def build(self, dataset, min_freq=1, embed=None):
r"""
Constructs a :class:`Vocab` object for this field from the dataset.
If the vocabulary has already existed, this function will have no effect.
Args:
dataset (Dataset):
A :class:`Dataset` object. One of the attributes should be named after the name of this field.
min_freq (int):
The minimum frequency needed to include a token in the vocabulary. Default: 1.
embed (Embedding):
An Embedding object, words in which will be extended to the vocabulary. Default: ``None``.
"""
if hasattr(self, 'vocab'):
return
sequences = getattr(dataset, self.name)
counter = Counter(token
for seq in sequences
for token in self.preprocess(seq))
self.vocab = Vocab(counter, min_freq, self.specials, self.unk_index)
if not embed:
self.embed = None
else:
tokens = self.preprocess(embed.tokens)
# if the `unk` token has existed in the pretrained,
# then replace it with a self-defined one
if embed.unk:
tokens[embed.unk_index] = self.unk
self.vocab.extend(tokens)
self.embed = torch.zeros(len(self.vocab), embed.dim)
self.embed[self.vocab[tokens]] = embed.vectors
self.embed /= torch.std(self.embed)
def build(self, dataset, min_freq=1):
counter = Counter(i
for chart in getattr(dataset, self.name)
for row in self.preprocess(chart)
for i in row if i is not None)
self.vocab = Vocab(counter, min_freq, self.specials, self.unk_index)
def build(self, dataset, min_freq=1, embed=None):
if hasattr(self, 'vocab'):
return
sequences = getattr(dataset, self.name)
counter = Counter(piece for seq in sequences for token in seq
for piece in self.preprocess(token))
self.vocab = Vocab(counter, min_freq, self.specials, self.unk_index)
if not embed:
self.embed = None
else:
tokens = self.preprocess(embed.tokens)
# if the `unk` token has existed in the pretrained,
# then replace it with a self-defined one
if embed.unk:
tokens[embed.unk_index] = self.unk
self.vocab.extend(tokens)
self.embed = torch.zeros(len(self.vocab), embed.dim)
self.embed[self.vocab[tokens]] = embed.vectors
def build(self, dataset):
r"""
Constructs a :class:`~supar.utils.vocab.Vocab` object for this field from the dataset.
If the vocabulary exists already, this function will have no effect.
Args:
dataset (Dataset):
A :class:`~supar.utils.data.Dataset` object.
One of the attributes should be named after the name of this field.
"""
sequences = getattr(dataset, self.name)
counter = Counter(
rel.split(':', 1)[1] for seq in sequences for rels in seq
if rels != '_' for rel in rels.split('|'))
self.vocab = Vocab(counter, specials=[self.pad])
def build(self, dataset, min_freq=1, embed=None):
r"""
Constructs a :class:`Vocab` object for this field from the dataset.
If the vocabulary has already existed, this function will have no effect.
Args:
dataset (Dataset):
A :class:`Dataset` object. One of the attributes should be named after the name of this field.
min_freq (int):
The minimum frequency needed to include a token in the vocabulary. Default: 1.
embed (Embedding):
An Embedding object, words in which will be extended to the vocabulary. Default: ``None``.
"""
if hasattr(self, 'vocab'):
return
sequences = getattr(dataset, self.name)
counter = Counter(token for seq in sequences
for token in self.preprocess(seq))
self.vocab = Vocab(counter, min_freq, self.specials, self.unk_index)
class Field(RawField):
r"""
Defines a datatype together with instructions for converting to :class:`~torch.Tensor`.
:class:`Field` models common text processing datatypes that can be represented by tensors.
It holds a :class:`Vocab` object that defines the set of possible values
for elements of the field and their corresponding numerical representations.
The :class:`Field` object also holds other parameters relating to how a datatype
should be numericalized, such as a tokenization method.
Args:
name (str):
The name of the field.
pad_token (str):
The string token used as padding. Default: ``None``.
unk_token (str):
The string token used to represent OOV words. Default: ``None``.
bos_token (str):
A token that will be prepended to every example using this field, or ``None`` for no `bos_token`.
Default: ``None``.
eos_token (str):
A token that will be appended to every example using this field, or ``None`` for no `eos_token`.
lower (bool):
Whether to lowercase the text in this field. Default: ``False``.
use_vocab (bool):
Whether to use a :class:`Vocab` object. If ``False``, the data in this field should already be numerical.
Default: ``True``.
tokenize (function):
The function used to tokenize strings using this field into sequential examples. Default: ``None``.
fn (function):
The function used for preprocessing the examples. Default: ``None``.
"""
def __init__(self, name, pad=None, unk=None, bos=None, eos=None,
lower=False, use_vocab=True, tokenize=None, fn=None):
self.name = name
self.pad = pad
self.unk = unk
self.bos = bos
self.eos = eos
self.lower = lower
self.use_vocab = use_vocab
self.tokenize = tokenize
self.fn = fn
self.specials = [token for token in [pad, unk, bos, eos] if token is not None]
def __repr__(self):
s, params = f"({self.name}): {self.__class__.__name__}(", []
if self.pad is not None:
params.append(f"pad={self.pad}")
if self.unk is not None:
params.append(f"unk={self.unk}")
if self.bos is not None:
params.append(f"bos={self.bos}")
if self.eos is not None:
params.append(f"eos={self.eos}")
if self.lower:
params.append(f"lower={self.lower}")
if not self.use_vocab:
params.append(f"use_vocab={self.use_vocab}")
s += ", ".join(params)
s += ")"
return s
def __getstate__(self):
state = self.__dict__
if self.tokenize is None:
state['tokenize_args'] = None
elif self.tokenize.__module__.startswith('transformers'):
state['tokenize_args'] = (self.tokenize.__module__, self.tokenize.__self__.name_or_path)
state['tokenize'] = None
return state
def __setstate__(self, state):
tokenize_args = state.pop('tokenize_args', None)
if tokenize_args is not None and tokenize_args[0].startswith('transformers'):
from transformers import AutoTokenizer
state['tokenize'] = AutoTokenizer.from_pretrained(tokenize_args[1]).tokenize
self.__dict__.update(state)
@property
def pad_index(self):
if self.pad is None:
return 0
if hasattr(self, 'vocab'):
return self.vocab[self.pad]
return self.specials.index(self.pad)
@property
def unk_index(self):
if self.unk is None:
return 0
if hasattr(self, 'vocab'):
return self.vocab[self.unk]
return self.specials.index(self.unk)
@property
def bos_index(self):
if hasattr(self, 'vocab'):
return self.vocab[self.bos]
return self.specials.index(self.bos)
@property
def eos_index(self):
if hasattr(self, 'vocab'):
return self.vocab[self.eos]
return self.specials.index(self.eos)
@property
def device(self):
return 'cuda' if torch.cuda.is_available() else 'cpu'
def preprocess(self, sequence):
r"""
Loads a single example using this field, tokenizing if necessary.
The sequence will be first passed to ``fn`` if available.
If ``tokenize`` is not None, the input will be tokenized.
Then the input will be lowercased optionally.
Args:
sequence (list):
The sequence to be preprocessed.
Returns:
A list of preprocessed sequence.
"""
if self.fn is not None:
sequence = self.fn(sequence)
if self.tokenize is not None:
sequence = self.tokenize(sequence)
if self.lower:
sequence = [str.lower(token) for token in sequence]
return sequence
def build(self, dataset, min_freq=1, embed=None):
r"""
Constructs a :class:`Vocab` object for this field from the dataset.
If the vocabulary has already existed, this function will have no effect.
Args:
dataset (Dataset):
A :class:`Dataset` object. One of the attributes should be named after the name of this field.
min_freq (int):
The minimum frequency needed to include a token in the vocabulary. Default: 1.
embed (Embedding):
An Embedding object, words in which will be extended to the vocabulary. Default: ``None``.
"""
if hasattr(self, 'vocab'):
return
sequences = getattr(dataset, self.name)
counter = Counter(token
for seq in sequences
for token in self.preprocess(seq))
self.vocab = Vocab(counter, min_freq, self.specials, self.unk_index)
if not embed:
self.embed = None
else:
tokens = self.preprocess(embed.tokens)
# if the `unk` token has existed in the pretrained,
# then replace it with a self-defined one
if embed.unk:
tokens[embed.unk_index] = self.unk
self.vocab.extend(tokens)
self.embed = torch.zeros(len(self.vocab), embed.dim)
self.embed[self.vocab[tokens]] = embed.vectors
self.embed /= torch.std(self.embed)
def transform(self, sequences):
r"""
Turns a list of sequences that use this field into tensors.
Each sequence is first preprocessed and then numericalized if needed.
Args:
sequences (list[list[str]]):
A list of sequences.
Returns:
A list of tensors transformed from the input sequences.
"""
sequences = [self.preprocess(seq) for seq in sequences]
if self.use_vocab:
sequences = [self.vocab[seq] for seq in sequences]
if self.bos:
sequences = [[self.bos_index] + seq for seq in sequences]
if self.eos:
sequences = [seq + [self.eos_index] for seq in sequences]
sequences = [torch.tensor(seq) for seq in sequences]
return sequences
def compose(self, sequences):
r"""
Composes a batch of sequences into a padded tensor.
Args:
sequences (list[~torch.Tensor]):
A list of tensors.
Returns:
A padded tensor converted to proper device.
"""
return pad(sequences, self.pad_index).to(self.device)
class SubwordField(Field):
r"""
A field that conducts tokenization and numericalization over each token rather the sequence.
This is customized for models requiring character/subword-level inputs, e.g., CharLSTM and BERT.
Args:
fix_len (int):
A fixed length that all subword pieces will be padded to.
This is used for truncating the subword pieces that exceed the length.
To save the memory, the final length will be the smaller value
between the max length of subword pieces in a batch and `fix_len`.
Examples:
>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained('bert-base-cased')
>>> field = SubwordField('bert',
pad=tokenizer.pad_token,
unk=tokenizer.unk_token,
bos=tokenizer.cls_token,
eos=tokenizer.sep_token,
fix_len=20,
tokenize=tokenizer.tokenize)
>>> field.vocab = tokenizer.get_vocab() # no need to re-build the vocab
>>> field.transform([['This', 'field', 'performs', 'token-level', 'tokenization']])[0]
tensor([[ 101, 0, 0],
[ 1188, 0, 0],
[ 1768, 0, 0],
[10383, 0, 0],
[22559, 118, 1634],
[22559, 2734, 0],
[ 102, 0, 0]])
"""
def __init__(self, *args, **kwargs):
self.fix_len = kwargs.pop('fix_len') if 'fix_len' in kwargs else 0
super().__init__(*args, **kwargs)
def build(self, dataset, min_freq=1, embed=None):
if hasattr(self, 'vocab'):
return
sequences = getattr(dataset, self.name)
counter = Counter(piece
for seq in sequences
for token in seq
for piece in self.preprocess(token))
self.vocab = Vocab(counter, min_freq, self.specials, self.unk_index)
if not embed:
self.embed = None
else:
tokens = self.preprocess(embed.tokens)
# if the `unk` token has existed in the pretrained,
# then replace it with a self-defined one
if embed.unk:
tokens[embed.unk_index] = self.unk
self.vocab.extend(tokens)
self.embed = torch.zeros(len(self.vocab), embed.dim)
self.embed[self.vocab[tokens]] = embed.vectors
def transform(self, sequences):
sequences = [[self.preprocess(token) for token in seq]
for seq in sequences]
if self.fix_len <= 0:
self.fix_len = max(len(token) for seq in sequences for token in seq)
if self.use_vocab:
sequences = [[[self.vocab[i] if i in self.vocab else self.unk_index for i in token] if token else [self.unk_index]
for token in seq] for seq in sequences]
if self.bos:
sequences = [[[self.bos_index]] + seq for seq in sequences]
if self.eos:
sequences = [seq + [[self.eos_index]] for seq in sequences]
lens = [min(self.fix_len, max(len(ids) for ids in seq)) for seq in sequences]
sequences = [pad([torch.tensor(ids[:i]) for ids in seq], self.pad_index, i)
for i, seq in zip(lens, sequences)]
return sequences
def build(self, dataset, min_freq=1):
counter = Counter(label for seq in getattr(dataset, self.name)
for i, j, label in self.preprocess(seq))
self.vocab = Vocab(counter, min_freq, self.specials, self.unk_index)
