text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None | 9,969 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
next_decoder_cache = () if use_cache else None
for i, layer_module in enumerate(self.layer):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_head_mask = head_mask[i] if head_mask is not None else None
past_key_value = p... | 9,969 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
layer_module.__call__,
hidden_states,
attention_mask,
layer_head_mask,
encoder_hidden_states,
... | 9,969 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[-1],)
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[1],)
if self.config.add_cross_attention:
all_cross_attent... | 9,969 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
if not return_dict:
return tuple(
v
for v in [
hidden_states,
next_decoder_cache,
all_hidden_states,
all_self_attentions,
all_cross_attentions,
]
... | 9,969 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
class RemBertPredictionHeadTransform(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
if isinstance(config.hidden_act, str):
self.transform_act_fn = ACT2FN[config.hidden_act]
else:
self.t... | 9,970 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
class RemBertLMPredictionHead(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.output_embedding_size)
self.decoder = nn.Linear(config.output_embedding_size, config.vocab_size)
self.activation = ACT2FN[config.hidden_act]
... | 9,971 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
class RemBertOnlyMLMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = RemBertLMPredictionHead(config)
def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
prediction_scores = self.predictions(sequence_output)
return prediction_scores | 9,972 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
class RemBertPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = RemBertConfig
load_tf_weights = load_tf_weights_in_rembert
base_model_prefix = "rembert"
support... | 9,973 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, nn.Linear):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.... | 9,973 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
class RemBertModel(RemBertPreTrainedModel):
"""
The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
cross-attention is added between the self-attention layers, following the architecture described in [Attention is
all you need](https://arxiv.org/... | 9,974 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
self.embeddings = RemBertEmbeddings(config)
self.encoder = RemBertEncoder(config)
self.pooler = RemBertPooler(config) if add_pooling_layer else None
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embeddings.word... | 9,974 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
@add_start_docstrings_to_model_forward(REMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint="google/rembert",
output_type=BaseModelOutputWithPastAndCrossAttentions,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
i... | 9,974 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPoolingAndCrossAttentions]:
r"""
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encod... | 9,974 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
Contains precomputed ... | 9,974 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None els... | 9,974 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
if self.config.is_decoder:
use_cache = use_cache if use_cache is not None else self.config.use_cache
else:
use_cache = False
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time... | 9,974 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
if attention_mask is None:
attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)
if token_type_ids is None:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
# We can provide a self-attention mask of dimensions [... | 9,974 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
# If a 2D or 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if self.config.is_decoder and encoder_hidden_states is not None:
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()... | 9,974 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_lengt... | 9,974 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
embedding_output = self.embeddings(
input_ids=input_ids,
position_ids=position_ids,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
past_key_values_length=past_key_values_length,
)
encoder_outputs = self.encoder(
embeddi... | 9,974 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
return BaseModelOutputWithPoolingAndCrossAttentions(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
past_key_values=encoder_outputs.past_key_values,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
... | 9,974 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
class RemBertForMaskedLM(RemBertPreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder.weight"]
def __init__(self, config):
super().__init__(config)
if config.is_decoder:
logger.warning(
"If you want to use `RemBertForMaskedLM` make sure `config.is_decoder... | 9,975 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
@add_start_docstrings_to_model_forward(REMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint="google/rembert",
output_type=MaskedLMOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: torch.LongTensor ... | 9,975 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
l... | 9,975 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
outputs = self.rembert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_hidden_states,
encod... | 9,975 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
if not return_dict:
output = (prediction_scores,) + outputs[2:]
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
return MaskedLMOutput(
loss=masked_lm_loss,
logits=prediction_scores,
hidden_states=outputs.hidden_states,
... | 9,975 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
return {"input_ids": input_ids, "attention_mask": attention_mask} | 9,975 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
class RemBertForCausalLM(RemBertPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["cls.predictions.decoder.weight"]
def __init__(self, config):
super().__init__(config)
if not config.is_decoder:
logger.warning("If you want to use `RemBertForCausalLM` as a standalone, add `is... | 9,976 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
@add_start_docstrings_to_model_forward(REMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[t... | 9,976 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
return_dict: Optional[bool] = None,
) -> Union[Tuple, CausalLMOutputWithCrossAttentions]:
r"""
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in... | 9,976 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
Contains precomputed ... | 9,976 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
`[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are
ignored (masked), the loss is only computed for the tokens with labels n `[0, ..., config.vocab_size]`.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value stat... | 9,976 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, RemBertForCausalLM, RemBertConfig
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("google/rembert")
>>> config = RemBertConfig.from_pretrained("google/rembert")
>>> config.is... | 9,976 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
outputs = self.rembert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_hidden_states,
encod... | 9,976 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
lm_loss = None
if labels is not None:
# we are doing next-token prediction; shift prediction scores and input ids by one
shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous()
labels = labels[:, 1:].contiguous()
loss_fct = CrossEntropyLoss()
... | 9,976 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
def _reorder_cache(self, past_key_values, beam_idx):
reordered_past = ()
for layer_past in past_key_values:
reordered_past += (
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past[:2])
+ layer_past[2:],
)
... | 9,976 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
class RemBertForSequenceClassification(RemBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.rembert = RemBertModel(config)
self.dropout = nn.Dropout(config.classifier_dropout_prob)
self.classifier = nn.Linear(c... | 9,977 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
@add_start_docstrings_to_model_forward(REMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint="google/rembert",
output_type=SequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: torch.F... | 9,977 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
... | 9,977 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
outputs = self.rembert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden... | 9,977 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.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... | 9,977 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
class RemBertForMultipleChoice(RemBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.rembert = RemBertModel(config)
self.dropout = nn.Dropout(config.classifier_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, 1)
# Initialize weights... | 9,978 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
@add_start_docstrings_to_model_forward(REMBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_code_sample_docstrings(
checkpoint="google/rembert",
output_type=MultipleChoiceModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
inpu... | 9,978 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
`input_ids` above)
"""
return_dict = return_dict if return_dict is not None else self.co... | 9,978 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.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
... | 9,978 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
reshaped_logits = logits.view(-1, num_choices)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(reshaped_logits... | 9,978 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
class RemBertForTokenClassification(RemBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.rembert = RemBertModel(config, add_pooling_layer=False)
self.dropout = nn.Dropout(config.classifier_dropout_prob)
self.c... | 9,979 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
@add_start_docstrings_to_model_forward(REMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint="google/rembert",
output_type=TokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: torch.Floa... | 9,979 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 9,979 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
outputs = self.rembert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden... | 9,979 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
) | 9,979 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
class RemBertForQuestionAnswering(RemBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.rembert = RemBertModel(config, add_pooling_layer=False)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
... | 9,980 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
@add_start_docstrings_to_model_forward(REMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint="google/rembert",
output_type=QuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: tor... | 9,980 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
... | 9,980 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
outputs = self.rembert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden... | 9,980 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.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:
... | 9,980 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.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,
... | 9,980 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py |
class RemBertTokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "fast" RemBert tokenizer (backed by HuggingFace's *tokenizers* library). Based on
[Unigram](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=unigram#models). This
tokenizer inherits from [`PreTrainedTokeni... | 9,981 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/tokenization_rembert_fast.py |
Args:
vocab_file (`str`):
[SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that
contains the vocabulary necessary to instantiate a tokenizer.
do_lower_case (`bool`, *optional*, defaults to `True`):
Whether or not to lowe... | 9,981 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/tokenization_rembert_fast.py |
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> | 9,981 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/tokenization_rembert_fast.py |
eos_token (`str`, *optional*, defaults to `"[SEP]"`):
The end of sequence token. .. note:: When building a sequence using special tokens, this is not the token
that is used for the end of sequence. The token used is the `sep_token`.
unk_token (`str`, *optional*, defaults to `"<unk>"`):
... | 9,981 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/tokenization_rembert_fast.py |
cls_token (`str`, *optional*, defaults to `"[CLS]"`):
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
mask_token (`str`,... | 9,981 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/tokenization_rembert_fast.py |
vocab_files_names = VOCAB_FILES_NAMES
slow_tokenizer_class = RemBertTokenizer
def __init__(
self,
vocab_file=None,
tokenizer_file=None,
do_lower_case=True,
remove_space=True,
keep_accents=False,
bos_token="[CLS]",
eos_token="[SEP]",
unk_to... | 9,981 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/tokenization_rembert_fast.py |
super().__init__(
vocab_file,
tokenizer_file=tokenizer_file,
do_lower_case=do_lower_case,
remove_space=remove_space,
keep_accents=keep_accents,
bos_token=bos_token,
eos_token=eos_token,
unk_token=unk_token,
sep_t... | 9,981 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/tokenization_rembert_fast.py |
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A RemBERT sequence ha... | 9,981 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/tokenization_rembert_fast.py |
Returns:
`List[int]`: list of [input IDs](../glossary#input-ids) with the appropriate special tokens.
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return cls + token_ids_0 + sep
return cls + token_ids_0 + sep + token_ids_... | 9,981 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/tokenization_rembert_fast.py |
Args:
token_ids_0 (`List[int]`):
List of ids.
token_ids_1 (`List[int]`, *optional*, defaults to `None`):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
Set to True i... | 9,981 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/tokenization_rembert_fast.py |
if token_ids_1 is not None:
return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
return [1] + ([0] * len(token_ids_0)) + [1]
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
... | 9,981 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/tokenization_rembert_fast.py |
Returns:
`List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
return len(cls + token... | 9,981 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/tokenization_rembert_fast.py |
class RemBertConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`RemBertModel`]. It is used to instantiate an
RemBERT model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a sim... | 9,982 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/configuration_rembert.py |
Args:
vocab_size (`int`, *optional*, defaults to 250300):
Vocabulary size of the RemBERT model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`RemBertModel`] or [`TFRemBertModel`]. Vocabulary size of the model.
Defines... | 9,982 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/configuration_rembert.py |
output_embedding_size (`int`, *optional*, defaults to 1664):
Dimensionality of the output embeddings.
intermediate_size (`int`, *optional*, defaults to 4608):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `functio... | 9,982 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/configuration_rembert.py |
max_position_embeddings (`int`, *optional*, defaults to 512):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
type_vocab_size (`int`, *optional*, defaults to 2):
The vocabular... | 9,982 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/configuration_rembert.py |
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`. | 9,982 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/configuration_rembert.py |
Example:
```python
>>> from transformers import RemBertModel, RemBertConfig
>>> # Initializing a RemBERT rembert style configuration
>>> configuration = RemBertConfig()
>>> # Initializing a model from the rembert style configuration
>>> model = RemBertModel(configuration)
>>> # Accessing... | 9,982 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/configuration_rembert.py |
def __init__(
self,
vocab_size=250300,
hidden_size=1152,
num_hidden_layers=32,
num_attention_heads=18,
input_embedding_size=256,
output_embedding_size=1664,
intermediate_size=4608,
hidden_act="gelu",
hidden_dropout_prob=0.0,
attenti... | 9,982 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/configuration_rembert.py |
self.vocab_size = vocab_size
self.input_embedding_size = input_embedding_size
self.output_embedding_size = output_embedding_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_atten... | 9,982 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/configuration_rembert.py |
class RemBertOnnxConfig(OnnxConfig):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
else:
dynamic_axis = {0: "batch", 1: "sequence"}
return OrderedDict(
... | 9,983 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/configuration_rembert.py |
class RemBertTokenizer(PreTrainedTokenizer):
"""
Construct a RemBERT tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information reg... | 9,984 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/tokenization_rembert.py |
eos_token (`str`, *optional*, defaults to `"[SEP]"`):
The end of sequence token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the end of sequence.
The token used is the `sep_token`.
</Tip> | 9,984 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/tokenization_rembert.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... | 9,984 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/tokenization_rembert.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... | 9,984 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/tokenization_rembert.py |
Attributes:
sp_model (`SentencePieceProcessor`):
The *SentencePiece* processor that is used for every conversion (string, tokens and IDs).
"""
vocab_files_names = VOCAB_FILES_NAMES
def __init__(
self,
vocab_file,
do_lower_case=False,
remove_space=True,
... | 9,984 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/tokenization_rembert.py |
self.sp_model = spm.SentencePieceProcessor()
self.sp_model.Load(vocab_file)
super().__init__(
do_lower_case=do_lower_case,
remove_space=remove_space,
keep_accents=keep_accents,
bos_token=bos_token,
eos_token=eos_token,
unk_token=unk... | 9,984 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/tokenization_rembert.py |
def __setstate__(self, d):
self.__dict__ = d
self.sp_model = spm.SentencePieceProcessor()
self.sp_model.Load(self.vocab_file)
def _tokenize(self, text, sample=False):
"""Tokenize a string."""
pieces = self.sp_model.EncodeAsPieces(text)
return pieces
def _convert... | 9,984 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/tokenization_rembert.py |
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A REMBERT sequence ha... | 9,984 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/tokenization_rembert.py |
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 token list that has no special tokens added. This method is called when adding
special tokens ... | 9,984 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/tokenization_rembert.py |
if already_has_special_tokens:
if token_ids_1 is not None:
raise ValueError(
"You should not supply a second sequence if the provided sequence of "
"ids is already formatted with special tokens for the model."
)
return [1 if... | 9,984 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/tokenization_rembert.py |
```
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence | second sequence |
```
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 (`L... | 9,984 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/tokenization_rembert.py |
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if not os.path.isdir(save_directory):
logger.error("Vocabulary path ({}) should be a directory".format(save_directory))
return
out_vocab_file = os.path.join(
save_dire... | 9,984 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/tokenization_rembert.py |
class TFRemBertEmbeddings(keras.layers.Layer):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config: RemBertConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.input_embedding_size = config.input_embedding_size
... | 9,985 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_tf_rembert.py |
with tf.name_scope("token_type_embeddings"):
self.token_type_embeddings = self.add_weight(
name="embeddings",
shape=[self.config.type_vocab_size, self.input_embedding_size],
initializer=get_initializer(self.initializer_range),
)
with tf.na... | 9,985 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_tf_rembert.py |
def call(
self,
input_ids: tf.Tensor = None,
position_ids: tf.Tensor = None,
token_type_ids: tf.Tensor = None,
inputs_embeds: tf.Tensor = None,
past_key_values_length=0,
training: bool = False,
) -> tf.Tensor:
"""
Applies embedding based on inp... | 9,985 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_tf_rembert.py |
if position_ids is None:
position_ids = tf.expand_dims(
tf.range(start=past_key_values_length, limit=input_shape[1] + past_key_values_length), axis=0
)
position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids)
token_type_embeds = tf.gathe... | 9,985 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_tf_rembert.py |
class TFRemBertSelfAttention(keras.layers.Layer):
def __init__(self, config: RemBertConfig, **kwargs):
super().__init__(**kwargs)
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the ... | 9,986 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_tf_rembert.py |
self.query = keras.layers.Dense(
units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="query"
)
self.key = keras.layers.Dense(
units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="key"
)
... | 9,986 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_tf_rembert.py |
# Transpose the tensor from [batch_size, seq_length, num_attention_heads, attention_head_size] to [batch_size, num_attention_heads, seq_length, attention_head_size]
return tf.transpose(tensor, perm=[0, 2, 1, 3])
def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
... | 9,986 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_tf_rembert.py |
if is_cross_attention and past_key_value is not None:
# reuse k,v, cross_attentions
key_layer = past_key_value[0]
value_layer = past_key_value[1]
attention_mask = encoder_attention_mask
elif is_cross_attention:
key_layer = self.transpose_for_scores(sel... | 9,986 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_tf_rembert.py |
key_layer = self.transpose_for_scores(self.key(inputs=hidden_states), batch_size)
value_layer = self.transpose_for_scores(self.value(inputs=hidden_states), batch_size) | 9,986 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_tf_rembert.py |
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