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Update how spancat_exclusive is constructed

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In this commit, I added the following:
- Put the default values of negative_weight and allow_overlap
    in the default_config dictionary.
- Rename make_spancat -> make_exclusive_spancat
This commit is contained in:
Lj Miranda 2023-01-17 10:17:29 +08:00
parent eac09df704
commit e61f0a4035
1 changed files with 7 additions and 5 deletions
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12
spacy/pipeline/spancat_exclusive.py
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@ -46,22 +46,24 @@ DEFAULT_EXCL_SPANCAT_MODEL = Config().from_str(spancat_excl_default_config)["mod
"spancat_exclusive",
assigns=["doc.spans"],
default_config={
"spans_key": "sc",
"model": DEFAULT_EXCL_SPANCAT_MODEL,
"suggester": {"@misc": "spacy.ngram_suggester.v1", "sizes": [1, 2, 3]},
"model": DEFAULT_EXCL_SPANCAT_MODEL,
"spans_key": "sc",
"scorer": {"@scorers": "spacy.spancat_scorer.v1"},
"negative_weight": 1.0,
"allow_overlap": True,
},
default_score_weights={"spans_sc_f": 1.0, "spans_sc_p": 0.0, "spans_sc_r": 0.0},
)
def make_spancat(
def make_exclusive_spancat(
nlp: Language,
name: str,
suggester: Suggester,
model: Model[Tuple[List[Doc], Ragged], Floats2d],
spans_key: str,
scorer: Optional[Callable],
negative_weight: float = 1.0,
allow_overlap: bool = True,
negative_weight: float,
allow_overlap: bool,
) -> "Exclusive_SpanCategorizer":
"""Create an Exclusive_SpanCategorizer component. The span categorizer consists of two
parts: a suggester function that proposes candidate spans, and a labeler