paddlenlp.transformers.xlm.tokenizer 源代码

# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2019 The Open AI Team Authors and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import json
import os
import re
import shutil
import sys
import unicodedata
from typing import List, Optional

from ..tokenizer_utils import AddedToken, TextInput
from ...utils.log import logger
from paddle.utils import try_import

from .. import PretrainedTokenizer

__all__ = ["XLMTokenizer"]

PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
    "xlm-mlm-en-2048": 512,
    "xlm-mlm-ende-1024": 512,
    "xlm-mlm-enfr-1024": 512,
    "xlm-mlm-enro-1024": 512,
    "xlm-mlm-tlm-xnli15-1024": 512,
    "xlm-mlm-xnli15-1024": 512,
    "xlm-clm-enfr-1024": 512,
    "xlm-clm-ende-1024": 512,
    "xlm-mlm-17-1280": 512,
    "xlm-mlm-100-1280": 512,
}


def get_pairs(word):
    """
    Return set of symbol pairs in a word.
    Word is represented as tuple of symbols (symbols being variable-length strings).
    """
    pairs = set()
    prev_char = word[0]
    for char in word[1:]:
        pairs.add((prev_char, char))
        prev_char = char

    pairs = set(pairs)
    return pairs


def lowercase_and_remove_accent(text):
    """
    Lowercase and strips accents from a piece of text based on
    https://github.com/facebookresearch/XLM/blob/master/tools/lowercase_and_remove_accent.py
    """
    text = " ".join(text)
    text = text.lower()
    text = unicodedata.normalize("NFD", text)
    output = []
    for char in text:
        cat = unicodedata.category(char)
        if cat == "Mn":
            continue
        output.append(char)
    return "".join(output).lower().split(" ")


def replace_unicode_punct(text):
    """
    Port of https://github.com/moses-smt/mosesdecoder/blob/master/scripts/tokenizer/replace-unicode-punctuation.perl
    """
    text = text.replace(",", ",")
    text = re.sub(r"。\s*", ". ", text)
    text = text.replace("、", ",")
    text = text.replace("”", '"')
    text = text.replace("“", '"')
    text = text.replace("∶", ":")
    text = text.replace(":", ":")
    text = text.replace("?", "?")
    text = text.replace("《", '"')
    text = text.replace("》", '"')
    text = text.replace(")", ")")
    text = text.replace("!", "!")
    text = text.replace("(", "(")
    text = text.replace(";", ";")
    text = text.replace("1", "1")
    text = text.replace("」", '"')
    text = text.replace("「", '"')
    text = text.replace("0", "0")
    text = text.replace("3", "3")
    text = text.replace("2", "2")
    text = text.replace("5", "5")
    text = text.replace("6", "6")
    text = text.replace("9", "9")
    text = text.replace("7", "7")
    text = text.replace("8", "8")
    text = text.replace("4", "4")
    text = re.sub(r".\s*", ". ", text)
    text = text.replace("~", "~")
    text = text.replace("’", "'")
    text = text.replace("…", "...")
    text = text.replace("━", "-")
    text = text.replace("〈", "<")
    text = text.replace("〉", ">")
    text = text.replace("【", "[")
    text = text.replace("】", "]")
    text = text.replace("%", "%")
    return text


def remove_non_printing_char(text):
    """
    Port of https://github.com/moses-smt/mosesdecoder/blob/master/scripts/tokenizer/remove-non-printing-char.perl
    """
    output = []
    for char in text:
        cat = unicodedata.category(char)
        if cat.startswith("C"):
            continue
        output.append(char)
    return "".join(output)


def romanian_preprocessing(text):
    """Sennrich's WMT16 scripts for Romanian preprocessing, used by model `xlm-mlm-enro-1024`"""
    # https://github.com/rsennrich/wmt16-scripts/blob/master/preprocess/normalise-romanian.py
    text = text.replace("\u015e", "\u0218").replace("\u015f", "\u0219")
    text = text.replace("\u0162", "\u021a").replace("\u0163", "\u021b")
    # https://github.com/rsennrich/wmt16-scripts/blob/master/preprocess/remove-diacritics.py
    text = text.replace("\u0218", "S").replace("\u0219", "s")  # s-comma
    text = text.replace("\u021a", "T").replace("\u021b", "t")  # t-comma
    text = text.replace("\u0102", "A").replace("\u0103", "a")
    text = text.replace("\u00C2", "A").replace("\u00E2", "a")
    text = text.replace("\u00CE", "I").replace("\u00EE", "i")
    return text


[文档]class XLMTokenizer(PretrainedTokenizer): """ Construct an XLM tokenizer. Based on Byte-Pair Encoding. The tokenization process is the following: - Moses preprocessing and tokenization for most supported languages. - Language specific tokenization for Chinese (Jieba), Japanese (KyTea) and Thai (PyThaiNLP). - Optionally lowercases and normalizes all inputs text. - The arguments `special_tokens` and the function `set_special_tokens`, can be used to add additional symbols (like "__classify__") to a vocabulary. - The `lang2id` attribute maps the languages supported by the model with their IDs if provided (automatically set for pretrained vocabularies). - The `id2lang` attributes does reverse mapping if provided (automatically set for pretrained vocabularies). This tokenizer inherits from :class:`~paddlenlp.transformers.tokenizer_utils.PretrainedTokenizer`. Users should refer to this superclass for more information regarding those methods. Args: vocab_file (str): Vocabulary file. merges_file (str): Merges file. unk_token (str, optional): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token. <Tip> When building a sequence using special tokens, this is not the token that is used for the beginning of sequence. The token used is the `cls_token`. </Tip> Defaults to `"<unk>"`. sep_token (str, optional): The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens. Defaults to `"</s>"`. pad_token (str, optional): The token used for padding, for example when batching sequences of different lengths. Defaults to `"<pad>"`. cls_token (str, optional): The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens. Defaults to `"</s>"`. mask_token (str, optional): The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict. Defaults to `"<special1>"`. additional_special_tokens (List[str], optional): List of additional special tokens. Defaults to `["<special0>","<special1>","<special2>","<special3>","<special4>","<special5>","<special6>","<special7>","<special8>","<special9>"]`. lang2id (Dict[str, int], optional): Dictionary mapping languages string identifiers to their IDs. id2lang (Dict[int, str], optional): Dictionary mapping language IDs to their string identifiers. do_lowercase_and_remove_accent (bool, optional): Whether to lowercase and remove accents when tokenizing. Defaults to `True`. """ resource_files_names = { "vocab_file": "vocab.json", "merges_file": "merges.txt", } pretrained_resource_files_map = { "vocab_file": { "xlm-mlm-en-2048": "https://bj.bcebos.com/paddlenlp/models/transformers/xlm/xlm-mlm-en-2048/vocab.json", "xlm-mlm-ende-1024": "https://bj.bcebos.com/paddlenlp/models/transformers/xlm/xlm-mlm-ende-1024/vocab.json", "xlm-mlm-enfr-1024": "https://bj.bcebos.com/paddlenlp/models/transformers/xlm/xlm-mlm-enfr-1024/vocab.json", "xlm-mlm-enro-1024": "https://bj.bcebos.com/paddlenlp/models/transformers/xlm/xlm-mlm-enro-1024/vocab.json", "xlm-mlm-tlm-xnli15-1024": "https://bj.bcebos.com/paddlenlp/models/transformers/xlm/xlm-mlm-tlm-xnli15-1024/vocab.json", "xlm-mlm-xnli15-1024": "https://bj.bcebos.com/paddlenlp/models/transformers/xlm/xlm-mlm-xnli15-1024/vocab.json", "xlm-clm-enfr-1024": "https://bj.bcebos.com/paddlenlp/models/transformers/xlm/xlm-clm-enfr-1024/vocab.json", "xlm-clm-ende-1024": "https://bj.bcebos.com/paddlenlp/models/transformers/xlm/xlm-clm-ende-1024/vocab.json", "xlm-mlm-17-1280": "https://bj.bcebos.com/paddlenlp/models/transformers/xlm/xlm-mlm-17-1280/vocab.json", "xlm-mlm-100-1280": "https://bj.bcebos.com/paddlenlp/models/transformers/xlm/xlm-mlm-100-1280/vocab.json", }, "merges_file": { "xlm-mlm-en-2048": "https://bj.bcebos.com/paddlenlp/models/transformers/xlm/xlm-mlm-en-2048/merges.txt", "xlm-mlm-ende-1024": "https://bj.bcebos.com/paddlenlp/models/transformers/xlm/xlm-mlm-ende-1024/merges.txt", "xlm-mlm-enfr-1024": "https://bj.bcebos.com/paddlenlp/models/transformers/xlm/xlm-mlm-enfr-1024/merges.txt", "xlm-mlm-enro-1024": "https://bj.bcebos.com/paddlenlp/models/transformers/xlm/xlm-mlm-enro-1024/merges.txt", "xlm-mlm-tlm-xnli15-1024": "https://bj.bcebos.com/paddlenlp/models/transformers/xlm/xlm-mlm-tlm-xnli15-1024/merges.txt", "xlm-mlm-xnli15-1024": "https://bj.bcebos.com/paddlenlp/models/transformers/xlm/xlm-mlm-xnli15-1024/merges.txt", "xlm-clm-enfr-1024": "https://bj.bcebos.com/paddlenlp/models/transformers/xlm/xlm-clm-enfr-1024/merges.txt", "xlm-clm-ende-1024": "https://bj.bcebos.com/paddlenlp/models/transformers/xlm/xlm-clm-ende-1024/merges.txt", "xlm-mlm-17-1280": "https://bj.bcebos.com/paddlenlp/models/transformers/xlm/xlm-mlm-17-1280/merges.txt", "xlm-mlm-100-1280": "https://bj.bcebos.com/paddlenlp/models/transformers/xlm/xlm-mlm-100-1280/merges.txt", }, } pretrained_init_configuration = { "xlm-mlm-en-2048": {"do_lowercase_and_remove_accent": True}, "xlm-mlm-ende-1024": { "do_lowercase_and_remove_accent": True, "id2lang": {0: "de", 1: "en"}, "lang2id": {"de": 0, "en": 1}, }, "xlm-mlm-enfr-1024": { "do_lowercase_and_remove_accent": True, "id2lang": {0: "en", 1: "fr"}, "lang2id": {"en": 0, "fr": 1}, }, "xlm-mlm-enro-1024": { "do_lowercase_and_remove_accent": True, "id2lang": {0: "en", 1: "ro"}, "lang2id": {"en": 0, "ro": 1}, }, "xlm-mlm-tlm-xnli15-1024": { "do_lowercase_and_remove_accent": True, "id2lang": { 0: "ar", 1: "bg", 2: "de", 3: "el", 4: "en", 5: "es", 6: "fr", 7: "hi", 8: "ru", 9: "sw", 10: "th", 11: "tr", 12: "ur", 13: "vi", 14: "zh", }, "lang2id": { "ar": 0, "bg": 1, "de": 2, "el": 3, "en": 4, "es": 5, "fr": 6, "hi": 7, "ru": 8, "sw": 9, "th": 10, "tr": 11, "ur": 12, "vi": 13, "zh": 14, }, }, "xlm-mlm-xnli15-1024": { "do_lowercase_and_remove_accent": True, "id2lang": { 0: "ar", 1: "bg", 2: "de", 3: "el", 4: "en", 5: "es", 6: "fr", 7: "hi", 8: "ru", 9: "sw", 10: "th", 11: "tr", 12: "ur", 13: "vi", 14: "zh", }, "lang2id": { "ar": 0, "bg": 1, "de": 2, "el": 3, "en": 4, "es": 5, "fr": 6, "hi": 7, "ru": 8, "sw": 9, "th": 10, "tr": 11, "ur": 12, "vi": 13, "zh": 14, }, }, "xlm-clm-enfr-1024": { "do_lowercase_and_remove_accent": True, "id2lang": {0: "en", 1: "fr"}, "lang2id": {"en": 0, "fr": 1}, }, "xlm-clm-ende-1024": { "do_lowercase_and_remove_accent": True, "id2lang": {0: "de", 1: "en"}, "lang2id": {"de": 0, "en": 1}, }, "xlm-mlm-17-1280": { "do_lowercase_and_remove_accent": False, "id2lang": { 0: "ar", 1: "de", 2: "en", 3: "es", 4: "fr", 5: "hi", 6: "it", 7: "ja", 8: "ko", 9: "nl", 10: "pl", 11: "pt", 12: "ru", 13: "sv", 14: "tr", 15: "vi", 16: "zh", }, "lang2id": { "ar": 0, "de": 1, "en": 2, "es": 3, "fr": 4, "hi": 5, "it": 6, "ja": 7, "ko": 8, "nl": 9, "pl": 10, "pt": 11, "ru": 12, "sv": 13, "tr": 14, "vi": 15, "zh": 16, }, }, "xlm-mlm-100-1280": { "do_lowercase_and_remove_accent": False, "id2lang": { 0: "af", 1: "als", 2: "am", 3: "an", 4: "ang", 5: "ar", 6: "arz", 7: "ast", 8: "az", 9: "bar", 10: "be", 11: "bg", 12: "bn", 13: "br", 14: "bs", 15: "ca", 16: "ceb", 17: "ckb", 18: "cs", 19: "cy", 20: "da", 21: "de", 22: "el", 23: "en", 24: "eo", 25: "es", 26: "et", 27: "eu", 28: "fa", 29: "fi", 30: "fr", 31: "fy", 32: "ga", 33: "gan", 34: "gl", 35: "gu", 36: "he", 37: "hi", 38: "hr", 39: "hu", 40: "hy", 41: "ia", 42: "id", 43: "is", 44: "it", 45: "ja", 46: "jv", 47: "ka", 48: "kk", 49: "kn", 50: "ko", 51: "ku", 52: "la", 53: "lb", 54: "lt", 55: "lv", 56: "mk", 57: "ml", 58: "mn", 59: "mr", 60: "ms", 61: "my", 62: "nds", 63: "ne", 64: "nl", 65: "nn", 66: "no", 67: "oc", 68: "pl", 69: "pt", 70: "ro", 71: "ru", 72: "scn", 73: "sco", 74: "sh", 75: "si", 76: "simple", 77: "sk", 78: "sl", 79: "sq", 80: "sr", 81: "sv", 82: "sw", 83: "ta", 84: "te", 85: "th", 86: "tl", 87: "tr", 88: "tt", 89: "uk", 90: "ur", 91: "uz", 92: "vi", 93: "war", 94: "wuu", 95: "yi", 96: "zh", 97: "zh_classical", 98: "zh_min_nan", 99: "zh_yue", }, "lang2id": { "af": 0, "als": 1, "am": 2, "an": 3, "ang": 4, "ar": 5, "arz": 6, "ast": 7, "az": 8, "bar": 9, "be": 10, "bg": 11, "bn": 12, "br": 13, "bs": 14, "ca": 15, "ceb": 16, "ckb": 17, "cs": 18, "cy": 19, "da": 20, "de": 21, "el": 22, "en": 23, "eo": 24, "es": 25, "et": 26, "eu": 27, "fa": 28, "fi": 29, "fr": 30, "fy": 31, "ga": 32, "gan": 33, "gl": 34, "gu": 35, "he": 36, "hi": 37, "hr": 38, "hu": 39, "hy": 40, "ia": 41, "id": 42, "is": 43, "it": 44, "ja": 45, "jv": 46, "ka": 47, "kk": 48, "kn": 49, "ko": 50, "ku": 51, "la": 52, "lb": 53, "lt": 54, "lv": 55, "mk": 56, "ml": 57, "mn": 58, "mr": 59, "ms": 60, "my": 61, "nds": 62, "ne": 63, "nl": 64, "nn": 65, "no": 66, "oc": 67, "pl": 68, "pt": 69, "ro": 70, "ru": 71, "scn": 72, "sco": 73, "sh": 74, "si": 75, "simple": 76, "sk": 77, "sl": 78, "sq": 79, "sr": 80, "sv": 81, "sw": 82, "ta": 83, "te": 84, "th": 85, "tl": 86, "tr": 87, "tt": 88, "uk": 89, "ur": 90, "uz": 91, "vi": 92, "war": 93, "wuu": 94, "yi": 95, "zh": 96, "zh_classical": 97, "zh_min_nan": 98, "zh_yue": 99, }, }, } max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES def __init__( self, vocab_file, merges_file, unk_token="<unk>", bos_token="<s>", sep_token="</s>", pad_token="<pad>", cls_token="</s>", mask_token="<special1>", additional_special_tokens=[ "<special0>", "<special1>", "<special2>", "<special3>", "<special4>", "<special5>", "<special6>", "<special7>", "<special8>", "<special9>", ], lang2id=None, id2lang=None, do_lowercase_and_remove_accent=True, **kwargs, ): self._vocab_file = vocab_file self._merges_file = merges_file self.sm = try_import("sacremoses") # cache of sm.MosesPunctNormalizer instance self.cache_moses_punct_normalizer = dict() # cache of sm.MosesTokenizer instance self.cache_moses_tokenizer = dict() self.lang_with_custom_tokenizer = set(["zh", "th", "ja"]) # True for current supported model (v1.2.0), False for XLM-17 & 100 self.do_lowercase_and_remove_accent = do_lowercase_and_remove_accent self.lang2id = lang2id self.id2lang = id2lang if lang2id is not None and id2lang is not None: assert len(lang2id) == len(id2lang) self.ja_word_tokenizer = None self.zh_word_tokenizer = None with open(vocab_file, encoding="utf-8") as vocab_handle: self.encoder = json.load(vocab_handle) self.decoder = {v: k for k, v in self.encoder.items()} with open(merges_file, encoding="utf-8") as merges_handle: merges = merges_handle.read().split("\n")[:-1] merges = [tuple(merge.split()[:2]) for merge in merges] self.bpe_ranks = dict(zip(merges, range(len(merges)))) self.cache = {} @property def do_lower_case(self): return self.do_lowercase_and_remove_accent def moses_punct_norm(self, text, lang): if lang not in self.cache_moses_punct_normalizer: punct_normalizer = self.sm.MosesPunctNormalizer(lang=lang) self.cache_moses_punct_normalizer[lang] = punct_normalizer else: punct_normalizer = self.cache_moses_punct_normalizer[lang] return punct_normalizer.normalize(text) def moses_tokenize(self, text, lang): if lang not in self.cache_moses_tokenizer: moses_tokenizer = self.sm.MosesTokenizer(lang=lang) self.cache_moses_tokenizer[lang] = moses_tokenizer else: moses_tokenizer = self.cache_moses_tokenizer[lang] return moses_tokenizer.tokenize(text, return_str=False, escape=False) def moses_pipeline(self, text, lang): text = replace_unicode_punct(text) text = self.moses_punct_norm(text, lang) text = remove_non_printing_char(text) return text
[文档] def ja_tokenize(self, text): """Tokenize a Japanese string.""" if self.ja_word_tokenizer is None: try: import Mykytea self.ja_word_tokenizer = Mykytea.Mykytea( f"-model {os.path.expanduser('~')}/local/share/kytea/model.bin" ) except (AttributeError, ImportError): logger.error( "Make sure you install KyTea (https://github.com/neubig/kytea) and it's python wrapper (https://github.com/chezou/Mykytea-python) with the following steps" ) logger.error("1. git clone git@github.com:neubig/kytea.git && cd kytea") logger.error("2. autoreconf -i") logger.error("3. ./configure --prefix=$HOME/local") logger.error("4. make && make install") logger.error("5. pip install kytea") raise return list(self.ja_word_tokenizer.getWS(text))
@property def vocab_size(self): return len(self.encoder)
[文档] def get_vocab(self): return dict(self.encoder, **self.added_tokens_encoder)
def __len__(self): return len(self.encoder) def bpe(self, token): word = tuple(token[:-1]) + (token[-1] + "</w>",) if token in self.cache: return self.cache[token] pairs = get_pairs(word) if not pairs: return token + "</w>" while True: bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf"))) if bigram not in self.bpe_ranks: break first, second = bigram new_word = [] i = 0 while i < len(word): try: j = word.index(first, i) except ValueError: new_word.extend(word[i:]) break else: new_word.extend(word[i:j]) i = j if word[i] == first and i < len(word) - 1 and word[i + 1] == second: new_word.append(first + second) i += 2 else: new_word.append(word[i]) i += 1 new_word = tuple(new_word) word = new_word if len(word) == 1: break else: pairs = get_pairs(word) word = " ".join(word) if word == "\n </w>": word = "\n</w>" self.cache[token] = word return word
[文档] def tokenize(self, text: TextInput, **kwargs) -> List[str]: """ Converts a string in a sequence of tokens, using the tokenizer. Split in words for word-based vocabulary or sub-words for sub-word-based vocabularies (BPE/SentencePieces/WordPieces). Takes care of added tokens. Args: text (`str`): The sequence to be encoded. **kwargs (additional keyword arguments): Passed along to the model-specific `prepare_for_tokenization` preprocessing method. Returns: `List[str]`: The list of tokens. """ # Simple mapping string => AddedToken for special tokens with specific tokenization behaviors all_special_tokens_extended = dict( (str(t), t) for t in self.all_special_tokens_extended if isinstance(t, AddedToken) ) text, kwargs = self.prepare_for_tokenization(text, **kwargs) # TODO: should this be in the base class? if hasattr(self, "do_lower_case") and self.do_lower_case: # convert non-special tokens to lowercase escaped_special_toks = [ re.escape(s_tok) for s_tok in (self.unique_no_split_tokens + self.all_special_tokens) ] pattern = r"(" + r"|".join(escaped_special_toks) + r")|" + r"(.+?)" text = re.sub(pattern, lambda m: m.groups()[0] or m.groups()[1].lower(), text) no_split_token = set(self.unique_no_split_tokens) tokens = self.tokens_trie.split(text) # ["This is something", "<special_token_1>", " else"] for i, token in enumerate(tokens): if token in no_split_token: tok_extended = all_special_tokens_extended.get(token, None) left = tokens[i - 1] if i > 0 else None right = tokens[i + 1] if i < len(tokens) - 1 else None if isinstance(tok_extended, AddedToken): if tok_extended.rstrip and right: # A bit counter-intuitive but we strip the left of the string # since tok_extended.rstrip means the special token is eating all white spaces on its right tokens[i + 1] = right.lstrip() # Strip white spaces on the left if tok_extended.lstrip and left: tokens[i - 1] = left.rstrip() # Opposite here else: # We strip left and right by default if right: tokens[i + 1] = right.lstrip() if left: tokens[i - 1] = left.rstrip() # ["This is something", "<special_token_1>", "else"] tokenized_text = [] lang = kwargs.pop("lang", "en") bypass_tokenizer = kwargs.pop("bypass_tokenizer", False) for token in tokens: # Need to skip eventual empty (fully stripped) tokens if not token: continue if token in no_split_token: tokenized_text.append(token) else: tokenized_text.extend(self._tokenize(token, lang=lang, bypass_tokenizer=bypass_tokenizer)) # ["This", " is", " something", "<special_token_1>", "else"] return tokenized_text
def _tokenize(self, text, lang="en", bypass_tokenizer=False): """ Tokenize a string given language code. For Chinese, Japanese and Thai, we use a language specific tokenizer. Otherwise, we use Moses. Details of tokenization: - [sacremoses](https://github.com/alvations/sacremoses): port of Moses - Install with `pip install sacremoses` - [pythainlp](https://github.com/PyThaiNLP/pythainlp): Thai tokenizer - Install with `pip install pythainlp` - [kytea](https://github.com/chezou/Mykytea-python): Japanese tokenizer, wrapper of [KyTea](https://github.com/neubig/kytea) - Install with the following steps: :: git clone git@github.com:neubig/kytea.git && cd kytea autoreconf -i ./configure --prefix=$HOME/local make && make install pip install kytea - [jieba](https://github.com/fxsjy/jieba): Chinese tokenizer (*) - Install with `pip install jieba` (*) The original XLM used [Stanford Segmenter](https://nlp.stanford.edu/software/stanford-segmenter-2018-10-16.zip). However, the wrapper (`nltk.tokenize.stanford_segmenter`) is slow due to JVM overhead, and it will be deprecated. Jieba is a lot faster and pip-installable. Note there is some mismatch with the Stanford Segmenter. It should be fine if you fine-tune the model with Chinese supervisionself. If you want the same exact behaviour, use the original XLM [preprocessing script](https://github.com/facebookresearch/XLM/tree/master/tools) to tokenize the sentence externally, and set `bypass_tokenizer=True` to bypass the tokenizer. Args: - lang: ISO language code (default = 'en') (string). Languages should belong of the model supported languages. However, we don't enforce it. - bypass_tokenizer: Allow users to preprocess and tokenize the sentences externally (default = False) (bool). If True, we only apply BPE. Returns: List of tokens. """ if lang and self.lang2id and lang not in self.lang2id: logger.error( "Supplied language code not found in lang2id mapping. Please check that your language is supported by the loaded pretrained model." ) if bypass_tokenizer: text = text.split() elif lang not in self.lang_with_custom_tokenizer: text = self.moses_pipeline(text, lang=lang) # TODO: make sure we are using `xlm-mlm-enro-1024`, since XLM-100 doesn't have this step if lang == "ro": text = romanian_preprocessing(text) text = self.moses_tokenize(text, lang=lang) elif lang == "th": text = self.moses_pipeline(text, lang=lang) try: if "pythainlp" not in sys.modules: from pythainlp.tokenize import word_tokenize as th_word_tokenize else: th_word_tokenize = sys.modules["pythainlp"].word_tokenize except (AttributeError, ImportError): logger.error( "Make sure you install PyThaiNLP (https://github.com/PyThaiNLP/pythainlp) with the following steps" ) logger.error("1. pip install pythainlp") raise text = th_word_tokenize(text) elif lang == "zh": try: if "jieba" not in sys.modules: import jieba else: jieba = sys.modules["jieba"] except (AttributeError, ImportError): logger.error("Make sure you install Jieba (https://github.com/fxsjy/jieba) with the following steps") logger.error("1. pip install jieba") raise text = " ".join(jieba.cut(text)) text = self.moses_pipeline(text, lang=lang) text = text.split() elif lang == "ja": text = self.moses_pipeline(text, lang=lang) text = self.ja_tokenize(text) else: raise ValueError("It should not reach here") if self.do_lowercase_and_remove_accent and not bypass_tokenizer: text = lowercase_and_remove_accent(text) split_tokens = [] for token in text: if token: split_tokens.extend([t for t in self.bpe(token).split(" ")]) return split_tokens def _convert_token_to_id(self, token): """Converts a token (str) in an id using the vocab.""" return self.encoder.get(token, self.encoder.get(self.unk_token)) def _convert_id_to_token(self, index): """Converts an index (integer) in a token (str) using the vocab.""" return self.decoder.get(index, self.unk_token)
[文档] def convert_tokens_to_string(self, tokens): """Converts a sequence of tokens (string) in a single string.""" out_string = "".join(tokens).replace("</w>", " ").strip() return out_string
[文档] def build_inputs_with_special_tokens( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. An XLM sequence has the following format: - single sequence: `<s> X </s>` - pair of sequences: `<s> A </s> B </s>` Args: token_ids_0 (`List[int]`): List of IDs to which the special tokens will be added. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: The model input with special tokens. """ bos = [self.bos_token_id] sep = [self.sep_token_id] if token_ids_1 is None: return bos + token_ids_0 + sep return bos + token_ids_0 + sep + token_ids_1 + sep
[文档] def create_token_type_ids_from_sequences( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Create a mask from the two sequences passed to be used in a sequence-pair classification task. An XLM sequence pair mask has the following format: ``` 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 | first sequence | second sequence | ``` If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s). Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of [token type IDs] according to the given sequence(s). """ sep = [self.sep_token_id] cls = [self.cls_token_id] if token_ids_1 is None: return len(cls + token_ids_0 + sep) * [0] return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]
[文档] def get_special_tokens_mask( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False, ) -> List[int]: """ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer `prepare_for_model` method. Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. already_has_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not the token list is already formatted with special tokens for the model. Returns: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. """ if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True, ) if token_ids_1 is not None: return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1] return [1] + ([0] * len(token_ids_0)) + [1]
[文档] def save_resources(self, save_directory): """ Save tokenizer related resources to files under `save_directory`. Args: save_directory (str): Directory to save files into. """ for name, file_name in self.resource_files_names.items(): source_path = getattr(self, "_%s" % name) save_path = os.path.join(save_directory, file_name) if os.path.abspath(source_path) != os.path.abspath(save_path): shutil.copyfile(source_path, save_path)