# cython: infer_types=True, profile=True, binding=True import srsly from typing import Optional, List from ..tokens.doc cimport Doc from .pipe import Pipe from ..language import Language from ..scorer import Scorer from ..gold import validate_examples from .. import util @Language.factory( "sentencizer", assigns=["token.is_sent_start", "doc.sents"], default_config={"punct_chars": None}, scores=["sents_p", "sents_r", "sents_f"], default_score_weights={"sents_f": 1.0, "sents_p": 0.0, "sents_r": 0.0}, ) def make_sentencizer( nlp: Language, name: str, punct_chars: Optional[List[str]] ): return Sentencizer(name, punct_chars=punct_chars) class Sentencizer(Pipe): """Segment the Doc into sentences using a rule-based strategy. DOCS: https://spacy.io/api/sentencizer """ default_punct_chars = ['!', '.', '?', '։', '؟', '۔', '܀', '܁', '܂', '߹', '।', '॥', '၊', '။', '።', '፧', '፨', '᙮', '᜵', '᜶', '᠃', '᠉', '᥄', '᥅', '᪨', '᪩', '᪪', '᪫', '᭚', '᭛', '᭞', '᭟', '᰻', '᰼', '᱾', '᱿', '‼', '‽', '⁇', '⁈', '⁉', '⸮', '⸼', '꓿', '꘎', '꘏', '꛳', '꛷', '꡶', '꡷', '꣎', '꣏', '꤯', '꧈', '꧉', '꩝', '꩞', '꩟', '꫰', '꫱', '꯫', '﹒', '﹖', '﹗', '！', '．', '？', '𐩖', '𐩗', '𑁇', '𑁈', '𑂾', '𑂿', '𑃀', '𑃁', '𑅁', '𑅂', '𑅃', '𑇅', '𑇆', '𑇍', '𑇞', '𑇟', '𑈸', '𑈹', '𑈻', '𑈼', '𑊩', '𑑋', '𑑌', '𑗂', '𑗃', '𑗉', '𑗊', '𑗋', '𑗌', '𑗍', '𑗎', '𑗏', '𑗐', '𑗑', '𑗒', '𑗓', '𑗔', '𑗕', '𑗖', '𑗗', '𑙁', '𑙂', '𑜼', '𑜽', '𑜾', '𑩂', '𑩃', '𑪛', '𑪜', '𑱁', '𑱂', '𖩮', '𖩯', '𖫵', '𖬷', '𖬸', '𖭄', '𛲟', '𝪈', '｡', '。'] def __init__(self, name="sentencizer", *, punct_chars=None): """Initialize the sentencizer. punct_chars (list): Punctuation characters to split on. Will be serialized with the nlp object. RETURNS (Sentencizer): The sentencizer component. DOCS: https://spacy.io/api/sentencizer#init """ self.name = name if punct_chars: self.punct_chars = set(punct_chars) else: self.punct_chars = set(self.default_punct_chars) def begin_training(self, get_examples, pipeline=None, sgd=None): pass def __call__(self, doc): """Apply the sentencizer to a Doc and set Token.is_sent_start. doc (Doc): The document to process. RETURNS (Doc): The processed Doc. DOCS: https://spacy.io/api/sentencizer#call """ start = 0 seen_period = False for i, token in enumerate(doc): is_in_punct_chars = token.text in self.punct_chars token.is_sent_start = i == 0 if seen_period and not token.is_punct and not is_in_punct_chars: doc[start].is_sent_start = True start = token.i seen_period = False elif is_in_punct_chars: seen_period = True if start < len(doc): doc[start].is_sent_start = True return doc def pipe(self, stream, batch_size=128): """Apply the pipe to a stream of documents. This usually happens under the hood when the nlp object is called on a text and all components are applied to the Doc. stream (Iterable[Doc]): A stream of documents. batch_size (int): The number of documents to buffer. YIELDS (Doc): Processed documents in order. DOCS: https://spacy.io/api/sentencizer#pipe """ for docs in util.minibatch(stream, size=batch_size): predictions = self.predict(docs) self.set_annotations(docs, predictions) yield from docs def predict(self, docs): """Apply the pipe to a batch of docs, without modifying them. docs (Iterable[Doc]): The documents to predict. RETURNS: The predictions for each document. """ if not any(len(doc) for doc in docs): # Handle cases where there are no tokens in any docs. guesses = [[] for doc in docs] return guesses guesses = [] for doc in docs: doc_guesses = [False] * len(doc) if len(doc) > 0: start = 0 seen_period = False doc_guesses[0] = True for i, token in enumerate(doc): is_in_punct_chars = token.text in self.punct_chars if seen_period and not token.is_punct and not is_in_punct_chars: doc_guesses[start] = True start = token.i seen_period = False elif is_in_punct_chars: seen_period = True if start < len(doc): doc_guesses[start] = True guesses.append(doc_guesses) return guesses def set_annotations(self, docs, batch_tag_ids): """Modify a batch of documents, using pre-computed scores. docs (Iterable[Doc]): The documents to modify. scores: The tag IDs produced by Sentencizer.predict. """ if isinstance(docs, Doc): docs = [docs] cdef Doc doc cdef int idx = 0 for i, doc in enumerate(docs): doc_tag_ids = batch_tag_ids[i] for j, tag_id in enumerate(doc_tag_ids): # Don't clobber existing sentence boundaries if doc.c[j].sent_start == 0: if tag_id: doc.c[j].sent_start = 1 else: doc.c[j].sent_start = -1 def score(self, examples, **kwargs): """Score a batch of examples. examples (Iterable[Example]): The examples to score. RETURNS (Dict[str, Any]): The scores, produced by Scorer.score_spans. DOCS: https://spacy.io/api/sentencizer#score """ validate_examples(examples, "Sentencizer.score") results = Scorer.score_spans(examples, "sents", **kwargs) del results["sents_per_type"] return results def to_bytes(self, *, exclude=tuple()): """Serialize the sentencizer to a bytestring. RETURNS (bytes): The serialized object. DOCS: https://spacy.io/api/sentencizer#to_bytes """ return srsly.msgpack_dumps({"punct_chars": list(self.punct_chars)}) def from_bytes(self, bytes_data, *, exclude=tuple()): """Load the sentencizer from a bytestring. bytes_data (bytes): The data to load. returns (Sentencizer): The loaded object. DOCS: https://spacy.io/api/sentencizer#from_bytes """ cfg = srsly.msgpack_loads(bytes_data) self.punct_chars = set(cfg.get("punct_chars", self.default_punct_chars)) return self def to_disk(self, path, *, exclude=tuple()): """Serialize the sentencizer to disk. DOCS: https://spacy.io/api/sentencizer#to_disk """ path = util.ensure_path(path) path = path.with_suffix(".json") srsly.write_json(path, {"punct_chars": list(self.punct_chars)}) def from_disk(self, path, *, exclude=tuple()): """Load the sentencizer from disk. DOCS: https://spacy.io/api/sentencizer#from_disk """ path = util.ensure_path(path) path = path.with_suffix(".json") cfg = srsly.read_json(path) self.punct_chars = set(cfg.get("punct_chars", self.default_punct_chars)) return self def get_loss(self, examples, scores): raise NotImplementedError def add_label(self, label): raise NotImplementedError