text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
@add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_MASKED_LM,
output_type=MaskedLMOutput,
config_class=_CONFIG_FOR_DOC,
mask="[MASK]",
expected_output=_MASKED_LM_EXPECT... | 3,939 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
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
loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
""" | 3,939 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.deberta(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
inputs_embeds=inputs_embeds,
... | 3,939 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
if not return_dict:
output = (prediction_scores,) + outputs[1:]
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,
... | 3,939 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
class ContextPooler(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.pooler_hidden_size, config.pooler_hidden_size)
self.dropout = nn.Dropout(config.pooler_dropout)
self.config = config
def forward(self, hidden_states):
# We "pool"... | 3,940 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
class DebertaForSequenceClassification(DebertaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
num_labels = getattr(config, "num_labels", 2)
self.num_labels = num_labels
self.deberta = DebertaModel(config)
self.pooler = ContextPooler(config)
ou... | 3,941 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
@add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=SequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Opti... | 3,941 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
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).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 3,941 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
outputs = self.deberta(
input_ids,
token_type_ids=token_type_ids,
attention_mask=attention_mask,
position_ids=position_ids,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
... | 3,941 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
# regression task
loss_fn = nn.MSELoss()
logits = logits.view(-1).to(labels.dtype)
loss = loss_fn(logits, l... | 3,941 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
loss = torch.tensor(0).to(logits)
else:
log_softmax = nn.LogSoftmax(-1)
loss = -((log_softmax(logits) * labels).sum(-1)).mean()
elif self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:... | 3,941 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
return SequenceClassifierOutput(
loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions
) | 3,941 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
class DebertaForTokenClassification(DebertaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.deberta = DebertaModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.... | 3,942 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
@add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optiona... | 3,942 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.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 | 3,942 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
outputs = self.deberta(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
... | 3,942 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
class DebertaForQuestionAnswering(DebertaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.deberta = DebertaModel(config)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights an... | 3,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
@add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_QA,
output_type=QuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
expected_output=_QA_EXPECTED_OUTPUT,
expe... | 3,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
) -> Union[Tuple, QuestionAnsweringModelOutput]:
r"""
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 s... | 3,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
outputs = self.deberta(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
... | 3,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.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:
... | 3,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
if not return_dict:
output = (start_logits, end_logits) + outputs[1:]
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,
... | 3,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
class DebertaTokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "fast" DeBERTa tokenizer (backed by HuggingFace's *tokenizers* library). Based on 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... | 3,944 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/tokenization_deberta_fast.py |
When used with `is_split_into_words=True`, this tokenizer needs to be instantiated with `add_prefix_space=True`.
</Tip>
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods. | 3,944 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/tokenization_deberta_fast.py |
Args:
vocab_file (`str`, *optional*):
Path to the vocabulary file.
merges_file (`str`, *optional*):
Path to the merges file.
tokenizer_file (`str`, *optional*):
The path to a tokenizer file to use instead of the vocab file.
errors (`str`, *optional*, d... | 3,944 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/tokenization_deberta_fast.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.
cls_token (`str`, *optional*, defaults to `"[CLS]"`):
The classifier token which is used when doing sequence classification (classificat... | 3,944 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/tokenization_deberta_fast.py |
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 model will try to predict.
add_prefix_space (`bool`, *optional*, defaults to `False`):
Whether or not to add an initial space to the input. This a... | 3,944 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/tokenization_deberta_fast.py |
vocab_files_names = VOCAB_FILES_NAMES
model_input_names = ["input_ids", "attention_mask", "token_type_ids"]
slow_tokenizer_class = DebertaTokenizer | 3,944 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/tokenization_deberta_fast.py |
def __init__(
self,
vocab_file=None,
merges_file=None,
tokenizer_file=None,
errors="replace",
bos_token="[CLS]",
eos_token="[SEP]",
sep_token="[SEP]",
cls_token="[CLS]",
unk_token="[UNK]",
pad_token="[PAD]",
mask_token="[MAS... | 3,944 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/tokenization_deberta_fast.py |
@property
def mask_token(self) -> str:
"""
`str`: Mask token, to use when training a model with masked-language modeling. Log an error if used while not
having been set.
Deberta tokenizer has a special mask token to be used in the fill-mask pipeline. The mask token will greedily
... | 3,944 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/tokenization_deberta_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 DeBERTa sequence ha... | 3,944 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/tokenization_deberta_fast.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 ... | 3,944 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/tokenization_deberta_fast.py |
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).... | 3,944 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/tokenization_deberta_fast.py |
# Copied from transformers.models.gpt2.tokenization_gpt2_fast.GPT2TokenizerFast._batch_encode_plus
def _batch_encode_plus(self, *args, **kwargs) -> BatchEncoding:
is_split_into_words = kwargs.get("is_split_into_words", False)
assert self.add_prefix_space or not is_split_into_words, (
f"Y... | 3,944 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/tokenization_deberta_fast.py |
return super()._encode_plus(*args, **kwargs)
# Copied from transformers.models.gpt2.tokenization_gpt2_fast.GPT2TokenizerFast.save_vocabulary
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
files = self._tokenizer.model.save(save_directory, name=filen... | 3,944 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/tokenization_deberta_fast.py |
class DebertaConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`DebertaModel`] or a [`TFDebertaModel`]. It is
used to instantiate a DeBERTa model according to the specified arguments, defining the model architecture.
Instantiating a configuration with the de... | 3,945 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/configuration_deberta.py |
Arguments:
vocab_size (`int`, *optional*, defaults to 50265):
Vocabulary size of the DeBERTa model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`DebertaModel`] or [`TFDebertaModel`].
hidden_size (`int`, *optional*, defau... | 3,945 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/configuration_deberta.py |
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"`, `"gelu"`, `"tanh"`, `"gelu_fast"`, `"mish"`, `"linear"`, `"sigmoid"` and `"gelu_new"`
are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
... | 3,945 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/configuration_deberta.py |
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
relative_attent... | 3,945 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/configuration_deberta.py |
The type of relative position attention, it can be a combination of `["p2c", "c2p"]`, e.g. `["p2c"]`,
`["p2c", "c2p"]`.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
legacy (`bool`, *optional*, defaults to `True`):
... | 3,945 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/configuration_deberta.py |
Example:
```python
>>> from transformers import DebertaConfig, DebertaModel
>>> # Initializing a DeBERTa microsoft/deberta-base style configuration
>>> configuration = DebertaConfig()
>>> # Initializing a model (with random weights) from the microsoft/deberta-base style configuration
>>> mode... | 3,945 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/configuration_deberta.py |
def __init__(
self,
vocab_size=50265,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
... | 3,945 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/configuration_deberta.py |
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_pro... | 3,945 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/configuration_deberta.py |
self.pooler_hidden_size = kwargs.get("pooler_hidden_size", hidden_size)
self.pooler_dropout = pooler_dropout
self.pooler_hidden_act = pooler_hidden_act
self.legacy = legacy | 3,945 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/configuration_deberta.py |
class DebertaOnnxConfig(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"}
if self._config.type_voc... | 3,946 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/configuration_deberta.py |
def generate_dummy_inputs(
self,
preprocessor: Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"],
batch_size: int = -1,
seq_length: int = -1,
num_choices: int = -1,
is_pair: bool = False,
framework: Optional["TensorType"] = None,
num_channels: int... | 3,946 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/configuration_deberta.py |
class TFDebertaContextPooler(keras.layers.Layer):
def __init__(self, config: DebertaConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(config.pooler_hidden_size, name="dense")
self.dropout = TFDebertaStableDropout(config.pooler_dropout, name="dropout")
self... | 3,947 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "dense", None) is not None:
with tf.name_scope(self.dense.name):
self.dense.build([None, None, self.config.pooler_hidden_size])
if getattr(self, "dropout", ... | 3,947 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
class TFDebertaXSoftmax(keras.layers.Layer):
"""
Masked Softmax which is optimized for saving memory
Args:
input (`tf.Tensor`): The input tensor that will apply softmax.
mask (`tf.Tensor`): The mask matrix where 0 indicate that element will be ignored in the softmax calculation.
dim... | 3,948 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
class TFDebertaStableDropout(keras.layers.Layer):
"""
Optimized dropout module for stabilizing the training
Args:
drop_prob (float): the dropout probabilities
"""
def __init__(self, drop_prob, **kwargs):
super().__init__(**kwargs)
self.drop_prob = drop_prob
@tf.custom_... | 3,949 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
def grad(upstream):
if self.drop_prob > 0:
return tf.where(mask, tf.cast(0.0, dtype=self.compute_dtype), upstream) * scale
else:
return upstream
return inputs, grad
def call(self, inputs: tf.Tensor, training: tf.Tensor = False):
if training:
... | 3,949 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
class TFDebertaLayerNorm(keras.layers.Layer):
"""LayerNorm module in the TF style (epsilon inside the square root)."""
def __init__(self, size, eps=1e-12, **kwargs):
super().__init__(**kwargs)
self.size = size
self.eps = eps
def build(self, input_shape):
self.gamma = self.a... | 3,950 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
class TFDebertaSelfOutput(keras.layers.Layer):
def __init__(self, config: DebertaConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(config.hidden_size, name="dense")
self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
... | 3,951 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "dense", None) is not None:
with tf.name_scope(self.dense.name):
self.dense.build([None, None, self.config.hidden_size])
if getattr(self, "LayerNorm", None)... | 3,951 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
class TFDebertaAttention(keras.layers.Layer):
def __init__(self, config: DebertaConfig, **kwargs):
super().__init__(**kwargs)
self.self = TFDebertaDisentangledSelfAttention(config, name="self")
self.dense_output = TFDebertaSelfOutput(config, name="output")
self.config = config | 3,952 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
def call(
self,
input_tensor: tf.Tensor,
attention_mask: tf.Tensor,
query_states: tf.Tensor = None,
relative_pos: tf.Tensor = None,
rel_embeddings: tf.Tensor = None,
output_attentions: bool = False,
training: bool = False,
) -> Tuple[tf.Tensor]:
... | 3,952 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "self", None) is not None:
with tf.name_scope(self.self.name):
self.self.build(None)
if getattr(self, "dense_output", None) is not None:
with tf... | 3,952 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
class TFDebertaIntermediate(keras.layers.Layer):
def __init__(self, config: DebertaConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
... | 3,953 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "dense", None) is not None:
with tf.name_scope(self.dense.name):
self.dense.build([None, None, self.config.hidden_size]) | 3,953 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
class TFDebertaOutput(keras.layers.Layer):
def __init__(self, config: DebertaConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
self.LayerNo... | 3,954 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "dense", None) is not None:
with tf.name_scope(self.dense.name):
self.dense.build([None, None, self.config.intermediate_size])
if getattr(self, "LayerNorm",... | 3,954 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
class TFDebertaLayer(keras.layers.Layer):
def __init__(self, config: DebertaConfig, **kwargs):
super().__init__(**kwargs)
self.attention = TFDebertaAttention(config, name="attention")
self.intermediate = TFDebertaIntermediate(config, name="intermediate")
self.bert_output = TFDeberta... | 3,955 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
query_states: tf.Tensor = None,
relative_pos: tf.Tensor = None,
rel_embeddings: tf.Tensor = None,
output_attentions: bool = False,
training: bool = False,
) -> Tuple[tf.Tensor]:
... | 3,955 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
outputs = (layer_output,) + attention_outputs[1:] # add attentions if we output them | 3,955 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
return outputs
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "attention", None) is not None:
with tf.name_scope(self.attention.name):
self.attention.build(None)
if getattr(self, "intermediate", Non... | 3,955 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
class TFDebertaEncoder(keras.layers.Layer):
def __init__(self, config: DebertaConfig, **kwargs):
super().__init__(**kwargs)
self.layer = [TFDebertaLayer(config, name=f"layer_._{i}") for i in range(config.num_hidden_layers)]
self.relative_attention = getattr(config, "relative_attention", Fal... | 3,956 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if self.relative_attention:
self.rel_embeddings = self.add_weight(
name="rel_embeddings.weight",
shape=[self.max_relative_positions * 2, self.config.hidden_size],
... | 3,956 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
def get_attention_mask(self, attention_mask):
if len(shape_list(attention_mask)) <= 2:
extended_attention_mask = tf.expand_dims(tf.expand_dims(attention_mask, 1), 2)
attention_mask = extended_attention_mask * tf.expand_dims(tf.squeeze(extended_attention_mask, -2), -1)
attenti... | 3,956 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
query_states: tf.Tensor = None,
relative_pos: tf.Tensor = None,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
training: bool = Fal... | 3,956 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
layer_outputs = layer_module(
hidden_states=next_kv,
attention_mask=attention_mask,
query_states=query_states,
relative_pos=relative_pos,
rel_embeddings=rel_embeddings,
output_attentions=output_attentions,
tr... | 3,956 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)
return TFBaseModelOutput(
last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
) | 3,956 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
class TFDebertaDisentangledSelfAttention(keras.layers.Layer):
"""
Disentangled self-attention module
Parameters:
config (`str`):
A model config class instance with the configuration to build a new model. The schema is similar to
*BertConfig*, for more details, please refer [... | 3,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
def __init__(self, config: DebertaConfig, **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 number of attention "
f"heads ({config.... | 3,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
self.relative_attention = getattr(config, "relative_attention", False)
self.talking_head = getattr(config, "talking_head", False)
if self.talking_head:
self.head_logits_proj = keras.layers.Dense(
self.num_attention_heads,
kernel_initializer=get_initializer(co... | 3,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
if self.relative_attention:
self.max_relative_positions = getattr(config, "max_relative_positions", -1)
if self.max_relative_positions < 1:
self.max_relative_positions = config.max_position_embeddings
self.pos_dropout = TFDebertaStableDropout(config.hidden_dropout_pro... | 3,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
self.q_bias = self.add_weight(
name="q_bias", shape=(self.all_head_size), initializer=keras.initializers.Zeros()
)
self.v_bias = self.add_weight(
name="v_bias", shape=(s... | 3,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
self.head_weights_proj.build(None)
if getattr(self, "pos_dropout", None) is not None:
with tf.name_scope(self.pos_dropout.name):
self.pos_dropout.build(None)
if getattr(self, "pos_proj", None) is not None:
with tf.name_scope(self.pos_proj.name):
se... | 3,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
def transpose_for_scores(self, tensor: tf.Tensor) -> tf.Tensor:
shape = shape_list(tensor)[:-1] + [self.num_attention_heads, -1]
# Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size]
tensor = tf.reshape(tensor=tensor, shape=s... | 3,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
Args:
hidden_states (`tf.Tensor`):
Input states to the module usually the output from previous layer, it will be the Q,K and V in
*Attention(Q,K,V)*
attention_mask (`tf.Tensor`):
An attention mask matrix of shape [*B*, *N*, *N*] where *B* is the b... | 3,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
rel_embeddings (`tf.Tensor`):
The embedding of relative distances. It's a tensor of shape [\\(2 \\times
\\text{max_relative_positions}\\), *hidden_size*].
"""
if query_states is None:
qp = self.in_proj(hidden_states) # .split(self.all_head_size, dim=-1)
... | 3,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
ws = tf.split(
tf.transpose(self.in_proj.weight[0]), num_or_size_splits=self.num_attention_heads * 3, axis=0
)
qkvw = tf.TensorArray(dtype=self.dtype, size=3)
for k in tf.range(3):
qkvw_inside = tf.TensorArray(dtype=self.dtype, size=self.num_attention_... | 3,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
query_layer = query_layer + self.transpose_for_scores(self.q_bias[None, None, :])
value_layer = value_layer + self.transpose_for_scores(self.v_bias[None, None, :])
rel_att = None
# Take the dot product between "query" and "key" to get the raw attention scores.
scale_factor = 1 + len(sel... | 3,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
if self.talking_head:
attention_scores = tf.transpose(
self.head_logits_proj(tf.transpose(attention_scores, [0, 2, 3, 1])), [0, 3, 1, 2]
)
attention_probs = self.softmax(attention_scores, attention_mask)
attention_probs = self.dropout(attention_probs, training=tr... | 3,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
context_layer = tf.matmul(attention_probs, value_layer)
context_layer = tf.transpose(context_layer, [0, 2, 1, 3])
context_layer_shape = shape_list(context_layer)
# Set the final dimension here explicitly.
# Calling tf.reshape(context_layer, (*context_layer_shape[:-2], -1)) raises an erro... | 3,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
def disentangled_att_bias(self, query_layer, key_layer, relative_pos, rel_embeddings, scale_factor):
if relative_pos is None:
q = shape_list(query_layer)[-2]
relative_pos = build_relative_position(q, shape_list(key_layer)[-2])
shape_list_pos = shape_list(relative_pos)
if ... | 3,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
att_span = tf.cast(
tf.minimum(
tf.maximum(shape_list(query_layer)[-2], shape_list(key_layer)[-2]), self.max_relative_positions
),
tf.int64,
)
rel_embeddings = tf.expand_dims(
rel_embeddings[self.max_relative_positions - att_span : self.max... | 3,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
# position->content
if "p2c" in self.pos_att_type:
pos_query_layer = self.pos_q_proj(rel_embeddings)
pos_query_layer = self.transpose_for_scores(pos_query_layer)
pos_query_layer /= tf.math.sqrt(
tf.cast(shape_list(pos_query_layer)[-1] * scale_factor, dtype=sel... | 3,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
pos_index = tf.expand_dims(relative_pos[:, :, :, 0], -1)
p2c_att = torch_gather(p2c_att, pos_dynamic_expand(pos_index, p2c_att, key_layer), -2)
score += p2c_att | 3,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
return score | 3,957 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
class TFDebertaEmbeddings(keras.layers.Layer):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config, **kwargs):
super().__init__(**kwargs) | 3,958 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
self.config = config
self.embedding_size = getattr(config, "embedding_size", config.hidden_size)
self.hidden_size = config.hidden_size
self.max_position_embeddings = config.max_position_embeddings
self.position_biased_input = getattr(config, "position_biased_input", True)
self.in... | 3,958 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
def build(self, input_shape=None):
with tf.name_scope("word_embeddings"):
self.weight = self.add_weight(
name="weight",
shape=[self.config.vocab_size, self.embedding_size],
initializer=get_initializer(self.initializer_range),
)
wit... | 3,958 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
with tf.name_scope("position_embeddings"):
if self.position_biased_input:
self.position_embeddings = self.add_weight(
name="embeddings",
shape=[self.max_position_embeddings, self.hidden_size],
initializer=get_initializer(self.initia... | 3,958 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.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,
mask: tf.Tensor = None,
training: bool = False,
) -> tf.Tensor:
"""
Applies embedding based on input... | 3,958 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
if position_ids is None:
position_ids = tf.expand_dims(tf.range(start=0, limit=input_shape[-1]), axis=0)
final_embeddings = inputs_embeds
if self.position_biased_input:
position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids)
final_embedding... | 3,958 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
if mask is not None:
if len(shape_list(mask)) != len(shape_list(final_embeddings)):
if len(shape_list(mask)) == 4:
mask = tf.squeeze(tf.squeeze(mask, axis=1), axis=1)
mask = tf.cast(tf.expand_dims(mask, axis=2), dtype=self.compute_dtype)
final... | 3,958 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
class TFDebertaPredictionHeadTransform(keras.layers.Layer):
def __init__(self, config: DebertaConfig, **kwargs):
super().__init__(**kwargs)
self.embedding_size = getattr(config, "embedding_size", config.hidden_size)
self.dense = keras.layers.Dense(
units=self.embedding_size,
... | 3,959 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "dense", None) is not None:
with tf.name_scope(self.dense.name):
self.dense.build([None, None, self.config.hidden_size])
if getattr(self, "LayerNorm", None)... | 3,959 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
class TFDebertaLMPredictionHead(keras.layers.Layer):
def __init__(self, config: DebertaConfig, input_embeddings: keras.layers.Layer, **kwargs):
super().__init__(**kwargs)
self.config = config
self.embedding_size = getattr(config, "embedding_size", config.hidden_size)
self.transform... | 3,960 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
def get_output_embeddings(self) -> keras.layers.Layer:
return self.input_embeddings
def set_output_embeddings(self, value: tf.Variable):
self.input_embeddings.weight = value
self.input_embeddings.vocab_size = shape_list(value)[0]
def get_bias(self) -> Dict[str, tf.Variable]:
re... | 3,960 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.