mirror of
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139 lines
4.4 KiB
Python
139 lines
4.4 KiB
Python
from typing import Dict, Any
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import srsly
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from ..language import Language
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from ..matcher import Matcher
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from ..tokens import Doc
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from .. import util
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@Language.component(
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"merge_noun_chunks",
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requires=["token.dep", "token.tag", "token.pos"],
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retokenizes=True,
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)
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def merge_noun_chunks(doc: Doc) -> Doc:
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"""Merge noun chunks into a single token.
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doc (Doc): The Doc object.
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RETURNS (Doc): The Doc object with merged noun chunks.
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DOCS: https://spacy.io/api/pipeline-functions#merge_noun_chunks
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"""
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if not doc.has_annotation("DEP"):
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return doc
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with doc.retokenize() as retokenizer:
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for np in doc.noun_chunks:
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attrs = {"tag": np.root.tag, "dep": np.root.dep}
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retokenizer.merge(np, attrs=attrs)
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return doc
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@Language.component(
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"merge_entities",
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requires=["doc.ents", "token.ent_iob", "token.ent_type"],
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retokenizes=True,
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)
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def merge_entities(doc: Doc):
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"""Merge entities into a single token.
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doc (Doc): The Doc object.
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RETURNS (Doc): The Doc object with merged entities.
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DOCS: https://spacy.io/api/pipeline-functions#merge_entities
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"""
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with doc.retokenize() as retokenizer:
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for ent in doc.ents:
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attrs = {"tag": ent.root.tag, "dep": ent.root.dep, "ent_type": ent.label}
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retokenizer.merge(ent, attrs=attrs)
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return doc
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@Language.component("merge_subtokens", requires=["token.dep"], retokenizes=True)
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def merge_subtokens(doc: Doc, label: str = "subtok") -> Doc:
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"""Merge subtokens into a single token.
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doc (Doc): The Doc object.
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label (str): The subtoken dependency label.
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RETURNS (Doc): The Doc object with merged subtokens.
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DOCS: https://spacy.io/api/pipeline-functions#merge_subtokens
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"""
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# TODO: make stateful component with "label" config
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merger = Matcher(doc.vocab)
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merger.add("SUBTOK", [[{"DEP": label, "op": "+"}]])
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matches = merger(doc)
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spans = util.filter_spans([doc[start : end + 1] for _, start, end in matches])
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with doc.retokenize() as retokenizer:
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for span in spans:
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retokenizer.merge(span)
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return doc
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@Language.factory(
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"token_splitter",
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default_config={"min_length": 25, "split_length": 10},
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retokenizes=True,
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)
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def make_token_splitter(
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nlp: Language, name: str, *, min_length: int = 0, split_length: int = 0
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):
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return TokenSplitter(min_length=min_length, split_length=split_length)
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class TokenSplitter:
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def __init__(self, min_length: int = 0, split_length: int = 0):
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self.min_length = min_length
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self.split_length = split_length
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def __call__(self, doc: Doc) -> Doc:
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if self.min_length > 0 and self.split_length > 0:
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with doc.retokenize() as retokenizer:
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for t in doc:
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if len(t.text) >= self.min_length:
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orths = []
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heads = []
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attrs = {}
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for i in range(0, len(t.text), self.split_length):
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orths.append(t.text[i : i + self.split_length])
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heads.append((t, i / self.split_length))
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retokenizer.split(t, orths, heads, attrs)
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return doc
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def _get_config(self) -> Dict[str, Any]:
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return {
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"min_length": self.min_length,
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"split_length": self.split_length,
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}
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def _set_config(self, config: Dict[str, Any] = {}) -> None:
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self.min_length = config.get("min_length", 0)
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self.split_length = config.get("split_length", 0)
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def to_bytes(self, **kwargs):
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serializers = {
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"cfg": lambda: srsly.json_dumps(self._get_config()),
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}
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return util.to_bytes(serializers, [])
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def from_bytes(self, data, **kwargs):
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deserializers = {
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"cfg": lambda b: self._set_config(srsly.json_loads(b)),
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}
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util.from_bytes(data, deserializers, [])
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return self
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def to_disk(self, path, **kwargs):
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path = util.ensure_path(path)
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serializers = {
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"cfg": lambda p: srsly.write_json(p, self._get_config()),
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}
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return util.to_disk(path, serializers, [])
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def from_disk(self, path, **kwargs):
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path = util.ensure_path(path)
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serializers = {
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"cfg": lambda p: self._set_config(srsly.read_json(p)),
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}
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util.from_disk(path, serializers, [])
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