def test_word_tokenizer():
tokenizer = WordTokenizer()
dummy = "justo. Praesent luctus."
assert tokenizer(dummy) == ["justo.", "Praesent", "luctus."]
dummy = ""
assert tokenizer(dummy) == []
def test_word_tokenizer():
tokenizer = WordTokenizer()
dummy = "justo. Praesent luctus."
assert tokenizer(dummy) == ['justo.', 'Praesent', 'luctus.']
dummy = ""
assert tokenizer(dummy) == []
def __init__(
self, # nosec
tokenizer: Optional[Tokenizer] = None,
lower: bool = False,
pad_token: Optional[str] = '<pad>',
unk_token: Optional[str] = '<unk>',
sos_token: Optional[str] = None,
eos_token: Optional[str] = None,
embeddings: Optional[str] = None,
embeddings_format: str = 'glove',
embeddings_binary: bool = False,
unk_init_all: bool = False,
drop_unknown: bool = False) -> None:
"""Initialize the TextField.
Parameters
----------
tokenizer : Tokenizer, optional
Tokenizer to use, by default WordTokenizer()
lower : bool, optional
If given, lowercase the input, by default False
pad_token : str, optional
Reserved padding token. Note that this object does not
perform padding. Padding is done on the fly, when sampling.
(defaults to '<pad>')
unk_token : str, optional
The token to use for out of vocabulary tokens
(defaults to '<unk>')
sos_token : str, optional
Start of sentence tokens to add to the start of
each sequence (defaults to '<sos>')
eos : Iterable[str], optional
List of end of sentence tokens to add to the end of each
sequence (defaults to an empty list)
embeddings : Optional[str], optional
Path to pretrained embeddings, by default None
embeddings_format : str, optional
The format of the input embeddings, should be one of:
'glove', 'word2vec', 'fasttext' or 'gensim'. The latter can
be used to download embeddings hosted on gensim on the fly.
See https://github.com/RaRe-Technologies/gensim-data
for the list of available embedding aliases.
embeddings_binary : bool, optional
Whether the input embeddings are provided in binary format,
by default False
unk_init_all : bool, optional
If True, every token not provided in the input embeddings is
given a random embedding from a normal distribution.
Otherwise, all of them map to the '<unk>' token.
drop_unknown: bool
Whether to drop tokens that don't have embeddings
associated. Defaults to True.
Important: this flag will only work when using embeddings.
"""
self.tokenizer = tokenizer or WordTokenizer()
self.lower = lower
self.pad = pad_token
self.unk = unk_token
self.sos = sos_token
self.eos = eos_token
self.embeddings = embeddings
self.embeddings_format = embeddings_format
self.embeddings_binary = embeddings_binary
self.embedding_matrix: Optional[torch.Tensor] = None
self.unk_init_all = unk_init_all
self.drop_unknown = drop_unknown
self.unk_numericals: Set[int] = set()
self.vocab: Dict = odict()
specials = [pad_token, unk_token, sos_token, eos_token]
self.specials = [
special for special in specials if special is not None
]
index = -1
for token in self.specials:
self.vocab[token] = index = index + 1
self.register_attrs('vocab')
def test_ngram_tokenizer_equivalence():
t1 = NGramsTokenizer(1)
t2 = WordTokenizer()
assert t1(example) == t2(example)
def __init__(
self, # nosec
tokenizer: Optional[Tokenizer] = None,
lower: bool = False,
pad_token: Optional[str] = '<pad>',
unk_token: str = '<unk>',
sos_token: Optional[str] = None,
eos_token: Optional[str] = None,
embeddings_info: Optional[EmbeddingsInformation] = None,
embeddings: Optional[str] = None,
embeddings_format: str = 'glove',
embeddings_binary: bool = False,
unk_init_all: bool = False,
drop_unknown: bool = False,
max_seq_len: Optional[int] = None,
truncate_end: bool = False,
setup_all_embeddings: bool = False) -> None:
"""Initialize the TextField.
Parameters
----------
tokenizer : Tokenizer, optional
Tokenizer to use, by default WordTokenizer()
lower : bool, optional
If given, lowercase the input, by default False
pad_token : str, optional
Reserved padding token. Note that this object does not
perform padding. Padding is done on the fly, when sampling.
(defaults to '<pad>')
unk_token : str, optional
The token to use for out of vocabulary tokens
(defaults to '<unk>')
sos_token : str, optional
Start of sentence tokens to add to the start of
each sequence (defaults to '<sos>')
eos : Iterable[str], optional
List of end of sentence tokens to add to the end of each
sequence (defaults to an empty list)
embeddings_info : EmbeddingsInformation, optional
The embeddings information. By default None
embeddings : str
WIlL BE DEPRECATED SOON. USE 'from_embeddings'
FACTORY INSTEAD.
Path to pretrained embeddings or the embedding name
in case format is gensim.
embeddings_format : str, optional
WIlL BE DEPRECATED SOON. USE 'from_embeddings'
FACTORY INSTEAD.
The format of the input embeddings, should be one of:
'glove', 'word2vec', 'fasttext' or 'gensim'. The latter can
be used to download embeddings hosted on gensim on the fly.
See https://github.com/RaRe-Technologies/gensim-data
for the list of available embedding aliases.
embeddings_binary : bool, optional
WIlL BE DEPRECATED SOON. USE 'from_embeddings'
FACTORY INSTEAD.
Whether the input embeddings are provided in binary format,
by default False
unk_init_all : bool, optional
If True, every token not provided in the input embeddings is
given a random embedding from a normal distribution.
Otherwise, all of them map to the '<unk>' token.
drop_unknown: bool
WIlL BE DEPRECATED SOON. USE 'from_embeddings'
FACTORY INSTEAD.
Whether to drop tokens that don't have embeddings
associated. Defaults to True.
Important: this flag will only work when using embeddings.
max_seq_len: int, optional
The maximum length possibly output by the process func.
If len of input tokens is larger than this number - then
the output will be truncated as a post processing step.
truncate_end: bool
Determines the window of observed text in process if the
input is larger than max_seq_len. If this value is True
the window starts from the end of the utterance.
Defaults to False.
example: max_seq_len=3, input_text=1 2 3 4 5
truncate_end=false: output=1 2 3
truncate_end=true: output=3 4 5
setup_all_embeddings: bool
WIlL BE DEPRECATED SOON. USE 'from_embeddings'
FACTORY INSTEAD.
Controls if all words from the optional provided
embeddings will be added to the vocabulary and to the
embedding matrix. Defaults to False.
"""
if embeddings:
if embeddings_info:
raise ValueError(
"Cannot submit embeddings information and use the embeddings parameters"
+
"simultaneously. Use the 'from_embeddings' factory instead."
)
warnings.warn(
"The embeddings-exclusive parameters " +
"('embeddings', 'embeddings_format', 'embeddings_binary', " +
"'setup_all_embeddings', 'drop_unknown', 'unk_init_all') " +
"will be deprecated in a future release. " +
"Please migrate to use the 'from_embeddings' factory.")
embeddings_info = EmbeddingsInformation(
embeddings=embeddings,
embeddings_format=embeddings_format,
embeddings_binary=embeddings_binary,
build_vocab_from_embeddings=setup_all_embeddings,
unk_init_all=unk_init_all,
drop_unknown=drop_unknown)
self.tokenizer = tokenizer or WordTokenizer()
self.lower = lower
self.pad = pad_token
self.unk = unk_token
self.sos = sos_token
self.eos = eos_token
self.embeddings_info = embeddings_info
self.embedding_matrix: Optional[torch.Tensor] = None
self.max_seq_len = max_seq_len
self.truncate_end = truncate_end
self.unk_numericals: Set[int] = set()
self.vocab: Dict = odict()
specials = [pad_token, unk_token, sos_token, eos_token]
self.specials = [
special for special in specials if special is not None
]
self.register_attrs('vocab')
def __init__(
self, # nosec
tokenizer: Optional[Tokenizer] = None,
lower: bool = False,
pad_token: Optional[str] = '<pad>',
unk_token: str = '<unk>',
sos_token: Optional[str] = None,
eos_token: Optional[str] = None,
embeddings: Optional[str] = None,
embeddings_format: str = 'glove',
embeddings_binary: bool = False,
model: Optional[KeyedVectors] = None,
unk_init_all: bool = False,
drop_unknown: bool = False,
max_seq_len: Optional[int] = None,
truncate_end: bool = False,
setup_all_embeddings: bool = False) -> None:
"""Initialize the TextField.
Parameters
----------
tokenizer : Tokenizer, optional
Tokenizer to use, by default WordTokenizer()
lower : bool, optional
If given, lowercase the input, by default False
pad_token : str, optional
Reserved padding token. Note that this object does not
perform padding. Padding is done on the fly, when sampling.
(defaults to '<pad>')
unk_token : str, optional
The token to use for out of vocabulary tokens
(defaults to '<unk>')
sos_token : str, optional
Start of sentence tokens to add to the start of
each sequence (defaults to '<sos>')
eos : Iterable[str], optional
List of end of sentence tokens to add to the end of each
sequence (defaults to an empty list)
model : KeyedVectors, optional
The embeddings model used for retrieving text embeddings,
by default None
unk_init_all : bool, optional
If True, every token not provided in the input embeddings is
given a random embedding from a normal distribution.
Otherwise, all of them map to the '<unk>' token.
drop_unknown: bool
Whether to drop tokens that don't have embeddings
associated. Defaults to True.
Important: this flag will only work when using embeddings.
max_seq_len: int, optional
The maximum length possibly output by the process func.
If len of input tokens is larger than this number - then
the output will be truncated as a post processing step.
truncate_end: bool
Determines the window of observed text in process if the
input is larger than max_seq_len. If this value is True
the window starts from the end of the utterance.
Defaults to False.
example: max_seq_len=3, input_text=1 2 3 4 5
truncate_end=false: output=1 2 3
truncate_end=true: output=3 4 5
setup_all_embeddings: bool
Controls if all words from the optional provided
embeddings will be added to the vocabulary and to the
embedding matrix. Defaults to False.
"""
if embeddings:
if model:
raise ValueError(
"Cannot submit a model and use the embeddings parameters" +
"simultaneously. Use the 'from_embeddings' factory instead."
)
warnings.warn(
"The embeddings-exclusive parameters " +
"('embeddings', 'embeddings_format', 'embeddings_binary', " +
"'setup_all_embeddings', 'drop_unknown', 'unk_init_all') will be "
+ "deprecated in a future release. " +
"Please migrate to use the 'from_embeddings' factory.")
model = get_embeddings(embeddings, embeddings_format,
embeddings_binary)
if setup_all_embeddings and not model:
raise ValueError(
"'setup_all_embeddings' cannot be enabled without passing embeddings."
)
self.tokenizer = tokenizer or WordTokenizer()
self.lower = lower
self.pad = pad_token
self.unk = unk_token
self.sos = sos_token
self.eos = eos_token
self.model = model
self.embedding_matrix: Optional[torch.Tensor] = None
self.unk_init_all = unk_init_all
self.drop_unknown = drop_unknown
self.setup_all_embeddings = setup_all_embeddings
self.max_seq_len = max_seq_len
self.truncate_end = truncate_end
self.unk_numericals: Set[int] = set()
self.vocab: Dict = odict()
specials = [pad_token, unk_token, sos_token, eos_token]
self.specials = [
special for special in specials if special is not None
]
self.register_attrs('vocab')