text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
Returns:
Examples:
```python
>>> from transformers import AutoTokenizer, LukeForEntityClassification
>>> tokenizer = AutoTokenizer.from_pretrained("studio-ousia/luke-large-finetuned-open-entity")
>>> model = LukeForEntityClassification.from_pretrained("studio-ousia/luke-large-... | 3,915 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
outputs = self.luke(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
entity_ids=entity_ids,
entity_attention_mask=entity_attention_mask,
entity_token_type_ids=entity_token_ty... | 3,915 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
loss = None
if labels is not None:
# When the number of dimension of `labels` is 1, cross entropy is used as the loss function. The binary
# cross entropy is used otherwise.
# move labels to correct device to enable model parallelism
labels = labels.to(logits.devi... | 3,915 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
return EntityClassificationOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
entity_hidden_states=outputs.entity_hidden_states,
attentions=outputs.attentions,
) | 3,915 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
class LukeForEntityPairClassification(LukePreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.luke = LukeModel(config)
self.num_labels = config.num_labels
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden... | 3,916 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
@add_start_docstrings_to_model_forward(LUKE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=EntityPairClassificationOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Option... | 3,916 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
return_dict: Optional[bool] = None,
) -> Union[Tuple, EntityPairClassificationOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)` or `(batch_size, num_labels)`, *optional*):
Labels for computing the classification loss. If the shape is `(batch_size,)`, the cross entropy los... | 3,916 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
Returns:
Examples:
```python
>>> from transformers import AutoTokenizer, LukeForEntityPairClassification
>>> tokenizer = AutoTokenizer.from_pretrained("studio-ousia/luke-large-finetuned-tacred")
>>> model = LukeForEntityPairClassification.from_pretrained("studio-ousia/luke-lar... | 3,916 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
>>> text = "Beyoncé lives in Los Angeles."
>>> entity_spans = [
... (0, 7),
... (17, 28),
... ] # character-based entity spans corresponding to "Beyoncé" and "Los Angeles"
>>> inputs = tokenizer(text, entity_spans=entity_spans, return_tensors="pt")
>>> outputs = ... | 3,916 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
outputs = self.luke(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
entity_ids=entity_ids,
entity_attention_mask=entity_attention_mask,
entity_token_type_ids=entity_token_ty... | 3,916 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
loss = None
if labels is not None:
# When the number of dimension of `labels` is 1, cross entropy is used as the loss function. The binary
# cross entropy is used otherwise.
# move labels to correct device to enable model parallelism
labels = labels.to(logits.devi... | 3,916 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
return EntityPairClassificationOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
entity_hidden_states=outputs.entity_hidden_states,
attentions=outputs.attentions,
) | 3,916 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
class LukeForEntitySpanClassification(LukePreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.luke = LukeModel(config)
self.num_labels = config.num_labels
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden... | 3,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
@add_start_docstrings_to_model_forward(LUKE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=EntitySpanClassificationOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Option... | 3,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, EntitySpanClassificationOutput]:
r"""
entity_start_positions (`torch.LongTensor`):
The start positions of entities in the word toke... | 3,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
entity_end_positions (`torch.LongTensor`):
The end positions of entities in the word token sequence.
labels (`torch.LongTensor` of shape `(batch_size, entity_length)` or `(batch_size, entity_length, num_labels)`, *optional*):
Labels for computing the classification loss. If the shape is... | 3,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
>>> tokenizer = AutoTokenizer.from_pretrained("studio-ousia/luke-large-finetuned-conll-2003")
>>> model = LukeForEntitySpanClassification.from_pretrained("studio-ousia/luke-large-finetuned-conll-2003")
>>> text = "Beyoncé lives in Los Angeles"
# List all possible entity spans in the text
... | 3,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
>>> inputs = tokenizer(text, entity_spans=entity_spans, return_tensors="pt")
>>> outputs = model(**inputs)
>>> logits = outputs.logits
>>> predicted_class_indices = logits.argmax(-1).squeeze().tolist()
>>> for span, predicted_class_idx in zip(entity_spans, predicted_class_indices):
... | 3,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
outputs = self.luke(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
entity_ids=entity_ids,
entity_attention_mask=entity_attention_mask,
entity_token_type_ids=entity_token_ty... | 3,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
entity_start_positions = entity_start_positions.unsqueeze(-1).expand(-1, -1, hidden_size)
if entity_start_positions.device != outputs.last_hidden_state.device:
entity_start_positions = entity_start_positions.to(outputs.last_hidden_state.device)
start_states = torch.gather(outputs.last_hidden... | 3,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
loss = None
if labels is not None:
# move labels to correct device to enable model parallelism
labels = labels.to(logits.device)
# When the number of dimension of `labels` is 2, cross entropy is used as the loss function. The binary
# cross entropy is used otherwi... | 3,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
return EntitySpanClassificationOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
entity_hidden_states=outputs.entity_hidden_states,
attentions=outputs.attentions,
) | 3,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
class LukeForSequenceClassification(LukePreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.luke = LukeModel(config)
self.dropout = nn.Dropout(
config.classifier_dropout if config.classifier_dropout is not None e... | 3,918 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
@add_start_docstrings_to_model_forward(LUKE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=LukeSequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Opt... | 3,918 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, LukeSequenceClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices shoul... | 3,918 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
outputs = self.luke(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
entity_ids=entity_ids,
entity_attention_mask=entity_attention_mask,
entity_token_type_ids=entity_token_ty... | 3,918 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
loss = None
if labels is not None:
# move labels to correct device to enable model parallelism
labels = labels.to(logits.device)
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
... | 3,918 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "singl... | 3,918 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
return LukeSequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
entity_hidden_states=outputs.entity_hidden_states,
attentions=outputs.attentions,
) | 3,918 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
class LukeForTokenClassification(LukePreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.luke = LukeModel(config, add_pooling_layer=False)
self.dropout = nn.Dropout(
config.classifier_dropout if config.classifie... | 3,919 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
@add_start_docstrings_to_model_forward(LUKE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=LukeTokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Option... | 3,919 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, LukeTokenClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the multiple choice classification loss. Indices should be in... | 3,919 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
outputs = self.luke(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
entity_ids=entity_ids,
entity_attention_mask=entity_attention_mask,
entity_token_type_ids=entity_token_ty... | 3,919 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
loss = None
if labels is not None:
# move labels to correct device to enable model parallelism
labels = labels.to(logits.device)
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
if not return_dict:
... | 3,919 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
class LukeForQuestionAnswering(LukePreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.luke = LukeModel(config, add_pooling_layer=False)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
# Initial... | 3,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
@add_start_docstrings_to_model_forward(LUKE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=LukeQuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids:... | 3,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, LukeQuestionAnsweringModelOutput]:
r"""
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for posit... | 3,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 3,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
outputs = self.luke(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
entity_ids=entity_ids,
entity_attention_mask=entity_attention_mask,
entity_token_type_ids=entity_token_ty... | 3,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.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:
... | 3,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
if not return_dict:
return tuple(
v
for v in [
total_loss,
start_logits,
end_logits,
outputs.hidden_states,
outputs.entity_hidden_states,
outputs.attentions... | 3,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
class LukeForMultipleChoice(LukePreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.luke = LukeModel(config)
self.dropout = nn.Dropout(
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
)
se... | 3,921 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
@add_start_docstrings_to_model_forward(LUKE_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=LukeMultipleChoiceModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
... | 3,921 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, LukeMultipleChoiceModelOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the multiple choice classification loss. Indices should b... | 3,921 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
... | 3,921 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
entity_ids = entity_ids.view(-1, entity_ids.size(-1)) if entity_ids is not None else None
entity_attention_mask = (
entity_attention_mask.view(-1, entity_attention_mask.size(-1))
if entity_attention_mask is not None
else None
)
entity_token_type_ids = (
... | 3,921 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
outputs = self.luke(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
entity_ids=entity_ids,
entity_attention_mask=entity_attention_mask,
entity_token_type_ids=entity_token_ty... | 3,921 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
loss = None
if labels is not None:
# move labels to correct device to enable model parallelism
labels = labels.to(reshaped_logits.device)
loss_fct = CrossEntropyLoss()
loss = loss_fct(reshaped_logits, labels)
if not return_dict:
return tuple(
... | 3,921 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
class LukeTokenizer(PreTrainedTokenizer):
"""
Constructs a LUKE tokenizer, derived from the GPT-2 tokenizer, using byte-level Byte-Pair-Encoding.
This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
be encoded differently whether it is at th... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
</Tip>
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods. It also creates entity sequences, namely
`entity_ids`, `entity_attention_mask`, `entity_token_type_ids`, and `entity_posit... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
Args:
vocab_file (`str`):
Path to the vocabulary file.
merges_file (`str`):
Path to the merges file.
entity_vocab_file (`str`):
Path to the entity vocabulary file.
task (`str`, *optional*):
Task for which you want to prepare sequences. One ... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
`task` is set to `"entity_classification"` or `"entity_pair_classification"`.
entity_token_2 (`str`, *optional*, defaults to `<ent2>`):
The special token used to represent an entity span in a word token sequence. This token is only used when
`task` is set to `"entity_pair_classification"... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
<Tip>
When building a sequence using special tokens, this is not the token that is used for the beginning of
sequence. The token used is the `cls_token`.
</Tip>
eos_token (`str`, *optional*, defaults to `"</s>"`):
The end of sequence token.
<Tip>
... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
sep_token (`str`, *optional*, defaults to `"</s>"`):
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequenc... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
The token used for padding, for example when batching sequences of different lengths.
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 the token which the mode... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
vocab_files_names = VOCAB_FILES_NAMES
model_input_names = ["input_ids", "attention_mask"] | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
def __init__(
self,
vocab_file,
merges_file,
entity_vocab_file,
task=None,
max_entity_length=32,
max_mention_length=30,
entity_token_1="<ent>",
entity_token_2="<ent2>",
entity_unk_token="[UNK]",
entity_pad_token="[PAD]",
ent... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
cls_token = AddedToken(cls_token, lstrip=False, rstrip=False) if isinstance(cls_token, str) else cls_token
unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
# Mask token behave like a normal word, i.e. include the space before it
mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
with open(vocab_file, encoding="utf-8") as vocab_handle:
self.encoder = json.load(vocab_handle)
self... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")
# we add 2 special tokens for downstream tasks
# for more information about lstrip and rst... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
with open(entity_vocab_file, encoding="utf-8") as entity_vocab_handle:
self.entity_vocab = json.load(entity_vocab_handle)
for entity_special_token in [entity_unk_token, entity_pad_token, entity_mask_token, entity_mask2_token]:
if entity_special_token not in self.entity_vocab:
... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
self.task = task
if task is None or task == "entity_span_classification":
self.max_entity_length = max_entity_length
elif task == "entity_classification":
self.max_entity_length = 1
elif task == "entity_pair_classification":
self.max_entity_length = 2
... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
super().__init__(
errors=errors,
bos_token=bos_token,
eos_token=eos_token,
unk_token=unk_token,
sep_token=sep_token,
cls_token=cls_token,
pad_token=pad_token,
mask_token=mask_token,
add_prefix_space=add_prefix_sp... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.get_vocab with Roberta->Luke, RoBERTa->LUKE
def get_vocab(self):
vocab = dict(self.encoder).copy()
vocab.update(self.added_tokens_encoder)
return vocab
# Copied from transformers.models.roberta.tokenization_... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
while True:
bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
if bigram not in self.bpe_ranks:
break
first, second = bigram
new_word = []
i = 0
while i < len(word):
try:
... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
new_word.append(first + second)
i += 2
else:
new_word.append(word[i])
i += 1
new_word = tuple(new_word)
word = new_word
... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer._tokenize with Roberta->Luke, RoBERTa->LUKE
def _tokenize(self, text):
"""Tokenize a string."""
bpe_tokens = []
for token in re.findall(self.pat, text):
token = "".join(
self.byte_enco... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer._convert_id_to_token with Roberta->Luke, RoBERTa->LUKE
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
return self.decoder.get(index)
# Copied from transfor... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.build_inputs_with_special_tokens with Roberta->Luke, RoBERTa->LUKE
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Build model i... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
Returns:
`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
"""
if token_ids_1 is None:
return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
cls = [self.cls_token_id]
sep = [self.sep_token_id]
return cls ... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not the token list is ... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.create_token_type_ids_from_sequences with Roberta->Luke, RoBERTa->LUKE
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Creat... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.prepare_for_tokenization with Roberta->Luke, RoBERTa->LUKE
def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs):
add_prefix_space = kwargs.pop("add_prefix_space", self.add_prefix_space)
if (is_sp... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
def __call__(
self,
text: Union[TextInput, List[TextInput]],
text_pair: Optional[Union[TextInput, List[TextInput]]] = None,
entity_spans: Optional[Union[EntitySpanInput, List[EntitySpanInput]]] = No... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
re... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
Args:
text (`str`, `List[str]`, `List[List[str]]`):
The sequence or batch of sequences to be encoded. Each sequence must be a string. Note that this
tokenizer does not support tokenization based on pretokenized strings.
text_pair (`str`, `List[str]`, `List[List[st... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
the length of each sequence must be 1 or 2, respectively. If you specify `entities`, the length of each
sequence must be equal to the length of each sequence of `entities`.
entity_spans_pair (`List[Tuple[int, int]]`, `List[List[Tuple[int, int]]]`, *optional*):
The sequence or... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
representing entities, i.e., special entities (e.g., [MASK]) or entity titles of Wikipedia (e.g., Los
Angeles). This argument is ignored if you specify the `task` argument in the constructor. The length of
each sequence must be equal to the length of each sequence of `entity_spans`. If y... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
each sequence must be equal to the length of each sequence of `entity_spans_pair`. If you specify
`entity_spans_pair` without specifying this argument, the entity sequence or the batch of entity
sequences is automatically constructed by filling it with the [MASK] entity.
max_... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
is_valid_single_text_pair = isinstance(text_pair, str)
is_valid_batch_text_pair = isinstance(text_pair, (list, tuple)) and (
len(text_pair) == 0 or isinstance(text_pair[0], str)
)
if not (text_pair is None or is_valid_single_text_pair or is_valid_batch_text_pair):
raise V... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
if entity_spans is None:
batch_entity_spans_or_entity_spans_pairs = None
else:
batch_entity_spans_or_entity_spans_pairs = (
list(zip(entity_spans, entity_spans_pair)) if entity_spans_pair is not None else entity_spans
) | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
return self.batch_encode_plus(
batch_text_or_text_pairs=batch_text_or_text_pairs,
batch_entity_spans_or_entity_spans_pairs=batch_entity_spans_or_entity_spans_pairs,
batch_entities_or_entities_pairs=batch_entities_or_entities_pairs,
add_special_tokens=add_s... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
return_offsets_mapping=return_offsets_mapping,
return_length=return_length,
verbose=verbose,
**kwargs,
)
else:
return self.encode_plus(
text=text,
text_pair=text_pair,
entity_spans=entity_span... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
return_length=return_length,
verbose=ver... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
def _encode_plus(
self,
text: Union[TextInput],
text_pair: Optional[Union[TextInput]] = None,
entity_spans: Optional[EntitySpanInput] = None,
entity_spans_pair: Optional[EntitySpanInput] = None,
entities: Optional[EntityInput] = None,
entities_pair: Optional[Entit... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs,
) -> BatchEncoding:
if return_offsets_mapping:
raise NotImplementedError(
... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
if is_split_into_words:
raise NotImplementedError("is_split_into_words is not supported in this tokenizer.")
(
first_ids,
second_ids,
first_entity_ids,
second_entity_ids,
first_entity_token_spans,
second_entity_token_spans,
... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
# prepare_for_model will create the attention_mask and token_type_ids
return self.prepare_for_model(
first_ids,
pair_ids=second_ids,
entity_ids=first_entity_ids,
pair_entity_ids=second_entity_ids,
entity_token_spans=first_entity_token_spans,
... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
return_special_tokens_mask=return_special_tokens_mask,
return_length=return_length,
verbose=verbose,
) | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
def _batch_encode_plus(
self,
batch_text_or_text_pairs: Union[List[TextInput], List[TextInputPair]],
batch_entity_spans_or_entity_spans_pairs: Optional[
Union[List[EntitySpanInput], List[Tuple[EntitySpanInput, EntitySpanInput]]]
] = None,
batch_entities_or_entities_pa... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs,
) -> BatchEncoding:
if return_of... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
if is_split_into_words:
raise NotImplementedError("is_split_into_words is not supported in this tokenizer.")
# input_ids is a list of tuples (one for each example in the batch)
input_ids = []
entity_ids = []
entity_token_spans = []
for index, text_or_text_pair in enu... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
entities, entities_pair = None, None
if batch_entities_or_entities_pairs is not None:
entities_or_entities_pairs = batch_entities_or_entities_pairs[index]
if entities_or_entities_pairs:
if isinstance(entities_or_entities_pairs[0], str):
... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
entity_spans, entity_spans_pair = None, None
if batch_entity_spans_or_entity_spans_pairs is not None:
entity_spans_or_entity_spans_pairs = batch_entity_spans_or_entity_spans_pairs[index]
if len(entity_spans_or_entity_spans_pairs) > 0 and isinstance(
entity... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
(
first_ids,
second_ids,
first_entity_ids,
second_entity_ids,
first_entity_token_spans,
second_entity_token_spans,
) = self._create_input_sequence(
text=text,
text_pair=text_pair,
... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
batch_outputs = self._batch_prepare_for_model(
input_ids,
batch_entity_ids_pairs=entity_ids,
batch_entity_token_spans_pairs=entity_token_spans,
add_special_tokens=add_special_tokens,
padding_strategy=padding_strategy,
truncation_strategy=truncation... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
def _check_entity_input_format(self, entities: Optional[EntityInput], entity_spans: Optional[EntitySpanInput]):
if not isinstance(entity_spans, list):
raise TypeError("entity_spans should be given as a list")
elif len(entity_spans) > 0 and not isinstance(entity_spans[0], tuple):
... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
def _create_input_sequence(
self,
text: Union[TextInput],
text_pair: Optional[Union[TextInput]] = None,
entities: Optional[EntityInput] = None,
entities_pair: Optional[EntityInput] = None,
entity_spans: Optional[EntitySpanInput] = None,
entity_spans_pair: Optional... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
for split_char_position in split_char_positions:
orig_split_char_position = split_char_position
if (
split_char_position > 0 and text[split_char_position - 1] == " "
): # whitespace should be prepended to the following token
split_... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
first_ids, second_ids = None, None
first_entity_ids, second_entity_ids = None, None
first_entity_token_spans, second_entity_token_spans = None, None
if self.task is None:
if entity_spans is None:
first_ids = get_input_ids(text)
else:
self.... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.