def __init__(self,
vocab_path,
language="en",
tokenizer=None,
subtokenizer=None,
subtokenizer_codes=None,
glossaries=None,
reverse_sequence=False,
**kwargs):
""" Initializes the data pipeline for text data.
Args:
language: The language.
vocab_path: The path to the vocabulary file, or a list of word tokens.
tokenizer: The tokenizer name.
subtokenizer: The name of tokenizer for subword encoding.
subtokenizer_codes: The subword codes.
glossaries: The glossaries that will not be split by tokenizer/subtokenizer.
reverse_sequence: A bool, whether to reverse the sequence.
"""
DataPipeline.__init__(self,
vocab_path=vocab_path,
language=language,
tokenizer=tokenizer,
subtokenizer=subtokenizer,
subtokenizer_codes=subtokenizer_codes,
glossaries=glossaries,
reverse_sequence=reverse_sequence,
**kwargs)
self._language = language
self._reverse_sequence = reverse_sequence
self._tokenizer = build_tokenizer(tokenizer,
language=language,
glossaries=glossaries)
self._subtokenizer = None
self._subtokenizer = build_tokenizer(subtokenizer,
language=language,
glossaries=glossaries,
vocabulary=vocab_path)
if self._subtokenizer is not None:
if subtokenizer_codes is None:
logging.info(
"No codes provided for subtokenizer: {}. "
"We assume this was done on purpose.".format(subtokenizer))
else:
self._subtokenizer.init_subtokenizer(subtokenizer_codes)
if isinstance(vocab_path, list):
tokens = Vocab.load_tokens(tokens=vocab_path)
else:
tokens = Vocab.load_tokens(vocab_path=vocab_path)
unk_token = Vocab.get_unique(tokens, "<UNK>")
bos_token = Vocab.get_unique(tokens, "<SEQ_BEG>")
eos_token = Vocab.get_unique(tokens, "<SEQ_END>")
assert unk_token != bos_token != eos_token
Vocab.__init__(self,
tokens, [unk_token, bos_token, eos_token],
lowercase=False)
self._eos_id = Vocab.map_token_to_id(self, eos_token)
self._bos_id = Vocab.map_token_to_id(self, bos_token)
self._unk_id = Vocab.map_token_to_id(self, unk_token)
def __init__(self,
vocab_path,
spm_model,
languages,
reverse_sequence=False,
**kwargs):
""" Initializes the data pipeline for text data.
Args:
vocab_path: The path to the vocabulary file, or a list of word tokens.
spm_model: The path to the sentence piece model.
languages: A list of languages. The corresponding language tags will automatically
append to the vocabulary.
reverse_sequence: A bool, whether to reverse the sequence.
"""
DataPipeline.__init__(self,
vocab_path=vocab_path,
languages=languages,
reverse_sequence=reverse_sequence,
**kwargs)
self._reverse_sequence = reverse_sequence
self._tokenizer = SentencePiece()
self._tokenizer.init_subtokenizer(spm_model)
if isinstance(vocab_path, list):
tokens = Vocab.load_tokens(tokens=vocab_path)
else:
tokens = Vocab.load_tokens(vocab_path=vocab_path)
if isinstance(languages, str):
languages = yaml.load(languages, Loader=yaml.FullLoader)
assert isinstance(languages, list), (
f"`languages` must be a list of strings, but got {languages}")
lang2tags = {}
for lang in languages:
lang2tags[lang] = Vocab.get_unique(tokens, "<" + lang + ">")
unk_token = Vocab.get_unique(tokens, "<UNK>")
bos_token = Vocab.get_unique(tokens, "<SEQ_BEG>")
eos_token = Vocab.get_unique(tokens, "<SEQ_END>")
assert unk_token != bos_token != eos_token
Vocab.__init__(self,
tokens, [unk_token, bos_token, eos_token] +
list(lang2tags.values()),
lowercase=False)
self._eos_id = Vocab.map_token_to_id(self, eos_token)
self._bos_id = Vocab.map_token_to_id(self, bos_token)
self._unk_id = Vocab.map_token_to_id(self, unk_token)
self._lang_ids = {
lang: Vocab.map_token_to_id(self, lang2tags[lang])
for lang in languages
}
def __init__(self,
name,
language="en",
vocab_path=None,
tokens=None,
**kwargs):
""" Initializes the data pipeline for text data.
Args:
name: The key of the BERT model, for creating the tokenizer and loading vocabulary.
language: The language.
tokens: A list of word tokens.
vocab_path: The path to the vocabulary file.
"""
if tokens is None and vocab_path is None:
path = GoogleBert.download(name)
if path is None:
raise ValueError(
f"Unknown BERT model name={name} for downloading.")
vocab_path = os.path.join(path, "vocab.txt")
else:
if tokens is not None:
vocab_path = None
tokens = Vocab.load_tokens(vocab_path, tokens)
vocab_path = None
# to handle with customized vocabulary
for spec_token in ["[UNK]", "[CLS]", "[SEP]", "[MASK]", "[PAD]"]:
if spec_token not in tokens:
tokens.insert(0, spec_token)
assert tokens[0] == "[PAD]"
Vocab.__init__(self,
Vocab.load_tokens(vocab_path, tokens),
lowercase=False)
DataPipeline.__init__(self,
name=name,
language=language,
tokens=self.tokens,
vocab_path=None,
**kwargs)
self._language = language
self._tokenizer = HuggingFaceTokenizer(language=language)
self._tokenizer.init_subtokenizer(name)
self._unk_id = Vocab.map_token_to_id(self, "[UNK]")
self._pad_id = Vocab.map_token_to_id(self, "[PAD]")
self._cls_id = Vocab.map_token_to_id(self, "[CLS]")
self._sep_id = Vocab.map_token_to_id(self, "[SEP]")
self._mask_id = Vocab.map_token_to_id(self, "[MASK]")
def __init__(self, language="en", tokens=None, vocab_path=None):
""" Initializes the data pipeline from OpenAI released GPT-2.
Args:
language: The language.
tokens: A list of word tokens.
vocab_path: The path to the vocabulary file.
"""
if tokens is None and vocab_path is None:
path = OpenAIGPT2.download("117M")
vocab_path = os.path.join(path, "encoder.json")
Vocab.__init__(self,
Vocab.load_tokens(vocab_path, tokens),
lowercase=False)
DataPipeline.__init__(self,
language=language,
tokens=self.tokens,
vocab_path=None)
self._language = language
self._tokenizer = HuggingFaceTokenizer(language=language)
self._tokenizer.init_subtokenizer("gpt2")
self._eos_id = Vocab.map_token_to_id(self, "<|endoftext|>")