wehub-resource-sync
253 lines
7.7 KiB
Python
253 lines
7.7 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Most of the tokenizers code here is copied from DrQA codebase to avoid adding extra dependency
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"""
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import copy
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import logging
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import regex
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import spacy
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logger = logging.getLogger(__name__)
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class Tokens(object):
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"""A class to represent a list of tokenized text."""
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TEXT = 0
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TEXT_WS = 1
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SPAN = 2
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POS = 3
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LEMMA = 4
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NER = 5
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def __init__(self, data, annotators, opts=None):
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self.data = data
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self.annotators = annotators
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self.opts = opts or {}
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def __len__(self):
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"""The number of tokens."""
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return len(self.data)
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def slice(self, i=None, j=None):
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"""Return a view of the list of tokens from [i, j)."""
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new_tokens = copy.copy(self)
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new_tokens.data = self.data[i:j]
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return new_tokens
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def untokenize(self):
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"""Returns the original text (with whitespace reinserted)."""
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return "".join([t[self.TEXT_WS] for t in self.data]).strip()
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def words(self, uncased=False):
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"""Returns a list of the text of each token
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Args:
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uncased: lower cases text
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"""
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if uncased:
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return [t[self.TEXT].lower() for t in self.data]
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else:
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return [t[self.TEXT] for t in self.data]
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def offsets(self):
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"""Returns a list of [start, end) character offsets of each token."""
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return [t[self.SPAN] for t in self.data]
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def pos(self):
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"""Returns a list of part-of-speech tags of each token.
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Returns None if this annotation was not included.
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"""
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if "pos" not in self.annotators:
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return None
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return [t[self.POS] for t in self.data]
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def lemmas(self):
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"""Returns a list of the lemmatized text of each token.
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Returns None if this annotation was not included.
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"""
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if "lemma" not in self.annotators:
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return None
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return [t[self.LEMMA] for t in self.data]
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def entities(self):
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"""Returns a list of named-entity-recognition tags of each token.
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Returns None if this annotation was not included.
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"""
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if "ner" not in self.annotators:
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return None
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return [t[self.NER] for t in self.data]
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def ngrams(self, n=1, uncased=False, filter_fn=None, as_strings=True):
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"""Returns a list of all ngrams from length 1 to n.
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Args:
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n: upper limit of ngram length
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uncased: lower cases text
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filter_fn: user function that takes in an ngram list and returns
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True or False to keep or not keep the ngram
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as_string: return the ngram as a string vs list
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"""
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def _skip(gram):
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if not filter_fn:
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return False
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return filter_fn(gram)
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words = self.words(uncased)
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ngrams = [
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(s, e + 1)
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for s in range(len(words))
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for e in range(s, min(s + n, len(words)))
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if not _skip(words[s : e + 1])
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]
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# Concatenate into strings
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if as_strings:
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ngrams = ["{}".format(" ".join(words[s:e])) for (s, e) in ngrams]
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return ngrams
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def entity_groups(self):
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"""Group consecutive entity tokens with the same NER tag."""
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entities = self.entities()
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if not entities:
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return None
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non_ent = self.opts.get("non_ent", "O")
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groups = []
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idx = 0
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while idx < len(entities):
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ner_tag = entities[idx]
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# Check for entity tag
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if ner_tag != non_ent:
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# Chomp the sequence
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start = idx
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while idx < len(entities) and entities[idx] == ner_tag:
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idx += 1
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groups.append((self.slice(start, idx).untokenize(), ner_tag))
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else:
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idx += 1
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return groups
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class Tokenizer(object):
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"""Base tokenizer class.
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Tokenizers implement tokenize, which should return a Tokens class.
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"""
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def tokenize(self, text):
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raise NotImplementedError
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def shutdown(self):
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pass
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def __del__(self):
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self.shutdown()
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class SimpleTokenizer(Tokenizer):
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ALPHA_NUM = r"[\p{L}\p{N}\p{M}]+"
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NON_WS = r"[^\p{Z}\p{C}]"
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def __init__(self, **kwargs):
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"""
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Args:
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annotators: None or empty set (only tokenizes).
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"""
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self._regexp = regex.compile(
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"(%s)|(%s)" % (self.ALPHA_NUM, self.NON_WS), flags=regex.IGNORECASE + regex.UNICODE + regex.MULTILINE
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)
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if len(kwargs.get("annotators", {})) > 0:
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logger.warning(
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"%s only tokenizes! Skipping annotators: %s" % (type(self).__name__, kwargs.get("annotators"))
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)
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self.annotators = set()
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def tokenize(self, text):
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data = []
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matches = [m for m in self._regexp.finditer(text)]
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for i in range(len(matches)):
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# Get text
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token = matches[i].group()
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# Get whitespace
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span = matches[i].span()
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start_ws = span[0]
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if i + 1 < len(matches):
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end_ws = matches[i + 1].span()[0]
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else:
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end_ws = span[1]
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# Format data
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data.append(
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(
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token,
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text[start_ws:end_ws],
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span,
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)
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)
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return Tokens(data, self.annotators)
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class SpacyTokenizer(Tokenizer):
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def __init__(self, **kwargs):
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"""
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Args:
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annotators: set that can include pos, lemma, and ner.
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model: spaCy model to use (either path, or keyword like 'en').
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"""
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model = kwargs.get("model", "en")
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self.annotators = copy.deepcopy(kwargs.get("annotators", set()))
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nlp_kwargs = {"parser": False}
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if not any([p in self.annotators for p in ["lemma", "pos", "ner"]]):
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nlp_kwargs["tagger"] = False
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if "ner" not in self.annotators:
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nlp_kwargs["entity"] = False
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self.nlp = spacy.load(model, **nlp_kwargs)
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def tokenize(self, text):
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# We don't treat new lines as tokens.
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clean_text = text.replace("\n", " ")
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tokens = self.nlp.tokenizer(clean_text)
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if any([p in self.annotators for p in ["lemma", "pos", "ner"]]):
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self.nlp.tagger(tokens)
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if "ner" in self.annotators:
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self.nlp.entity(tokens)
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data = []
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for i in range(len(tokens)):
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# Get whitespace
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start_ws = tokens[i].idx
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if i + 1 < len(tokens):
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end_ws = tokens[i + 1].idx
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else:
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end_ws = tokens[i].idx + len(tokens[i].text)
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data.append(
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(
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tokens[i].text,
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text[start_ws:end_ws],
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(tokens[i].idx, tokens[i].idx + len(tokens[i].text)),
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tokens[i].tag_,
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tokens[i].lemma_,
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tokens[i].ent_type_,
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)
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)
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# Set special option for non-entity tag: '' vs 'O' in spaCy
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return Tokens(data, self.annotators, opts={"non_ent": ""})
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