Source code for transformers.tokenization_transfo_xl

# coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
#
# 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.
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# limitations under the License.
""" Tokenization classes for Transformer XL model.
    Adapted from https://github.com/kimiyoung/transformer-xl.
"""


import glob
import logging
import os
import pickle
import re
from collections import Counter, OrderedDict
from typing import Optional

import numpy as np
from tokenizers import Tokenizer
from tokenizers.implementations import BaseTokenizer
from tokenizers.models import WordLevel
from tokenizers.normalizers import Lowercase, Sequence, Strip, unicode_normalizer_from_str
from tokenizers.pre_tokenizers import CharDelimiterSplit, WhitespaceSplit
from tokenizers.processors import BertProcessing

from .file_utils import cached_path, is_torch_available
from .tokenization_utils import PreTrainedTokenizer
from .tokenization_utils_fast import PreTrainedTokenizerFast


if is_torch_available():
    import torch


logger = logging.getLogger(__name__)

VOCAB_FILES_NAMES = {"pretrained_vocab_file": "vocab.bin", "vocab_file": "vocab.txt"}
VOCAB_FILES_NAMES_FAST = {"pretrained_vocab_file": "vocab.json", "vocab_file": "vocab.json"}

PRETRAINED_VOCAB_FILES_MAP = {
    "pretrained_vocab_file": {
        "transfo-xl-wt103": "https://s3.amazonaws.com/models.huggingface.co/bert/transfo-xl-wt103-vocab.bin",
    }
}

PRETRAINED_VOCAB_FILES_MAP_FAST = {
    "pretrained_vocab_file": {
        "transfo-xl-wt103": "https://s3.amazonaws.com/models.huggingface.co/bert/transfo-xl-wt103-vocab.json",
    }
}

PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
    "transfo-xl-wt103": None,
}

PRETRAINED_CORPUS_ARCHIVE_MAP = {
    "transfo-xl-wt103": "https://s3.amazonaws.com/models.huggingface.co/bert/transfo-xl-wt103-corpus.bin",
}
CORPUS_NAME = "corpus.bin"


[docs]class TransfoXLTokenizer(PreTrainedTokenizer): """ Transformer-XL tokenizer adapted from Vocab class in https://github.com/kimiyoung/transformer-xl This tokenizer inherits from :class:`~transformers.PreTrainedTokenizer` which contains most of the methods. Users should refer to the superclass for more information regarding methods. """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES model_input_names = [] def __init__( self, special=None, min_freq=0, max_size=None, lower_case=False, delimiter=None, vocab_file=None, pretrained_vocab_file=None, never_split=None, unk_token="<unk>", eos_token="<eos>", additional_special_tokens=["<formula>"], **kwargs ): super().__init__( unk_token=unk_token, eos_token=eos_token, additional_special_tokens=additional_special_tokens, **kwargs ) if never_split is None: never_split = self.all_special_tokens if special is None: special = [] self.counter = Counter() self.special = special self.min_freq = min_freq self.max_size = max_size self.lower_case = lower_case self.delimiter = delimiter self.vocab_file = vocab_file self.never_split = never_split self.punctuation_symbols = '!"#$%&()*+,-./\\:;<=>?@[\\]^_`{|}~' self.punction_without_space_before_pattern = re.compile(r"[^\s][{}]".format(self.punctuation_symbols)) self.punctuation_with_space_around_pattern = self._compile_space_around_punctuation_pattern() try: if pretrained_vocab_file is not None: # Hack because, honestly this tokenizer was not made to be used # in a library like ours, at all. vocab_dict = torch.load(pretrained_vocab_file) for key, value in vocab_dict.items(): if key not in self.__dict__: self.__dict__[key] = value if vocab_file is not None: self.build_vocab() except Exception: raise ValueError( "Unable to parse file {}. Unknown format. " "If you tried to load a model saved through TransfoXLTokenizerFast," "please note they are not compatible.".format(pretrained_vocab_file) ) if vocab_file is not None: self.build_vocab() def _compile_space_around_punctuation_pattern(self): look_ahead_for_special_token = "(?=[{}])".format(self.punctuation_symbols) look_ahead_to_match_all_except_space = r"(?=[^\s])" return re.compile(r"" + look_ahead_for_special_token + look_ahead_to_match_all_except_space) def count_file(self, path, verbose=False, add_eos=False): if verbose: logger.info("counting file {} ...".format(path)) assert os.path.exists(path) sents = [] with open(path, "r", encoding="utf-8") as f: for idx, line in enumerate(f): if verbose and idx > 0 and idx % 500000 == 0: logger.info(" line {}".format(idx)) symbols = self.tokenize(line, add_eos=add_eos) self.counter.update(symbols) sents.append(symbols) return sents def count_sents(self, sents, verbose=False): """ sents : a list of sentences, each a list of tokenized symbols """ if verbose: logger.info("counting {} sents ...".format(len(sents))) for idx, symbols in enumerate(sents): if verbose and idx > 0 and idx % 500000 == 0: logger.info(" line {}".format(idx)) self.counter.update(symbols) def _build_from_file(self, vocab_file): self.idx2sym = [] self.sym2idx = OrderedDict() with open(vocab_file, "r", encoding="utf-8") as f: for line in f: symb = line.strip().split()[0] self.add_symbol(symb) if "<UNK>" in self.sym2idx: self.unk_idx = self.sym2idx["<UNK>"] elif "<unk>" in self.sym2idx: self.unk_idx = self.sym2idx["<unk>"] else: raise ValueError("No <unkown> token in vocabulary")
[docs] def save_vocabulary(self, vocab_path): """ Save the vocabulary and special tokens file to a directory. Args: vocab_path (:obj:`str`): The directory in which to save the vocabulary. Returns: :obj:`Tuple(str)`: Paths to the files saved. """ logger.warning( "Please note you will not be able to load the save vocabulary in" " Rust-based TransfoXLTokenizerFast as they don't share the same structure." ) if os.path.isdir(vocab_path): vocab_file = os.path.join(vocab_path, VOCAB_FILES_NAMES["pretrained_vocab_file"]) else: vocab_file = vocab_path torch.save(self.__dict__, vocab_file) return (vocab_file,)
def build_vocab(self): if self.vocab_file: logger.info("building vocab from {}".format(self.vocab_file)) self._build_from_file(self.vocab_file) logger.info("final vocab size {}".format(len(self))) else: logger.info("building vocab with min_freq={}, max_size={}".format(self.min_freq, self.max_size)) self.idx2sym = [] self.sym2idx = OrderedDict() for sym in self.special: self.add_special(sym) for sym, cnt in self.counter.most_common(self.max_size): if cnt < self.min_freq: break self.add_symbol(sym) logger.info("final vocab size {} from {} unique tokens".format(len(self), len(self.counter))) def encode_file(self, path, ordered=False, verbose=False, add_eos=True, add_double_eos=False): if verbose: logger.info("encoding file {} ...".format(path)) assert os.path.exists(path) encoded = [] with open(path, "r", encoding="utf-8") as f: for idx, line in enumerate(f): if verbose and idx > 0 and idx % 500000 == 0: logger.info(" line {}".format(idx)) symbols = self.tokenize(line, add_eos=add_eos, add_double_eos=add_double_eos) encoded.append(self.convert_to_tensor(symbols)) if ordered: encoded = torch.cat(encoded) return encoded def encode_sents(self, sents, ordered=False, verbose=False): if verbose: logger.info("encoding {} sents ...".format(len(sents))) encoded = [] for idx, symbols in enumerate(sents): if verbose and idx > 0 and idx % 500000 == 0: logger.info(" line {}".format(idx)) encoded.append(self.convert_to_tensor(symbols)) if ordered: encoded = torch.cat(encoded) return encoded def add_special(self, sym): if sym not in self.sym2idx: self.idx2sym.append(sym) self.sym2idx[sym] = len(self.idx2sym) - 1 setattr(self, "{}_idx".format(sym.strip("<>")), self.sym2idx[sym]) def add_symbol(self, sym): if sym not in self.sym2idx: self.idx2sym.append(sym) self.sym2idx[sym] = len(self.idx2sym) - 1 def move_added_token(self, token: str, target_idx: int): """ Moves an added token to a specific position in the vocab. This method should be used when resizing an embedding layer other than the last one in the `AdaptiveEmbedding` in order to move the token in the tokenizer from the default position (at the very end) to the desired one. Args: token: The token to move to a specific position in the vocab. target_idx: The position where the token should be moved to. """ assert token in self.added_tokens_encoder, "Token which should be moved has to be an added token" assert token not in self.idx2sym, "Token which should be moved is already in vocab" # Insert sym into vocab self.idx2sym.insert(target_idx, token) self.sym2idx[token] = target_idx # Shift following indices in sym2idx for idx in range(target_idx + 1, len(self.idx2sym)): current_sym = self.idx2sym[idx] self.sym2idx[current_sym] = idx # Delete token from added_tokens old_index = self.added_tokens_encoder[token] del self.added_tokens_decoder[old_index] del self.added_tokens_encoder[token] def _convert_id_to_token(self, idx): """Converts an id in a token (BPE) using the vocab.""" assert 0 <= idx < len(self), "Index {} out of vocabulary range".format(idx) return self.idx2sym[idx] def _convert_token_to_id(self, sym): """ Converts a token (str) in an id using the vocab. """ if sym in self.sym2idx: return self.sym2idx[sym] else: # logger.info('encounter unk {}'.format(sym)) # assert '<eos>' not in sym if hasattr(self, "unk_idx"): return self.sym2idx.get(sym, self.unk_idx) # Backward compatibility with pre-trained models elif "<unk>" in self.sym2idx: return self.sym2idx["<unk>"] elif "<UNK>" in self.sym2idx: return self.sym2idx["<UNK>"] else: raise ValueError("Token not in vocabulary and no <unk> token in vocabulary for replacement") def convert_tokens_to_string(self, tokens): """ Converts a sequence of tokens (string) in a single string. """ out_string = " ".join(tokens).strip() return out_string def convert_to_tensor(self, symbols): return torch.LongTensor(self.convert_tokens_to_ids(symbols)) @property def vocab_size(self): return len(self.idx2sym) def get_vocab(self): return dict(self.sym2idx, **self.added_tokens_encoder) def _tokenize(self, line, add_eos=False, add_double_eos=False): line = line.strip() # convert to lower case if self.lower_case: line = line.lower() # empty delimiter '' will evaluate False if self.delimiter == "": symbols = line else: symbols = line.split(self.delimiter) if add_double_eos: # lm1b return ["<S>"] + symbols + ["<S>"] elif add_eos: return symbols + ["<eos>"] else: return symbols def prepare_for_tokenization(self, text, is_pretokenized=False, **kwargs): # add spaces before punctuation symbols as should be done in transfo-xl add_space_before_punct_symbol = kwargs.pop("add_space_before_punct_symbol", False) if add_space_before_punct_symbol: text = self.punctuation_with_space_around_pattern.sub(r" ", text) elif self.punction_without_space_before_pattern.search(text): # searches until the first occurence of a punctuation symbol without surrounding spaces logger.warning( "You might want to consider setting `add_space_before_punct_symbol=True` as an argument to the `tokenizer.encode()` to avoid tokenizing words with punctuation symbols to the `<unk>` token" ) return (text, kwargs)
class _TransfoXLDelimiterLookupTokenizer(BaseTokenizer): def __init__( self, vocab_file, delimiter, lowercase, unk_token, eos_token, add_eos=False, add_double_eos=False, normalization: Optional[str] = None, ): try: tokenizer = WordLevel(vocab_file, unk_token=unk_token) tokenizer = Tokenizer(tokenizer) except Exception: raise ValueError( "Unable to parse file {}. Unknown format. " "If you tried to load a model saved through TransfoXLTokenizer," "please note they are not compatible.".format(vocab_file) ) # Create the correct normalization path normalizer = [] # Include unicode normalization if normalization: normalizer += [unicode_normalizer_from_str(normalization)] # Include case normalization if lowercase: normalizer += [Lowercase()] # Strip normalizer at the end normalizer += [Strip(left=True, right=True)] if len(normalizer) > 0: tokenizer.normalizer = Sequence(normalizer) if len(normalizer) > 1 else normalizer[0] # Setup the splitter tokenizer.pre_tokenizer = CharDelimiterSplit(delimiter) if delimiter else WhitespaceSplit() if add_double_eos: tokenizer.post_processor = BertProcessing( (eos_token, tokenizer.token_to_id(eos_token)), (eos_token, tokenizer.token_to_id(eos_token)) ) parameters = { "model": "TransfoXLModel", "add_eos": add_eos, "add_double_eos": add_double_eos, "unk_token": unk_token, "eos_token": eos_token, "delimiter": delimiter, "lowercase": lowercase, } super().__init__(tokenizer, parameters)
[docs]class TransfoXLTokenizerFast(PreTrainedTokenizerFast): """ Construct a "Fast" Transformer-XL tokenizer (backed by HuggingFace's `tokenizers` library). The Transformer-XL tokenizer is a word-level tokenizer (no sub-word tokenization). Adapted from Vocab class in https://github.com/kimiyoung/transformer-xl This tokenizer inherits from :class:`~transformers.PreTrainedTokenizerFast` which contains most of the methods. Users should refer to the superclass for more information regarding methods. """ vocab_files_names = VOCAB_FILES_NAMES_FAST pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP_FAST max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES model_input_names = [] def __init__( self, special=None, min_freq=0, max_size=None, lower_case=False, delimiter=None, vocab_file=None, pretrained_vocab_file=None, never_split=None, unk_token="<unk>", eos_token="<eos>", additional_special_tokens=["<formula>"], add_eos=False, add_double_eos=False, normalization=None, **kwargs ): super().__init__( _TransfoXLDelimiterLookupTokenizer( vocab_file=vocab_file or pretrained_vocab_file, delimiter=delimiter, lowercase=lower_case, unk_token=unk_token, eos_token=eos_token, add_eos=add_eos, add_double_eos=add_double_eos, normalization=normalization, ), unk_token=unk_token, eos_token=eos_token, additional_special_tokens=additional_special_tokens, **kwargs, )
[docs] def save_pretrained(self, save_directory): logger.warning( "Please note you will not be able to load the vocabulary in" " Python-based TransfoXLTokenizer as they don't share the same structure." ) return super().save_pretrained(save_directory)
class LMOrderedIterator(object): def __init__(self, data, bsz, bptt, device="cpu", ext_len=None): """ data -- LongTensor -- the LongTensor is strictly ordered """ self.bsz = bsz self.bptt = bptt self.ext_len = ext_len if ext_len is not None else 0 self.device = device # Work out how cleanly we can divide the dataset into bsz parts. self.n_step = data.size(0) // bsz # Trim off any extra elements that wouldn't cleanly fit (remainders). data = data.narrow(0, 0, self.n_step * bsz) # Evenly divide the data across the bsz batches. self.data = data.view(bsz, -1).t().contiguous().to(device) # Number of mini-batches self.n_batch = (self.n_step + self.bptt - 1) // self.bptt def get_batch(self, i, bptt=None): if bptt is None: bptt = self.bptt seq_len = min(bptt, self.data.size(0) - 1 - i) end_idx = i + seq_len beg_idx = max(0, i - self.ext_len) data = self.data[beg_idx:end_idx] target = self.data[i + 1 : i + 1 + seq_len] data_out = data.transpose(0, 1).contiguous().to(self.device) target_out = target.transpose(0, 1).contiguous().to(self.device) return data_out, target_out, seq_len def get_fixlen_iter(self, start=0): for i in range(start, self.data.size(0) - 1, self.bptt): yield self.get_batch(i) def get_varlen_iter(self, start=0, std=5, min_len=5, max_deviation=3): max_len = self.bptt + max_deviation * std i = start while True: bptt = self.bptt if np.random.random() < 0.95 else self.bptt / 2.0 bptt = min(max_len, max(min_len, int(np.random.normal(bptt, std)))) data, target, seq_len = self.get_batch(i, bptt) i += seq_len yield data, target, seq_len if i >= self.data.size(0) - 2: break def __iter__(self): return self.get_fixlen_iter() class LMShuffledIterator(object): def __init__(self, data, bsz, bptt, device="cpu", ext_len=None, shuffle=False): """ data -- list[LongTensor] -- there is no order among the LongTensors """ self.data = data self.bsz = bsz self.bptt = bptt self.ext_len = ext_len if ext_len is not None else 0 self.device = device self.shuffle = shuffle def get_sent_stream(self): # index iterator epoch_indices = np.random.permutation(len(self.data)) if self.shuffle else np.array(range(len(self.data))) # sentence iterator for idx in epoch_indices: yield self.data[idx] def stream_iterator(self, sent_stream): # streams for each data in the batch streams = [None] * self.bsz data = torch.LongTensor(self.bptt, self.bsz) target = torch.LongTensor(self.bptt, self.bsz) n_retain = 0 while True: # data : [n_retain+bptt x bsz] # target : [bptt x bsz] data[n_retain:].fill_(-1) target.fill_(-1) valid_batch = True for i in range(self.bsz): n_filled = 0 try: while n_filled < self.bptt: if streams[i] is None or len(streams[i]) <= 1: streams[i] = next(sent_stream) # number of new tokens to fill in n_new = min(len(streams[i]) - 1, self.bptt - n_filled) # first n_retain tokens are retained from last batch data[n_retain + n_filled : n_retain + n_filled + n_new, i] = streams[i][:n_new] target[n_filled : n_filled + n_new, i] = streams[i][1 : n_new + 1] streams[i] = streams[i][n_new:] n_filled += n_new except StopIteration: valid_batch = False break if not valid_batch: return data_out = data.transpose(0, 1).contiguous().to(self.device) target_out = target.transpose(0, 1).contiguous().to(self.device) yield data_out, target_out, self.bptt n_retain = min(data.size(0), self.ext_len) if n_retain > 0: data[:n_retain] = data[-n_retain:] data.resize_(n_retain + self.bptt, data.size(1)) def __iter__(self): # sent_stream is an iterator sent_stream = self.get_sent_stream() for batch in self.stream_iterator(sent_stream): yield batch class LMMultiFileIterator(LMShuffledIterator): def __init__(self, paths, vocab, bsz, bptt, device="cpu", ext_len=None, shuffle=False): self.paths = paths self.vocab = vocab self.bsz = bsz self.bptt = bptt self.ext_len = ext_len if ext_len is not None else 0 self.device = device self.shuffle = shuffle def get_sent_stream(self, path): sents = self.vocab.encode_file(path, add_double_eos=True) if self.shuffle: np.random.shuffle(sents) sent_stream = iter(sents) return sent_stream def __iter__(self): if self.shuffle: np.random.shuffle(self.paths) for path in self.paths: # sent_stream is an iterator sent_stream = self.get_sent_stream(path) for batch in self.stream_iterator(sent_stream): yield batch class TransfoXLCorpus(object): @classmethod def from_pretrained(cls, pretrained_model_name_or_path, cache_dir=None, *inputs, **kwargs): """ Instantiate a pre-processed corpus. """ vocab = TransfoXLTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs) if pretrained_model_name_or_path in PRETRAINED_CORPUS_ARCHIVE_MAP: corpus_file = PRETRAINED_CORPUS_ARCHIVE_MAP[pretrained_model_name_or_path] else: corpus_file = os.path.join(pretrained_model_name_or_path, CORPUS_NAME) # redirect to the cache, if necessary try: resolved_corpus_file = cached_path(corpus_file, cache_dir=cache_dir) except EnvironmentError: logger.error( "Corpus '{}' was not found in corpus list ({}). " "We assumed '{}' was a path or url but couldn't find files {} " "at this path or url.".format( pretrained_model_name_or_path, ", ".join(PRETRAINED_CORPUS_ARCHIVE_MAP.keys()), pretrained_model_name_or_path, corpus_file, ) ) return None if resolved_corpus_file == corpus_file: logger.info("loading corpus file {}".format(corpus_file)) else: logger.info("loading corpus file {} from cache at {}".format(corpus_file, resolved_corpus_file)) # Instantiate tokenizer. corpus = cls(*inputs, **kwargs) corpus_dict = torch.load(resolved_corpus_file) for key, value in corpus_dict.items(): corpus.__dict__[key] = value corpus.vocab = vocab if corpus.train is not None: corpus.train = torch.tensor(corpus.train, dtype=torch.long) if corpus.valid is not None: corpus.valid = torch.tensor(corpus.valid, dtype=torch.long) if corpus.test is not None: corpus.test = torch.tensor(corpus.test, dtype=torch.long) return corpus def __init__(self, *args, **kwargs): self.vocab = TransfoXLTokenizer(*args, **kwargs) self.dataset = None self.train = None self.valid = None self.test = None def build_corpus(self, path, dataset): self.dataset = dataset if self.dataset in ["ptb", "wt2", "enwik8", "text8"]: self.vocab.count_file(os.path.join(path, "train.txt")) self.vocab.count_file(os.path.join(path, "valid.txt")) self.vocab.count_file(os.path.join(path, "test.txt")) elif self.dataset == "wt103": self.vocab.count_file(os.path.join(path, "train.txt")) elif self.dataset == "lm1b": train_path_pattern = os.path.join( path, "1-billion-word-language-modeling-benchmark-r13output", "training-monolingual.tokenized.shuffled", "news.en-*", ) train_paths = glob.glob(train_path_pattern) # the vocab will load from file when build_vocab() is called self.vocab.build_vocab() if self.dataset in ["ptb", "wt2", "wt103"]: self.train = self.vocab.encode_file(os.path.join(path, "train.txt"), ordered=True) self.valid = self.vocab.encode_file(os.path.join(path, "valid.txt"), ordered=True) self.test = self.vocab.encode_file(os.path.join(path, "test.txt"), ordered=True) elif self.dataset in ["enwik8", "text8"]: self.train = self.vocab.encode_file(os.path.join(path, "train.txt"), ordered=True, add_eos=False) self.valid = self.vocab.encode_file(os.path.join(path, "valid.txt"), ordered=True, add_eos=False) self.test = self.vocab.encode_file(os.path.join(path, "test.txt"), ordered=True, add_eos=False) elif self.dataset == "lm1b": self.train = train_paths self.valid = self.vocab.encode_file(os.path.join(path, "valid.txt"), ordered=False, add_double_eos=True) self.test = self.vocab.encode_file(os.path.join(path, "test.txt"), ordered=False, add_double_eos=True) def get_iterator(self, split, *args, **kwargs): if split == "train": if self.dataset in ["ptb", "wt2", "wt103", "enwik8", "text8"]: data_iter = LMOrderedIterator(self.train, *args, **kwargs) elif self.dataset == "lm1b": kwargs["shuffle"] = True data_iter = LMMultiFileIterator(self.train, self.vocab, *args, **kwargs) elif split in ["valid", "test"]: data = self.valid if split == "valid" else self.test if self.dataset in ["ptb", "wt2", "wt103", "enwik8", "text8"]: data_iter = LMOrderedIterator(data, *args, **kwargs) elif self.dataset == "lm1b": data_iter = LMShuffledIterator(data, *args, **kwargs) return data_iter def get_lm_corpus(datadir, dataset): fn = os.path.join(datadir, "cache.pt") fn_pickle = os.path.join(datadir, "cache.pkl") if os.path.exists(fn): logger.info("Loading cached dataset...") corpus = torch.load(fn_pickle) elif os.path.exists(fn): logger.info("Loading cached dataset from pickle...") with open(fn, "rb") as fp: corpus = pickle.load(fp) else: logger.info("Producing dataset {}...".format(dataset)) kwargs = {} if dataset in ["wt103", "wt2"]: kwargs["special"] = ["<eos>"] kwargs["lower_case"] = False elif dataset == "ptb": kwargs["special"] = ["<eos>"] kwargs["lower_case"] = True elif dataset == "lm1b": kwargs["special"] = [] kwargs["lower_case"] = False kwargs["vocab_file"] = os.path.join(datadir, "1b_word_vocab.txt") elif dataset in ["enwik8", "text8"]: pass corpus = TransfoXLCorpus(datadir, dataset, **kwargs) torch.save(corpus, fn) return corpus