text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
if not return_dict:
output = (reshaped_logits,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
return TFMultipleChoiceModelOutput(
loss=loss,
logits=reshaped_logits,
hidden_states=outputs.hidden_states,
attentions... | 3,843 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
class TFElectraForTokenClassification(TFElectraPreTrainedModel, TFTokenClassificationLoss):
def __init__(self, config, **kwargs):
super().__init__(config, **kwargs)
self.electra = TFElectraMainLayer(config, name="electra")
classifier_dropout = (
config.classifier_dropout if conf... | 3,844 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint="bhadresh-savani/electra-base-discriminator-finetuned-conll03-english",
output_type=TFTokenClassifierOutput,
config_class=_CONFIG_... | 3,844 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
labels: np.ndarray | tf.Tensor | None = None,
training: Optional[bool] = False,
) -> Union[TFTokenClassifierOutput, Tuple[tf.Tensor]]:
r"""
labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the token classification loss. Indices shoul... | 3,844 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
logits = self.classifier(discriminator_sequence_output)
loss = None if labels is None else self.hf_compute_loss(labels, logits) | 3,844 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
if not return_dict:
output = (logits,) + discriminator_hidden_states[1:]
return ((loss,) + output) if loss is not None else output
return TFTokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=discriminator_hidden_states.hidden_states,
... | 3,844 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
class TFElectraForQuestionAnswering(TFElectraPreTrainedModel, TFQuestionAnsweringLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.electra = TFElectraMainLayer(config, name="electra")
self.qa_ou... | 3,845 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint="bhadresh-savani/electra-base-squad2",
output_type=TFQuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
qa_target... | 3,845 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
start_positions: np.ndarray | tf.Tensor | None = None,
end_positions: np.ndarray | tf.Tensor | None = None,
training: Optional[bool] = False,
) -> Union[TFQuestionAnsweringModelOutput, Tuple[tf.Tensor]]:
r"""
start_positions (`tf.Tensor` of shape `(batch_size,)`, *optional*):
... | 3,845 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
discriminator_hidden_states = self.electra(
input_ids=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_attenti... | 3,845 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
if start_positions is not None and end_positions is not None:
labels = {"start_position": start_positions}
labels["end_position"] = end_positions
loss = self.hf_compute_loss(labels, (start_logits, end_logits))
if not return_dict:
output = (
start_... | 3,845 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "electra", None) is not None:
with tf.name_scope(self.electra.name):
self.electra.build(None)
if getattr(self, "qa_outputs", None) is not None:
... | 3,845 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_tf_electra.py |
class ElectraTokenizer(PreTrainedTokenizer):
r"""
Construct a Electra 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. | 3,846 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/tokenization_electra.py |
Args:
vocab_file (`str`):
File containing the vocabulary.
do_lower_case (`bool`, *optional*, defaults to `True`):
Whether or not to lowercase the input when tokenizing.
do_basic_tokenize (`bool`, *optional*, defaults to `True`):
Whether or not to do basic toke... | 3,846 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/tokenization_electra.py |
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.
pad_token (`str`, *optional*, defaults to `"[PAD]"`):
The token used for padding, for example when batching sequences of different lengt... | 3,846 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/tokenization_electra.py |
Whether or not to tokenize Chinese characters. | 3,846 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/tokenization_electra.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... | 3,846 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/tokenization_electra.py |
def __init__(
self,
vocab_file,
do_lower_case=True,
do_basic_tokenize=True,
never_split=None,
unk_token="[UNK]",
sep_token="[SEP]",
pad_token="[PAD]",
cls_token="[CLS]",
mask_token="[MASK]",
tokenize_chinese_chars=True,
stri... | 3,846 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/tokenization_electra.py |
do_lower_case=do_lower_case,
never_split=never_split,
tokenize_chinese_chars=tokenize_chinese_chars,
strip_accents=strip_accents,
) | 3,846 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/tokenization_electra.py |
self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=str(unk_token))
super().__init__(
do_lower_case=do_lower_case,
do_basic_tokenize=do_basic_tokenize,
never_split=never_split,
unk_token=unk_token,
sep_token=sep_token,
... | 3,846 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/tokenization_electra.py |
def _tokenize(self, text, split_special_tokens=False):
split_tokens = []
if self.do_basic_tokenize:
for token in self.basic_tokenizer.tokenize(
text, never_split=self.all_special_tokens if not split_special_tokens else None
):
# If the token is par... | 3,846 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/tokenization_electra.py |
def convert_tokens_to_string(self, tokens):
"""Converts a sequence of tokens (string) in a single string."""
out_string = " ".join(tokens).replace(" ##", "").strip()
return out_string
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]... | 3,846 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/tokenization_electra.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 ... | 3,846 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/tokenization_electra.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 ... | 3,846 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/tokenization_electra.py |
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task. A Electra sequence
pair mask has the following format:
... | 3,846 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/tokenization_electra.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_... | 3,846 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/tokenization_electra.py |
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
index = 0
if os.path.isdir(save_directory):
vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
... | 3,846 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/tokenization_electra.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... | 3,847 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/tokenization_electra.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... | 3,847 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/tokenization_electra.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(... | 3,847 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/tokenization_electra.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... | 3,847 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/tokenization_electra.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
#... | 3,847 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/tokenization_electra.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)) | 3,847 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/tokenization_electra.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... | 3,847 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/tokenization_electra.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 = []
... | 3,847 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/tokenization_electra.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(" ")
... | 3,847 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/tokenization_electra.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 ... | 3,847 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/tokenization_electra.py |
or (cp >= 0x2F800 and cp <= 0x2FA1F) #
): #
return True | 3,847 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/tokenization_electra.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... | 3,847 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/tokenization_electra.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... | 3,848 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/tokenization_electra.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 = []
... | 3,848 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/tokenization_electra.py |
if is_bad:
output_tokens.append(self.unk_token)
else:
output_tokens.extend(sub_tokens)
return output_tokens | 3,848 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/tokenization_electra.py |
class ElectraTokenizerFast(PreTrainedTokenizerFast):
r"""
Construct a "fast" ELECTRA tokenizer (backed by HuggingFace's *tokenizers* library). Based on WordPiece.
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
refer to this superclass for ... | 3,849 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/tokenization_electra_fast.py |
Args:
vocab_file (`str`):
File containing the vocabulary.
do_lower_case (`bool`, *optional*, defaults to `True`):
Whether or not to lowercase the input when tokenizing.
unk_token (`str`, *optional*, defaults to `"[UNK]"`):
The unknown token. A token that is no... | 3,849 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/tokenization_electra_fast.py |
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`, *optional*, defaults to `"[MASK]"`):
The token used f... | 3,849 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/tokenization_electra_fast.py |
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 `lowercase` (as in the original ELECTRA).
wordpieces_prefix (`str`, *optional*, defaults to `"##"`):
The prefix for subwords.
... | 3,849 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/tokenization_electra_fast.py |
vocab_files_names = VOCAB_FILES_NAMES
slow_tokenizer_class = ElectraTokenizer
def __init__(
self,
vocab_file=None,
tokenizer_file=None,
do_lower_case=True,
unk_token="[UNK]",
sep_token="[SEP]",
pad_token="[PAD]",
cls_token="[CLS]",
mask_to... | 3,849 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/tokenization_electra_fast.py |
normalizer_state = json.loads(self.backend_tokenizer.normalizer.__getstate__())
if (
normalizer_state.get("lowercase", do_lower_case) != do_lower_case
or normalizer_state.get("strip_accents", strip_accents) != strip_accents
or normalizer_state.get("handle_chinese_chars", toke... | 3,849 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/tokenization_electra_fast.py |
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A ELECTRA sequence has the following format:
- single sequence: `[CLS] X ... | 3,849 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/tokenization_electra_fast.py |
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task. A ELECTRA sequence
pair mask has the following format:
... | 3,849 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/tokenization_electra_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_... | 3,849 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/tokenization_electra_fast.py |
class ElectraEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.embedding_size, padding_idx=config.pad_token_id)
self.position_e... | 3,850 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
# position_ids (1, len position emb) is contiguous in memory and exported when serialized
self.register_buffer(
"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
)
self.position_embedding_type = getattr(config, "position_embedding_type", "... | 3,850 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
# Copied from transformers.models.bert.modeling_bert.BertEmbeddings.forward
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.Float... | 3,850 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
# Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs
# when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves
# issue #5664
if token_type_ids is None:
if hasattr(... | 3,850 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
embeddings = inputs_embeds + token_type_embeddings
if self.position_embedding_type == "absolute":
position_embeddings = self.position_embeddings(position_ids)
embeddings += position_embeddings
embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddings)
... | 3,850 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
class ElectraSelfAttention(nn.Module):
def __init__(self, config, position_embedding_type=None):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size ({config.hidden_size}... | 3,851 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
self.position_embedding_type = position_embedding_type or getattr(
config, "position_embedding_type", "absolute"
)
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":... | 3,851 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
... | 3,851 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.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... | 3,851 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
query_layer = self.transpose_for_scores(mixed_query_layer)
use_cache = past_key_value is not None
if self.is_decoder:
# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all ... | 3,851 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
query_length, key_length = q... | 3,851 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility
if self.position_embedding_type == "relative_key":
relative_position_scores = torch.einsum("b... | 3,851 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in ElectraModel forward() function)
attention_scores = attention_scores + attention_mask
# Normalize the attention s... | 3,851 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(new_context_layer_shape)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
i... | 3,851 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
class ElectraSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
de... | 3,852 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
class ElectraAttention(nn.Module):
def __init__(self, config, position_embedding_type=None):
super().__init__()
self.self = ELECTRA_SELF_ATTENTION_CLASSES[config._attn_implementation](
config, position_embedding_type=position_embedding_type
)
self.output = ElectraSelfOutp... | 3,853 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
# Update hyper params and store pruned heads
self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads) | 3,853 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
... | 3,853 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
class ElectraIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.int... | 3,854 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
class ElectraOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
... | 3,855 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
class ElectraLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = ElectraAttention(config)
self.is_decoder = config.is_decoder
self.add_cross_attention = co... | 3,856 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
... | 3,856 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
# if decoder, the last output is tuple of self-attn cache
if self.is_decoder:
outputs = self_attention_outputs[1:-1]
present_key_value = self_attention_outputs[-1]
else:
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
... | 3,856 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
# cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
cross_attention_outputs = self.crossattention(
attention_output,
attention_mask,
... | 3,856 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
layer_output = apply_chunking_to_forward(
self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
)
outputs = (layer_output,) + outputs
# if decoder, return the attn key/values as the last output
if self.is_decoder:
outputs = out... | 3,856 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
class ElectraEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([ElectraLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False | 3,857 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
... | 3,857 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
next_decoder_cache = () if use_ca... | 3,857 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.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,
... | 3,857 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.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... | 3,857 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.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,
]
... | 3,857 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
class ElectraDiscriminatorPredictions(nn.Module):
"""Prediction module for the discriminator, made up of two dense layers."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = get_activation(config.hidden_act... | 3,858 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
class ElectraGeneratorPredictions(nn.Module):
"""Prediction module for the generator, made up of two dense layers."""
def __init__(self, config):
super().__init__()
self.activation = get_activation("gelu")
self.LayerNorm = nn.LayerNorm(config.embedding_size, eps=config.layer_norm_eps)
... | 3,859 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
class ElectraPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = ElectraConfig
load_tf_weights = load_tf_weights_in_electra
base_model_prefix = "electra"
support... | 3,860 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
# Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights
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 ... | 3,860 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
class ElectraForPreTrainingOutput(ModelOutput):
"""
Output type of [`ElectraForPreTraining`].
Args:
loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
Total loss of the ELECTRA objective.
logits (`torch.FloatTensor` of shape `(batch_size,... | 3,861 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch... | 3,861 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
class ElectraModel(ElectraPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.embeddings = ElectraEmbeddings(config)
if config.embedding_size != config.hidden_size:
self.embeddings_project = nn.Linear(config.embedding_size, config.hidden_size)
se... | 3,862 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
@add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithCrossAttentions,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input... | 3,862 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
) -> Union[Tuple[torch.Tensor], BaseModelOutputWithCrossAttentions]:
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_stat... | 3,862 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
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")
elif input_ids is not None:
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
input_shape = input_ids.size()
... | 3,862 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
if attention_mask is None:
attention_mask = torch.ones(input_shape, device=device)
if token_type_ids is None:
if hasattr(self.embeddings, "token_type_ids"):
buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
buffered_token_type_ids_ex... | 3,862 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.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()... | 3,862 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
hidden_states = 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,
)
if hasattr(self, "embeddings_project"):
h... | 3,862 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
class ElectraClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
classifier_dropout = (
config.classifier_dropout if config.classifier... | 3,863 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
class ElectraForSequenceClassification(ElectraPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.electra = ElectraModel(config)
self.classifier = ElectraClassificationHead(config)
# Ini... | 3,864 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
@add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint="bhadresh-savani/electra-base-emotion",
output_type=SequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
expected_output="'joy'",
... | 3,864 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
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_lab... | 3,864 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
discriminator_hidden_states = self.electra(
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,
... | 3,864 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_electra.py |
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