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"NLP data processing; tokenizes text and creates vocab indexes"
from ..torch_core import *
__all__ = ['BaseTokenizer', 'SpacyTokenizer', 'Tokenizer', 'Vocab', 'fix_html', 'replace_all_caps', 'replace_rep', 'replace_wrep',
'rm_useless_spaces', 'spec_add_spaces', 'BOS', 'FLD', 'UNK', 'PAD', 'TK_MAJ', 'TK_UP', 'TK_REP', 'TK_REP', 'TK_WREP',
'deal_caps']
BOS,FLD,UNK,PAD = 'xxbos','xxfld','xxunk','xxpad'
TK_MAJ,TK_UP,TK_REP,TK_WREP = 'xxmaj','xxup','xxrep','xxwrep'
defaults.text_spec_tok = [UNK,PAD,BOS,FLD,TK_MAJ,TK_UP,TK_REP,TK_WREP]
class BaseTokenizer():
"Basic class for a tokenizer function."
def __init__(self, lang:str): self.lang = lang
def tokenizer(self, t:str) -> List[str]: return t.split(' ')
def add_special_cases(self, toks:Collection[str]): pass
class SpacyTokenizer(BaseTokenizer):
"Wrapper around a spacy tokenizer to make it a `BaseTokenizer`."
def __init__(self, lang:str):
self.tok = spacy.blank(lang)
def tokenizer(self, t:str) -> List[str]:
return [t.text for t in self.tok.tokenizer(t)]
def add_special_cases(self, toks:Collection[str]):
for w in toks:
self.tok.tokenizer.add_special_case(w, [{ORTH: w}])
def spec_add_spaces(t:str) -> str:
"Add spaces around / and # in `t`."
return re.sub(r'([/#])', r' \1 ', t)
def rm_useless_spaces(t:str) -> str:
"Remove multiple spaces in `t`."
return re.sub(' {2,}', ' ', t)
def replace_rep(t:str) -> str:
"Replace repetitions at the character level in `t`."
def _replace_rep(m:Collection[str]) -> str:
c,cc = m.groups()
return f' {TK_REP} {len(cc)+1} {c} '
re_rep = re.compile(r'(\S)(\1{3,})')
return re_rep.sub(_replace_rep, t)
def replace_wrep(t:str) -> str:
"Replace word repetitions in `t`."
def _replace_wrep(m:Collection[str]) -> str:
c,cc = m.groups()
return f' {TK_WREP} {len(cc.split())+1} {c} '
re_wrep = re.compile(r'(\b\w+\W+)(\1{3,})')
return re_wrep.sub(_replace_wrep, t)
def fix_html(x:str) -> str:
"List of replacements from html strings in `x`."
re1 = re.compile(r' +')
x = x.replace('#39;', "'").replace('amp;', '&').replace('#146;', "'").replace(
'nbsp;', ' ').replace('#36;', '$').replace('\\n', "\n").replace('quot;', "'").replace(
'<br />', "\n").replace('\\"', '"').replace('<unk>',UNK).replace(' @.@ ','.').replace(
' @-@ ','-').replace('\\', ' \\ ')
return re1.sub(' ', html.unescape(x))
def replace_all_caps(x:Collection[str]) -> Collection[str]:
"Add `TK_UP` for words in all caps in `x`."
res = []
for t in x:
if t.isupper() and len(t) > 1: res.append(TK_UP)
res.append(t)
return res
def deal_caps(x:Collection[str]) -> Collection[str]:
"Replace all words in `x` by their lower version and add `TK_MAJ`."
res = []
for t in x:
if t[0].isupper() and t[1:].islower(): res.append(TK_MAJ)
res.append(t.lower())
return res
defaults.text_pre_rules = [fix_html, replace_rep, replace_wrep, spec_add_spaces, rm_useless_spaces]
defaults.text_post_rules = [replace_all_caps, deal_caps]
class Tokenizer():
"Put together rules and a tokenizer function to tokenize text with multiprocessing."
def __init__(self, tok_func:Callable=SpacyTokenizer, lang:str='en', pre_rules:ListRules=None,
post_rules:ListRules=None, special_cases:Collection[str]=None, n_cpus:int=None):
self.tok_func,self.lang,self.special_cases = tok_func,lang,special_cases
self.pre_rules = ifnone(pre_rules, defaults.text_pre_rules )
self.post_rules = ifnone(post_rules, defaults.text_post_rules)
self.special_cases = special_cases if special_cases else defaults.text_spec_tok
self.n_cpus = ifnone(n_cpus, defaults.cpus)
def __repr__(self) -> str:
res = f'Tokenizer {self.tok_func.__name__} in {self.lang} with the following rules:\n'
for rule in self.pre_rules: res += f' - {rule.__name__}\n'
for rule in self.post_rules: res += f' - {rule.__name__}\n'
return res
def process_text(self, t:str, tok:BaseTokenizer) -> List[str]:
"Process one text `t` with tokenizer `tok`."
for rule in self.pre_rules: t = rule(t)
toks = tok.tokenizer(t)
for rule in self.post_rules: toks = rule(toks)
return toks
def _process_all_1(self, texts:Collection[str]) -> List[List[str]]:
"Process a list of `texts` in one process."
tok = self.tok_func(self.lang)
if self.special_cases: tok.add_special_cases(self.special_cases)
return [self.process_text(str(t), tok) for t in texts]
def process_all(self, texts:Collection[str]) -> List[List[str]]:
"Process a list of `texts`."
if self.n_cpus <= 1: return self._process_all_1(texts)
with ProcessPoolExecutor(self.n_cpus) as e:
return sum(e.map(self._process_all_1, partition_by_cores(texts, self.n_cpus)), [])
class Vocab():
"Contain the correspondance between numbers and tokens and numericalize."
def __init__(self, itos:Collection[str]):
self.itos = itos
self.stoi = collections.defaultdict(int,{v:k for k,v in enumerate(self.itos)})
def numericalize(self, t:Collection[str]) -> List[int]:
"Convert a list of tokens `t` to their ids."
return [self.stoi[w] for w in t]
def textify(self, nums:Collection[int], sep=' ') -> List[str]:
"Convert a list of `nums` to their tokens."
return sep.join([self.itos[i] for i in nums])
def __getstate__(self):
return {'itos':self.itos}
def __setstate__(self, state:dict):
self.itos = state['itos']
self.stoi = collections.defaultdict(int,{v:k for k,v in enumerate(self.itos)})
@classmethod
def create(cls, tokens:Tokens, max_vocab:int, min_freq:int) -> 'Vocab':
"Create a vocabulary from a set of `tokens`."
freq = Counter(p for o in tokens for p in o)
itos = [o for o,c in freq.most_common(max_vocab) if c > min_freq]
for o in reversed(defaults.text_spec_tok):
if o in itos: itos.remove(o)
itos.insert(0, o)
return cls(itos)