Spaces:
Runtime error
Runtime error
File size: 7,071 Bytes
58627fa |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 |
"""
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)
|