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| """ | |
| Source: DPR Implementation from Facebook Research | |
| https://github.com/facebookresearch/DPR/tree/master/dpr | |
| """ | |
| import string | |
| import spacy | |
| import regex | |
| import unicodedata | |
| class Tokens(object): | |
| """A class to represent a list of tokenized text.""" | |
| TEXT = 0 | |
| TEXT_WS = 1 | |
| SPAN = 2 | |
| POS = 3 | |
| LEMMA = 4 | |
| NER = 5 | |
| def __init__(self, data, annotators, opts=None): | |
| self.data = data | |
| self.annotators = annotators | |
| self.opts = opts or {} | |
| def __len__(self): | |
| """The number of tokens.""" | |
| return len(self.data) | |
| def slice(self, i=None, j=None): | |
| """Return a view of the list of tokens from [i, j).""" | |
| new_tokens = copy.copy(self) | |
| new_tokens.data = self.data[i: j] | |
| return new_tokens | |
| def untokenize(self): | |
| """Returns the original text (with whitespace reinserted).""" | |
| return ''.join([t[self.TEXT_WS] for t in self.data]).strip() | |
| def words(self, uncased=False): | |
| """Returns a list of the text of each token | |
| Args: | |
| uncased: lower cases text | |
| """ | |
| if uncased: | |
| return [t[self.TEXT].lower() for t in self.data] | |
| else: | |
| return [t[self.TEXT] for t in self.data] | |
| def offsets(self): | |
| """Returns a list of [start, end) character offsets of each token.""" | |
| return [t[self.SPAN] for t in self.data] | |
| def pos(self): | |
| """Returns a list of part-of-speech tags of each token. | |
| Returns None if this annotation was not included. | |
| """ | |
| if 'pos' not in self.annotators: | |
| return None | |
| return [t[self.POS] for t in self.data] | |
| def lemmas(self): | |
| """Returns a list of the lemmatized text of each token. | |
| Returns None if this annotation was not included. | |
| """ | |
| if 'lemma' not in self.annotators: | |
| return None | |
| return [t[self.LEMMA] for t in self.data] | |
| def entities(self): | |
| """Returns a list of named-entity-recognition tags of each token. | |
| Returns None if this annotation was not included. | |
| """ | |
| if 'ner' not in self.annotators: | |
| return None | |
| return [t[self.NER] for t in self.data] | |
| def ngrams(self, n=1, uncased=False, filter_fn=None, as_strings=True): | |
| """Returns a list of all ngrams from length 1 to n. | |
| Args: | |
| n: upper limit of ngram length | |
| uncased: lower cases text | |
| filter_fn: user function that takes in an ngram list and returns | |
| True or False to keep or not keep the ngram | |
| as_string: return the ngram as a string vs list | |
| """ | |
| def _skip(gram): | |
| if not filter_fn: | |
| return False | |
| return filter_fn(gram) | |
| words = self.words(uncased) | |
| ngrams = [(s, e + 1) | |
| for s in range(len(words)) | |
| for e in range(s, min(s + n, len(words))) | |
| if not _skip(words[s:e + 1])] | |
| # Concatenate into strings | |
| if as_strings: | |
| ngrams = ['{}'.format(' '.join(words[s:e])) for (s, e) in ngrams] | |
| return ngrams | |
| def entity_groups(self): | |
| """Group consecutive entity tokens with the same NER tag.""" | |
| entities = self.entities() | |
| if not entities: | |
| return None | |
| non_ent = self.opts.get('non_ent', 'O') | |
| groups = [] | |
| idx = 0 | |
| while idx < len(entities): | |
| ner_tag = entities[idx] | |
| # Check for entity tag | |
| if ner_tag != non_ent: | |
| # Chomp the sequence | |
| start = idx | |
| while (idx < len(entities) and entities[idx] == ner_tag): | |
| idx += 1 | |
| groups.append((self.slice(start, idx).untokenize(), ner_tag)) | |
| else: | |
| idx += 1 | |
| return groups | |
| class Tokenizer(object): | |
| """Base tokenizer class. | |
| Tokenizers implement tokenize, which should return a Tokens class. | |
| """ | |
| def tokenize(self, text): | |
| raise NotImplementedError | |
| def shutdown(self): | |
| pass | |
| def __del__(self): | |
| self.shutdown() | |
| class SimpleTokenizer(Tokenizer): | |
| ALPHA_NUM = r'[\p{L}\p{N}\p{M}]+' | |
| NON_WS = r'[^\p{Z}\p{C}]' | |
| def __init__(self, **kwargs): | |
| """ | |
| Args: | |
| annotators: None or empty set (only tokenizes). | |
| """ | |
| self._regexp = regex.compile( | |
| '(%s)|(%s)' % (self.ALPHA_NUM, self.NON_WS), | |
| flags=regex.IGNORECASE + regex.UNICODE + regex.MULTILINE | |
| ) | |
| if len(kwargs.get('annotators', {})) > 0: | |
| logger.warning('%s only tokenizes! Skipping annotators: %s' % | |
| (type(self).__name__, kwargs.get('annotators'))) | |
| self.annotators = set() | |
| def tokenize(self, text): | |
| data = [] | |
| matches = [m for m in self._regexp.finditer(text)] | |
| for i in range(len(matches)): | |
| # Get text | |
| token = matches[i].group() | |
| # Get whitespace | |
| span = matches[i].span() | |
| start_ws = span[0] | |
| if i + 1 < len(matches): | |
| end_ws = matches[i + 1].span()[0] | |
| else: | |
| end_ws = span[1] | |
| # Format data | |
| data.append(( | |
| token, | |
| text[start_ws: end_ws], | |
| span, | |
| )) | |
| return Tokens(data, self.annotators) | |
| def has_answer(tokenized_answers, text): | |
| text = DPR_normalize(text) | |
| for single_answer in tokenized_answers: | |
| for i in range(0, len(text) - len(single_answer) + 1): | |
| if single_answer == text[i: i + len(single_answer)]: | |
| return True | |
| return False | |
| def locate_answers(tokenized_answers, text): | |
| """ | |
| Returns each occurrence of an answer as (offset, endpos) in terms of *characters*. | |
| """ | |
| tokenized_text = DPR_tokenize(text) | |
| occurrences = [] | |
| text_words, text_word_positions = tokenized_text.words(uncased=True), tokenized_text.offsets() | |
| answers_words = [ans.words(uncased=True) for ans in tokenized_answers] | |
| for single_answer in answers_words: | |
| for i in range(0, len(text_words) - len(single_answer) + 1): | |
| if single_answer == text_words[i: i + len(single_answer)]: | |
| (offset, _), (_, endpos) = text_word_positions[i], text_word_positions[i+len(single_answer)-1] | |
| occurrences.append((offset, endpos)) | |
| return occurrences | |
| STokenizer = SimpleTokenizer() | |
| def DPR_tokenize(text): | |
| return STokenizer.tokenize(unicodedata.normalize('NFD', text)) | |
| def DPR_normalize(text): | |
| return DPR_tokenize(text).words(uncased=True) | |
| # Source: https://github.com/shmsw25/qa-hard-em/blob/master/prepro_util.py | |
| def strip_accents(text): | |
| """Strips accents from a piece of text.""" | |
| text = unicodedata.normalize("NFD", text) | |
| output = [] | |
| for char in text: | |
| cat = unicodedata.category(char) | |
| if cat == "Mn": | |
| continue | |
| output.append(char) | |
| return "".join(output) | |