update tokenizer
This commit is contained in:
thomwolf
@@ -16,21 +16,13 @@
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from __future__ import (absolute_import, division, print_function,
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unicode_literals)
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import sys
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import json
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import logging
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import os
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import regex as re
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from io import open
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import pdb
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try:
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from functools import lru_cache
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except ImportError:
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# Just a dummy decorator to get the checks to run on python2
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# because honestly I don't want to support a byte-level unicode BPE tokenizer on python 2 right now.
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def lru_cache():
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return lambda func: func
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from .tokenization_bert import BasicTokenizer
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from .tokenization_utils import PreTrainedTokenizer
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@@ -53,49 +45,47 @@ PRETRAINED_VOCAB_FILES_MAP = {
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}
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PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
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'ctrl': 1280,
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'ctrl': 256,
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}
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@lru_cache()
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def bytes_to_unicode():
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def text_standardize(text):
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"""
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Returns list of utf-8 byte and a mapping to unicode strings.
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We specifically avoids mapping to whitespace/control characters the bpe code barfs on.
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The reversible bpe codes work on unicode strings.
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This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
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When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
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This is a signficant percentage of your normal, say, 32K bpe vocab.
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To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
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fixes some issues the spacy tokenizer had on books corpus
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also does some whitespace standardization
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"""
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_chr = unichr if sys.version_info[0] == 2 else chr
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bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
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cs = bs[:]
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n = 0
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for b in range(2**8):
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if b not in bs:
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bs.append(b)
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cs.append(2**8+n)
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n += 1
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cs = [_chr(n) for n in cs]
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return dict(zip(bs, cs))
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text = text.replace('—', '-')
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text = text.replace('–', '-')
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text = text.replace('―', '-')
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text = text.replace('…', '...')
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text = text.replace('´', "'")
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text = re.sub(r'''(-+|~+|!+|"+|;+|\?+|\++|,+|\)+|\(+|\\+|\/+|\*+|\[+|\]+|}+|{+|\|+|_+)''', r' \1 ', text)
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text = re.sub(r'\s*\n\s*', ' \n ', text)
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text = re.sub(r'[^\S\n]+', ' ', text)
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return text.strip()
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def get_pairs(word):
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"""Return set of symbol pairs in a word.
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Word is represented as tuple of symbols (symbols being variable-length strings).
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"""
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pairs= []
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# pairs = []
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# prev_char = word[0]
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# for i, char in enumerate(word[1:]):
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# #_i = i + 1
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# #if word[_i+1:] == tuple('</w>'):
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# # pairs.append((prev_char, char+'</w>'))
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# # break
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# #else:
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# if True:
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# pairs.append((prev_char, char))
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# prev_char = char
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pairs = set()
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prev_char = word[0]
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for i, char in enumerate(word[1:]):
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#_i = i + 1
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#if word[_i+1:] == tuple('</w>'):
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# pairs.append((prev_char, char+'</w>'))
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# break
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#else:
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if True:
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pairs.append((prev_char, char))
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prev_char = char
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for char in word[1:]:
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pairs.add((prev_char, char))
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prev_char = char
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pairs = set(pairs)
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return pairs
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@@ -113,24 +103,28 @@ class CTRLTokenizer(PreTrainedTokenizer):
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pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
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max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
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def __init__(self, vocab_file, merges_file, errors='replace', unk_token="<unk>",
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bos_token="<|endoftext|>", eos_token="<|endoftext|>", **kwargs):
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super(CTRLTokenizer, self).__init__(bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, **kwargs)
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def __init__(self, vocab_file, merges_file, unk_token="<unk>", **kwargs):
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super(CTRLTokenizer, self).__init__(unk_token=unk_token, **kwargs)
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self.max_len_single_sentence = self.max_len # no default special tokens - you can update this value if you add special tokens
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self.max_len_sentences_pair = self.max_len # no default special tokens - you can update this value if you add special tokens
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self.encoder = json.load(open(vocab_file, encoding="utf-8"))
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self.decoder = {v: k for k, v in self.encoder.items()}
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self.errors = errors # how to handle errors in decoding
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self.byte_encoder = bytes_to_unicode()
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self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
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bpe_data = open(merges_file, encoding='utf-8').read().split('\n')[1:-1]
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bpe_merges = [tuple(merge.split()) for merge in bpe_data]
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self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
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self.cache = {}
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try:
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import ftfy
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from spacy.lang.en import English
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_nlp = English()
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self.nlp = _nlp.Defaults.create_tokenizer(_nlp)
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self.fix_text = ftfy.fix_text
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except ImportError:
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logger.warning("ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy.")
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self.nlp = BasicTokenizer(do_lower_case=True)
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self.fix_text = None
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# Should haved added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
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self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")
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self.encoder = json.load(open(vocab_file, encoding="utf-8"))
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self.decoder = {v:k for k,v in self.encoder.items()}
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merges = open(merges_file, encoding='utf-8').read().split('\n')[1:-1]
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merges = [tuple(merge.split()) for merge in merges]
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self.bpe_ranks = dict(zip(merges, range(len(merges))))
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self.cache = {}
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@property
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def vocab_size(self):
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@@ -179,23 +173,27 @@ class CTRLTokenizer(PreTrainedTokenizer):
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self.cache[token] = word
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return word
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def _tokenize(self, text, add_prefix_space=False):
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def _tokenize(self, text):
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""" Tokenize a string.
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Args:
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- add_prefix_space (boolean, default False):
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Begin the sentence with at least one space toto get invariance to word order in CTRL (and RoBERTa) tokenizers.
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"""
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if add_prefix_space:
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text = ' ' + text
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bpe_tokens = []
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for token in text.split():
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if sys.version_info[0] == 2:
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token = ''.join(self.byte_encoder[ord(b)] for b in token) # Maps all our bytes to unicode strings, avoiding controle tokens of the BPE (spaces in our case)
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else:
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token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8')) # Maps all our bytes to unicode strings, avoiding controle tokens of the BPE (spaces in our case)
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bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(' '))
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return bpe_tokens
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split_tokens = []
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if self.fix_text is None:
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# Using BERT's BasicTokenizer
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text = self.nlp.tokenize(text)
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for token in text:
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split_tokens.extend([t for t in self.bpe(token).split(' ')])
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else:
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# Using SpaCy & ftfy (original tokenization process of OpenAI GPT)
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text = self.nlp(text_standardize(self.fix_text(text)))
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for token in text:
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split_tokens.extend([t for t in self.bpe(token.text.lower()).split(' ')])
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# for token in text.split():
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# if sys.version_info[0] == 2:
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# token = ''.join(self.byte_encoder[ord(b)] for b in token) # Maps all our bytes to unicode strings, avoiding controle tokens of the BPE (spaces in our case)
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# else:
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# token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8')) # Maps all our bytes to unicode strings, avoiding controle tokens of the BPE (spaces in our case)
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# bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(' '))
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return split_tokens
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def _convert_token_to_id(self, token):
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""" Converts a token (str/unicode) in an id using the vocab. """
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@@ -203,13 +201,12 @@ class CTRLTokenizer(PreTrainedTokenizer):
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def _convert_id_to_token(self, index):
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"""Converts an index (integer) in a token (string/unicode) using the vocab."""
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return self.decoder.get(index)
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return self.decoder.get(index, self.unk_token)
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def convert_tokens_to_string(self, tokens):
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""" Converts a sequence of tokens (string) in a single string. """
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text = ''.join(tokens)
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text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors=self.errors)
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return text
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out_string = ''.join(tokens).replace('@@', ' ').strip()
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return out_string
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def save_vocabulary(self, save_directory):
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"""Save the tokenizer vocabulary and merge files to a directory."""
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@@ -235,10 +232,8 @@ class CTRLTokenizer(PreTrainedTokenizer):
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return vocab_file, merge_file
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def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
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filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
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tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
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tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
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return ''.join(tokens_generated_so_far)
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# def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
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# filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
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# tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
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# tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
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# return ''.join(tokens_generated_so_far)
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