text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
) | 9,314 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
class RoCBertForQuestionAnswering(RoCBertPreTrainedModel):
# Copied from transformers.models.bert.modeling_bert.BertForQuestionAnswering.__init__ with Bert->RoCBert,bert->roc_bert
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.roc_bert = Ro... | 9,315 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
@add_start_docstrings_to_model_forward(ROC_BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_QA,
output_type=QuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
qa_target_start_index=_QA_TARGET_START_INDEX,
... | 9,315 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
end_positions: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], QuestionAnsweringModelOutput]:
r"""
start_positions (`torch.LongTensor` of ... | 9,315 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
are not taken into account for computing the loss.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 9,315 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
outputs = self.roc_bert(
input_ids,
input_shape_ids=input_shape_ids,
input_pronunciation_ids=input_pronunciation_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
... | 9,315 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
total_loss = None
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
... | 9,315 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
if not return_dict:
output = (start_logits, end_logits) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return QuestionAnsweringModelOutput(
loss=total_loss,
start_logits=start_logits,
end_logits=end_logits,
... | 9,315 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py |
class RoCBertTokenizer(PreTrainedTokenizer):
r"""
Args:
Construct a RoCBert tokenizer. Based on WordPiece. This tokenizer inherits from [`PreTrainedTokenizer`] which
contains most of the main methods. Users should refer to this superclass for more information regarding those
methods.
vocab_f... | 9,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
unk_token (`str`, *optional*, defaults to `"[UNK]"`):
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.
sep_token (`str`, *optional*, defaults to `"[SEP]"`):
The separator token, which is used when build... | 9,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
instead of per-token classification). It is the first token of the sequence when built with special tokens.
mask_token (`str`, *optional*, defaults to `"[MASK]"`):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is th... | 9,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
vocab_files_names = VOCAB_FILES_NAMES
def __init__(
self,
vocab_file,
word_shape_file,
word_pronunciation_file,
do_lower_case=True,
do_basic_tokenize=True,
never_split=None,
unk_token="[UNK]",
sep_token="[SEP]",
pad_token="[PAD]",
... | 9,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
with open(word_shape_file, "r", encoding="utf8") as in_file:
self.word_shape = json.load(in_file)
with open(word_pronunciation_file, "r", encoding="utf8") as in_file:
self.word_pronunciation = json.load(in_file)
self.ids_to_tokens = collections.OrderedDict([(ids, tok) for tok, ... | 9,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
self.do_basic_tokenize = do_basic_tokenize
if do_basic_tokenize:
self.basic_tokenizer = RoCBertBasicTokenizer(
do_lower_case=do_lower_case,
never_split=never_split,
tokenize_chinese_chars=tokenize_chinese_chars,
strip_accents=strip_acce... | 9,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
@property
def vocab_size(self):
return len(self.vocab)
# Copied from transformers.models.bert.tokenization_bert.BertTokenizer.get_vocab
def get_vocab(self):
return dict(self.vocab, **self.added_tokens_encoder)
# Copied from transformers.models.bert.tokenization_bert.BertTokenizer._toke... | 9,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
def _encode_plus(
self,
text: Union[TextInput, PreTokenizedInput, EncodedInput],
text_pair: Optional[Union[TextInput, PreTokenizedInput, EncodedInput]] = None,
add_special_tokens: bool = True,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
truncation_stra... | 9,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
def get_input_ids(text):
if isinstance(text, str):
tokens = self.tokenize(text, **kwargs)
tokens_ids = self.convert_tokens_to_ids(tokens)
tokens_shape_ids = self.convert_tokens_to_shape_ids(tokens)
tokens_proun_ids = self.convert_tokens_to_pron... | 9,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
return tokens_ids, tokens_shape_ids, tokens_proun_ids
else:
tokens_ids = self.convert_tokens_to_ids(text)
tokens_shape_ids = self.convert_tokens_to_shape_ids(text)
tokens_proun_ids = self.convert_tokens_to_pronunciation_ids(text)
... | 9,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
f"Input {text} is not valid. Should be a string, a list/tuple of strings or a list/tuple of"
" integers."
) | 9,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
if return_offsets_mapping:
raise NotImplementedError(
"return_offset_mapping is not available when using Python tokenizers. "
"To use this feature, change your tokenizer to one deriving from "
"transformers.PreTrainedTokenizerFast. "
"More info... | 9,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
return self.prepare_for_model(
first_ids,
first_shape_ids,
first_proun_ids,
pair_ids=second_ids,
pair_shape_ids=second_shape_ids,
pair_pronunciation_ids=second_proun_ids,
add_special_tokens=add_special_tokens,
padding=paddin... | 9,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
def prepare_for_model(
self,
ids: List[int],
shape_ids: List[int],
pronunciation_ids: List[int],
pair_ids: Optional[List[int]] = None,
pair_shape_ids: Optional[List[int]] = None,
... | 9,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
prepend_batch_axis: bool = False,
**kwargs,
) -> BatchEncoding:
"""
Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model. It
... | 9,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
Args:
ids (`List[int]`):
Tokenized input ids of the first sequence. Can be obtained from a string by chaining the `tokenize` and
`convert_tokens_to_id` methods.
shape_ids (`List[int]`):
Tokenized input ids of the first sequence. Can be obtained fro... | 9,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
Tokenized input ids of the second sequence. Can be obtained from a string by chaining the `tokenize`
and `convert_token_to_shape_id` methods.
pair_pronunciation_ids (`List[int]`, *optional*):
Tokenized input ids of the second sequence. Can be obtained from a string by chainin... | 9,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
# Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
padding=padding,
truncation=truncation,
max_length=max_length,
pad_to_multiple_of=pad_to_mu... | 9,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
if (
return_overflowing_tokens
and truncation_strategy == TruncationStrategy.LONGEST_FIRST
and pair_ids is not None
):
raise ValueError(
"Not possible to return overflowing tokens for pair of sequences with the "
"`longest_first`. P... | 9,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
# Truncation: Handle max sequence length
overflowing_tokens = []
if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE and max_length and total_len > max_length:
ids, pair_ids, overflowing_tokens = self.truncate_sequences(
ids,
pair_ids=pair_ids,
... | 9,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
truncation_strategy=truncation_strategy,
stride=stride,
) | 9,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
if return_overflowing_tokens:
encoded_inputs["overflowing_tokens"] = overflowing_tokens
encoded_inputs["num_truncated_tokens"] = total_len - max_length | 9,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
# Add special tokens
if add_special_tokens:
sequence = self.build_inputs_with_special_tokens(ids, pair_ids)
token_type_ids = self.create_token_type_ids_from_sequences(ids, pair_ids)
input_shape_ids = self.build_inputs_with_special_tokens(
shape_ids, pair_shape... | 9,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
pronunciation_ids + pair_pronunciation_ids if pair_pronunciation_ids else pronunciation_ids
) | 9,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
# Build output dictionary
encoded_inputs["input_ids"] = sequence
encoded_inputs["input_shape_ids"] = input_shape_ids
encoded_inputs["input_pronunciation_ids"] = input_pronunciation_ids
if return_token_type_ids:
encoded_inputs["token_type_ids"] = token_type_ids
if retu... | 9,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
# Padding
if padding_strategy != PaddingStrategy.DO_NOT_PAD or return_attention_mask:
encoded_inputs = self.pad(
encoded_inputs,
max_length=max_length,
padding=padding_strategy.value,
pad_to_multiple_of=pad_to_multiple_of,
... | 9,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
def _pad(
self,
encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding],
max_length: Optional[int] = None,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
pad_to_multiple_of: Optional[int] = None,
padding_side: Optional[bool] = None,
return_a... | 9,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
# Initialize attention mask if not present.
if return_attention_mask and "attention_mask" not in encoded_inputs:
encoded_inputs["attention_mask"] = [1] * len(required_input)
if needs_to_be_padded:
difference = max_length - len(required_input)
padding_side = padding_s... | 9,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
if padding_side == "right":
if return_attention_mask:
encoded_inputs["attention_mask"] = encoded_inputs["attention_mask"] + [0] * difference
if "token_type_ids" in encoded_inputs:
encoded_inputs["token_type_ids"] = (
encoded... | 9,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"]
if "token_type_ids" in encoded_inputs:
encoded_inputs["token_type_ids"] = [self.pad_token_type_id] * difference + encoded_inputs[
"token_type_ids"
]
... | 9,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
return encoded_inputs | 9,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
def _batch_encode_plus(
self,
batch_text_or_text_pairs: Union[
List[TextInput],
List[TextInputPair],
List[PreTokenizedInput],
List[PreTokenizedInputPair],
List[EncodedInput],
List[EncodedInputPair],
],
add_special_to... | 9,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs,
) -> BatchEncoding:
def get_input_ids(text):
if isinstance(text, str):
tokens = self.tokenize(text, **kwargs)
tokens_ids = self.convert_token... | 9,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
tokens_shape_ids = self.convert_tokens_to_shape_ids(tokens)
tokens_proun_ids = self.convert_tokens_to_pronunciation_ids(tokens)
return tokens_ids, tokens_shape_ids, tokens_proun_ids
else:
tokens_ids = self.convert_tokens_to_ids(text)
... | 9,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
if return_offsets_mapping:
raise NotImplementedError(
"return_offset_mapping is not available when using Python tokenizers. "
"To use this feature, change your tokenizer to one deriving from "
"transformers.PreTrainedTokenizerFast."
)
inpu... | 9,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
first_ids, first_shape_ids, first_proun_ids = get_input_ids(ids)
if pair_ids is not None:
second_ids, second_shape_ids, second_proun_ids = get_input_ids(pair_ids)
else:
second_ids, second_shape_ids, second_proun_ids = None, None, None
input_ids.append... | 9,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
batch_outputs = self._batch_prepare_for_model(
input_ids,
batch_shape_ids_pairs=input_shape_ids,
batch_pronunciation_ids_pairs=input_pronunciation_ids,
add_special_tokens=add_special_tokens,
padding_strategy=padding_strategy,
truncation_strategy=tr... | 9,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
def _batch_prepare_for_model(
self,
batch_ids_pairs: List[Union[PreTokenizedInputPair, Tuple[List[int], None]]],
batch_shape_ids_pairs: List[Union[PreTokenizedInputPair, Tuple[List[int], None]]],
ba... | 9,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
return_special_tokens_mask: bool = False,
return_length: bool = False,
verbose: bool = True,
) -> BatchEncoding:
"""
Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model. It
adds special tokens, truncates sequences if overf... | 9,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
Args:
batch_ids_pairs: list of tokenized input ids or input ids pairs
batch_shape_ids_pairs: list of tokenized input shape ids or input shape ids pairs
batch_pronunciation_ids_pairs: list of tokenized input pronunciation ids or input pronunciation ids pairs
""" | 9,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
batch_outputs = {}
for i, (first_ids, second_ids) in enumerate(batch_ids_pairs):
first_shape_ids, second_shape_ids = batch_shape_ids_pairs[i]
first_pronunciation_ids, second_pronunciation_ids = batch_pronunciation_ids_pairs[i]
outputs = self.prepare_for_model(
... | 9,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
return_attention_mask=False, # we pad in batch afterward
return_token_type_ids=return_token_type_ids,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_length=return_length,
... | 9,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
for key, value in outputs.items():
if key not in batch_outputs:
batch_outputs[key] = []
batch_outputs[key].append(value)
batch_outputs = self.pad(
batch_outputs,
padding=padding_strategy.value,
max_length=max_length,
... | 9,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
def _convert_token_to_shape_id(self, token):
"""Converts a token (str) in an shape_id using the shape vocab."""
return self.word_shape.get(token, self.word_shape.get(self.unk_token))
def convert_tokens_to_shape_ids(self, tokens: Union[str, List[str]]) -> Union[int, List[int]]:
if tokens is ... | 9,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
# Copied from transformers.models.bert.tokenization_bert.BertTokenizer._convert_id_to_token
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
return self.ids_to_tokens.get(index, self.unk_token)
# Copied from transformers.models.bert.toke... | 9,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
- single sequence: `[CLS] X [SEP]`
- pair of sequences: `[CLS] A [SEP] B [SEP]`
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... | 9,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
# Copied from transformers.models.bert.tokenization_bert.BertTokenizer.get_special_tokens_mask
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 tok... | 9,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
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]... | 9,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
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:
`Li... | 9,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str, str, str]:
index = 0
if os.path.isdir(save_directory):
vocab_file = os.path.join(
save_directory,
(filename_prefix + "-" if filename_prefix else "") + self.voca... | 9,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
"pretrained model use `tokenizer = RoCBertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
) | 9,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
with open(vocab_file, "w", encoding="utf-8") as writer:
for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]):
if index != token_index:
logger.warning(
f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive... | 9,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
class RoCBertBasicTokenizer:
"""
Constructs a RoCBertBasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.).
Args:
do_lower_case (`bool`, *optional*, defaults to `True`):
Whether or not to lowercase the input when tokenizing.
never_split (`It... | 9,317 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
This should likely be deactivated for Japanese (see this
[issue](https://github.com/huggingface/transformers/issues/328)).
strip_accents (`bool`, *optional*):
Whether or not to strip all accents. If this option is not specified, then it will be determined by the
value for `lo... | 9,317 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
def __init__(
self,
do_lower_case=True,
never_split=None,
tokenize_chinese_chars=True,
strip_accents=None,
do_split_on_punc=True,
):
if never_split is None:
never_split = []
self.do_lower_case = do_lower_case
self.never_split = set(... | 9,317 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
Args:
never_split (`List[str]`, *optional*)
Kept for backward compatibility purposes. Now implemented directly at the base class level (see
[`PreTrainedTokenizer.tokenize`]) List of token not to split.
"""
# union() returns a new set by concatenating the two s... | 9,317 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
# This was added on November 1st, 2018 for the multilingual and Chinese
# models. This is also applied to the English models now, but it doesn't
# matter since the English models were not trained on any Chinese data
# and generally don't have any Chinese data in them (there are Chinese
#... | 9,317 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
token = self._run_strip_accents(token)
elif self.strip_accents:
token = self._run_strip_accents(token)
split_tokens.extend(self._run_split_on_punc(token, never_split)) | 9,317 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
output_tokens = whitespace_tokenize(" ".join(split_tokens))
return output_tokens
def _run_strip_accents(self, text):
"""Strips accents from a piece of text."""
text = unicodedata.normalize("NFD", text)
output = []
for char in text:
cat = unicodedata.category(char... | 9,317 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
def _run_split_on_punc(self, text, never_split=None):
"""Splits punctuation on a piece of text."""
if not self.do_split_on_punc or (never_split is not None and text in never_split):
return [text]
chars = list(text)
i = 0
start_new_word = True
output = []
... | 9,317 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
def _tokenize_chinese_chars(self, text):
"""Adds whitespace around any CJK character."""
output = []
for char in text:
cp = ord(char)
if self._is_chinese_char(cp):
output.append(" ")
output.append(char)
output.append(" ")
... | 9,317 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
def _is_chinese_char(self, cp):
"""Checks whether CP is the codepoint of a CJK character."""
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is ... | 9,317 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
or (cp >= 0x2F800 and cp <= 0x2FA1F) #
): #
return True | 9,317 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
return False
def _clean_text(self, text):
"""Performs invalid character removal and whitespace cleanup on text."""
output = []
for char in text:
cp = ord(char)
if cp == 0 or cp == 0xFFFD or _is_control(char):
continue
if _is_whitespace(cha... | 9,317 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
class RoCBertWordpieceTokenizer:
"""Runs WordPiece tokenization."""
def __init__(self, vocab, unk_token, max_input_chars_per_word=100):
self.vocab = vocab
self.unk_token = unk_token
self.max_input_chars_per_word = max_input_chars_per_word
def tokenize(self, text):
"""
... | 9,318 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
output_tokens = []
for token in whitespace_tokenize(text):
chars = list(token)
if len(chars) > self.max_input_chars_per_word:
output_tokens.append(self.unk_token)
continue
is_bad = False
start = 0
sub_tokens = []
... | 9,318 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
if is_bad:
output_tokens.append(self.unk_token)
else:
output_tokens.extend(sub_tokens)
return output_tokens | 9,318 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py |
class RoCBertConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`RoCBertModel`]. It is used to instantiate a
RoCBert model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a simi... | 9,319 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/configuration_roc_bert.py |
Args:
vocab_size (`int`, *optional*, defaults to 30522):
Vocabulary size of the RoCBert model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`RoCBertModel`].
hidden_size (`int`, *optional*, defaults to 768):
Di... | 9,319 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/configuration_roc_bert.py |
`"relu"`, `"selu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
The d... | 9,319 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/configuration_roc_bert.py |
The epsilon used by the layer normalization layers.
is_decoder (`bool`, *optional*, defaults to `False`):
Whether the model is used as a decoder or not. If `False`, the model is used as an encoder.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model shoul... | 9,319 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/configuration_roc_bert.py |
with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
classifier_dropout (`float`, *optional*):
The dropout ratio for the classification head.
enable_pronunciation (`bool`, *optional*, defaults to `True`):
Whether or not the model use pronunc... | 9,319 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/configuration_roc_bert.py |
shape_vocab_size (`int`, *optional*, defaults to 24858):
Shape Vocabulary size of the RoCBert model. Defines the number of different tokens that can be represented
by the `input_shape_ids` passed when calling [`RoCBertModel`].
concat_input (`bool`, *optional*, defaults to `True`):
... | 9,319 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/configuration_roc_bert.py |
```python
>>> from transformers import RoCBertModel, RoCBertConfig
>>> # Initializing a RoCBert weiweishi/roc-bert-base-zh style configuration
>>> configuration = RoCBertConfig()
>>> # Initializing a model from the weiweishi/roc-bert-base-zh style configuration
>>> model = RoCBertModel(configurati... | 9,319 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/configuration_roc_bert.py |
def __init__(
self,
vocab_size=30522,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
... | 9,319 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/configuration_roc_bert.py |
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.initializer_range = initializer_range
... | 9,319 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/configuration_roc_bert.py |
class BasicTokenizer:
"""
Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.).
Args:
do_lower_case (`bool`, *optional*, defaults to `True`):
Whether or not to lowercase the input when tokenizing.
never_split (`Iterable`, *opti... | 9,320 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/tokenization_prophetnet.py |
This should likely be deactivated for Japanese (see this
[issue](https://github.com/huggingface/transformers/issues/328)).
strip_accents (`bool`, *optional*):
Whether or not to strip all accents. If this option is not specified, then it will be determined by the
value for `lo... | 9,320 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/tokenization_prophetnet.py |
def __init__(
self,
do_lower_case=True,
never_split=None,
tokenize_chinese_chars=True,
strip_accents=None,
do_split_on_punc=True,
):
if never_split is None:
never_split = []
self.do_lower_case = do_lower_case
self.never_split = set(... | 9,320 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/tokenization_prophetnet.py |
Args:
never_split (`List[str]`, *optional*)
Kept for backward compatibility purposes. Now implemented directly at the base class level (see
[`PreTrainedTokenizer.tokenize`]) List of token not to split.
"""
# union() returns a new set by concatenating the two s... | 9,320 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/tokenization_prophetnet.py |
# This was added on November 1st, 2018 for the multilingual and Chinese
# models. This is also applied to the English models now, but it doesn't
# matter since the English models were not trained on any Chinese data
# and generally don't have any Chinese data in them (there are Chinese
#... | 9,320 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/tokenization_prophetnet.py |
token = self._run_strip_accents(token)
elif self.strip_accents:
token = self._run_strip_accents(token)
split_tokens.extend(self._run_split_on_punc(token, never_split)) | 9,320 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/tokenization_prophetnet.py |
output_tokens = whitespace_tokenize(" ".join(split_tokens))
return output_tokens
def _run_strip_accents(self, text):
"""Strips accents from a piece of text."""
text = unicodedata.normalize("NFD", text)
output = []
for char in text:
cat = unicodedata.category(char... | 9,320 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/tokenization_prophetnet.py |
def _run_split_on_punc(self, text, never_split=None):
"""Splits punctuation on a piece of text."""
if not self.do_split_on_punc or (never_split is not None and text in never_split):
return [text]
chars = list(text)
i = 0
start_new_word = True
output = []
... | 9,320 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/tokenization_prophetnet.py |
def _tokenize_chinese_chars(self, text):
"""Adds whitespace around any CJK character."""
output = []
for char in text:
cp = ord(char)
if self._is_chinese_char(cp):
output.append(" ")
output.append(char)
output.append(" ")
... | 9,320 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/tokenization_prophetnet.py |
def _is_chinese_char(self, cp):
"""Checks whether CP is the codepoint of a CJK character."""
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is ... | 9,320 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/tokenization_prophetnet.py |
or (cp >= 0x2F800 and cp <= 0x2FA1F) #
): #
return True | 9,320 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/tokenization_prophetnet.py |
return False
def _clean_text(self, text):
"""Performs invalid character removal and whitespace cleanup on text."""
output = []
for char in text:
cp = ord(char)
if cp == 0 or cp == 0xFFFD or _is_control(char):
continue
if _is_whitespace(cha... | 9,320 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/tokenization_prophetnet.py |
class WordpieceTokenizer:
"""Runs WordPiece tokenization."""
def __init__(self, vocab, unk_token, max_input_chars_per_word=100):
self.vocab = vocab
self.unk_token = unk_token
self.max_input_chars_per_word = max_input_chars_per_word
def tokenize(self, text):
"""
Toke... | 9,321 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/tokenization_prophetnet.py |
output_tokens = []
for token in whitespace_tokenize(text):
chars = list(token)
if len(chars) > self.max_input_chars_per_word:
output_tokens.append(self.unk_token)
continue
is_bad = False
start = 0
sub_tokens = []
... | 9,321 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/tokenization_prophetnet.py |
if is_bad:
output_tokens.append(self.unk_token)
else:
output_tokens.extend(sub_tokens)
return output_tokens | 9,321 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/tokenization_prophetnet.py |
class ProphetNetTokenizer(PreTrainedTokenizer):
r"""
Construct a ProphetNetTokenizer. Based on WordPiece.
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods. | 9,322 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/tokenization_prophetnet.py |
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