "matcher_fuzzy_compare": {"@misc": "spacy.levenshtein_compare.v1"},
-        },
-        default_score_weights={
-            "ents_f": 1.0,
-            "ents_p": 0.0,
-            "ents_r": 0.0,
-            "ents_per_type": None,
-        },
-    )(make_future_entity_ruler)
-
-    # span_ruler
-    Language.factory(
-        "span_ruler",
-        assigns=["doc.spans"],
-        default_config={
-            "spans_key": SPAN_RULER_DEFAULT_SPANS_KEY,
-            "spans_filter": None,
-            "annotate_ents": False,
-            "ents_filter": {"@misc": "spacy.first_longest_spans_filter.v1"},
-            "phrase_matcher_attr": None,
-            "matcher_fuzzy_compare": {"@misc": "spacy.levenshtein_compare.v1"},
-            "validate": False,
-            "overwrite": True,
-            "scorer": {
-                "@scorers": "spacy.overlapping_labeled_spans_scorer.v1",
-                "spans_key": SPAN_RULER_DEFAULT_SPANS_KEY,
-            },
-        },
-        default_score_weights={
-            f"spans_{SPAN_RULER_DEFAULT_SPANS_KEY}_f": 1.0,
-            f"spans_{SPAN_RULER_DEFAULT_SPANS_KEY}_p": 0.0,
-            f"spans_{SPAN_RULER_DEFAULT_SPANS_KEY}_r": 0.0,
-            f"spans_{SPAN_RULER_DEFAULT_SPANS_KEY}_per_type": None,
-        },
-    )(make_span_ruler)
-
-    # trainable_lemmatizer
-    Language.factory(
-        "trainable_lemmatizer",
-        assigns=["token.lemma"],
-        requires=[],
-        default_config={
-            "model": DEFAULT_EDIT_TREE_LEMMATIZER_MODEL,
-            "backoff": "orth",
-            "min_tree_freq": 3,
-            "overwrite": False,
-            "top_k": 1,
-            "scorer": {"@scorers": "spacy.lemmatizer_scorer.v1"},
-        },
-        default_score_weights={"lemma_acc": 1.0},
-    )(make_edit_tree_lemmatizer)
-
-    # textcat_multilabel
-    Language.factory(
-        "textcat_multilabel",
-        assigns=["doc.cats"],
-        default_config={
-            "threshold": 0.5,
-            "model": DEFAULT_MULTI_TEXTCAT_MODEL,
-            "scorer": {"@scorers": "spacy.textcat_multilabel_scorer.v2"},
-        },
-        default_score_weights={
-            "cats_score": 1.0,
-            "cats_score_desc": None,
-            "cats_micro_p": None,
-            "cats_micro_r": None,
-            "cats_micro_f": None,
-            "cats_macro_p": None,
-            "cats_macro_r": None,
-            "cats_macro_f": None,
-            "cats_macro_auc": None,
-            "cats_f_per_type": None,
-        },
-    )(make_multilabel_textcat)
-
-    # span_finder
-    Language.factory(
-        "span_finder",
-        assigns=["doc.spans"],
-        default_config={
-            "threshold": 0.5,
-            "model": DEFAULT_SPAN_FINDER_MODEL,
-            "spans_key": DEFAULT_SPANS_KEY,
-            "max_length": 25,
-            "min_length": None,
-            "scorer": {"@scorers": "spacy.span_finder_scorer.v1"},
-        },
-        default_score_weights={
-            f"spans_{DEFAULT_SPANS_KEY}_f": 1.0,
-            f"spans_{DEFAULT_SPANS_KEY}_p": 0.0,
-            f"spans_{DEFAULT_SPANS_KEY}_r": 0.0,
-        },
-    )(make_span_finder)
-
-    # ner
-    Language.factory(
-        "ner",
-        assigns=["doc.ents", "token.ent_iob", "token.ent_type"],
-        default_config={
-            "moves": None,
-            "update_with_oracle_cut_size": 100,
-            "model": DEFAULT_NER_MODEL,
-            "incorrect_spans_key": None,
-            "scorer": {"@scorers": "spacy.ner_scorer.v1"},
-        },
-        default_score_weights={
-            "ents_f": 1.0,
-            "ents_p": 0.0,
-            "ents_r": 0.0,
-            "ents_per_type": None,
-        },
-    )(make_ner)
-
-    # beam_ner
-    Language.factory(
-        "beam_ner",
-        assigns=["doc.ents", "token.ent_iob", "token.ent_type"],
-        default_config={
-            "moves": None,
-            "update_with_oracle_cut_size": 100,
-            "model": DEFAULT_NER_MODEL,
-            "beam_density": 0.01,
-            "beam_update_prob": 0.5,
-            "beam_width": 32,
-            "incorrect_spans_key": None,
-            "scorer": {"@scorers": "spacy.ner_scorer.v1"},
-        },
-        default_score_weights={
-            "ents_f": 1.0,
-            "ents_p": 0.0,
-            "ents_r": 0.0,
-            "ents_per_type": None,
-        },
-    )(make_beam_ner)
-
-    # parser
-    Language.factory(
-        "parser",
-        assigns=["token.dep", "token.head", "token.is_sent_start", "doc.sents"],
-        default_config={
-            "moves": None,
-            "update_with_oracle_cut_size": 100,
-            "learn_tokens": False,
-            "min_action_freq": 30,
-            "model": DEFAULT_PARSER_MODEL,
-            "scorer": {"@scorers": "spacy.parser_scorer.v1"},
-        },
-        default_score_weights={
-            "dep_uas": 0.5,
-            "dep_las": 0.5,
-            "dep_las_per_type": None,
-            "sents_p": None,
-            "sents_r": None,
-            "sents_f": 0.0,
-        },
-    )(make_parser)
-
-    # beam_parser
-    Language.factory(
-        "beam_parser",
-        assigns=["token.dep", "token.head", "token.is_sent_start", "doc.sents"],
-        default_config={
-            "moves": None,
-            "update_with_oracle_cut_size": 100,
-            "learn_tokens": False,
-            "min_action_freq": 30,
-            "beam_width": 8,
-            "beam_density": 0.0001,
-            "beam_update_prob": 0.5,
-            "model": DEFAULT_PARSER_MODEL,
-            "scorer": {"@scorers": "spacy.parser_scorer.v1"},
-        },
-        default_score_weights={
-            "dep_uas": 0.5,
-            "dep_las": 0.5,
-            "dep_las_per_type": None,
-            "sents_p": None,
-            "sents_r": None,
-            "sents_f": 0.0,
-        },
-    )(make_beam_parser)
-
-    # tagger
-    Language.factory(
-        "tagger",
-        assigns=["token.tag"],
-        default_config={
-            "model": DEFAULT_TAGGER_MODEL,
-            "overwrite": False,
-            "scorer": {"@scorers": "spacy.tagger_scorer.v1"},
-            "neg_prefix": "!",
-            "label_smoothing": 0.0,
-        },
-        default_score_weights={
-            "tag_acc": 1.0,
-            "pos_acc": 0.0,
-            "tag_micro_p": None,
-            "tag_micro_r": None,
-            "tag_micro_f": None,
-        },
-    )(make_tagger)
-
-    # nn_labeller
-    Language.factory(
-        "nn_labeller",
-        default_config={
-            "labels": None,
-            "target": "dep_tag_offset",
-            "model": DEFAULT_MT_MODEL,
-        },
-    )(make_nn_labeller)
-
-    # sentencizer
-    Language.factory(
-        "sentencizer",
-        assigns=["token.is_sent_start", "doc.sents"],
-        default_config={
-            "punct_chars": None,
-            "overwrite": False,
-            "scorer": {"@scorers": "spacy.senter_scorer.v1"},
-        },
-        default_score_weights={"sents_f": 1.0, "sents_p": 0.0, "sents_r": 0.0},
-    )(make_sentencizer)
-
-    # Set the flag to indicate that all factories have been registered
-    FACTORIES_REGISTERED = True

spacy/tests/training/test_readers.py

@@ -101,7 +101,11 @@ def test_cat_readers(reader, additional_config):
     nlp = load_model_from_config(config, auto_fill=True)
     T = registry.resolve(nlp.config["training"], schema=ConfigSchemaTraining)
     dot_names = [T["train_corpus"], T["dev_corpus"]]
+    print("T", T)
+    print("dot names", dot_names)
     train_corpus, dev_corpus = resolve_dot_names(nlp.config, dot_names)
+    data = list(train_corpus(nlp))
+    print(len(data))
     optimizer = T["optimizer"]
     # simulate a training loop
     nlp.initialize(lambda: train_corpus(nlp), sgd=optimizer)