text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
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def __init__(
self,
num_channels=3,
embedding_size=64,
hidden_sizes=[256, 512, 1024, 2048],
depths=[3, 4, 6, 3],
layer_type="preactivation",
hidden_act="relu",
global_padding=None,
num_groups=32,
drop_path_rate=0.0,
embedding_dynami... | 3,887 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/configuration_bit.py |
self.hidden_sizes = hidden_sizes
self.depths = depths
self.layer_type = layer_type
self.hidden_act = hidden_act
self.global_padding = global_padding
self.num_groups = num_groups
self.drop_path_rate = drop_path_rate
self.embedding_dynamic_padding = embedding_dynami... | 3,887 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/configuration_bit.py |
self.stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, len(depths) + 1)]
self._out_features, self._out_indices = get_aligned_output_features_output_indices(
out_features=out_features, out_indices=out_indices, stage_names=self.stage_names
) | 3,887 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bit/configuration_bit.py |
class LukeConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`LukeModel`]. It is used to instantiate a LUKE
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar confi... | 3,888 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/configuration_luke.py |
Args:
vocab_size (`int`, *optional*, defaults to 50267):
Vocabulary size of the LUKE model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`LukeModel`].
entity_vocab_size (`int`, *optional*, defaults to 500000):
... | 3,888 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/configuration_luke.py |
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `Callable`, *optional*, defaults to `... | 3,888 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/configuration_luke.py |
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 vocabulary size of the `token_type_ids` passed when calling [`LukeModel`].
... | 3,888 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/configuration_luke.py |
The dropout ratio for the classification head.
pad_token_id (`int`, *optional*, defaults to 1):
Padding token id.
bos_token_id (`int`, *optional*, defaults to 0):
Beginning of stream token id.
eos_token_id (`int`, *optional*, defaults to 2):
End of stream toke... | 3,888 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/configuration_luke.py |
Examples:
```python
>>> from transformers import LukeConfig, LukeModel
>>> # Initializing a LUKE configuration
>>> configuration = LukeConfig()
>>> # Initializing a model from the configuration
>>> model = LukeModel(configuration)
>>> # Accessing the model configuration
>>> configura... | 3,888 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/configuration_luke.py |
def __init__(
self,
vocab_size=50267,
entity_vocab_size=500000,
hidden_size=768,
entity_emb_size=256,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_... | 3,888 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/configuration_luke.py |
self.vocab_size = vocab_size
self.entity_vocab_size = entity_vocab_size
self.hidden_size = hidden_size
self.entity_emb_size = entity_emb_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_act = hidden_act
sel... | 3,888 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/configuration_luke.py |
class BaseLukeModelOutputWithPooling(BaseModelOutputWithPooling):
"""
Base class for outputs of the LUKE model. | 3,889 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
entity_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, entity_length, hidden_size)`):
Sequenc... | 3,889 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer
plus the initial embedding outputs.
entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`)... | 3,889 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
compute the weighted average in the self-attention heads.
""" | 3,889 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
entity_last_hidden_state: torch.FloatTensor = None
entity_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None | 3,889 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
class BaseLukeModelOutput(BaseModelOutput):
"""
Base class for model's outputs, with potential hidden states and attentions.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of ... | 3,890 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output o... | 3,890 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
entity_last_hidden_state: torch.FloatTensor = None
entity_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None | 3,890 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
class LukeMaskedLMOutput(ModelOutput):
"""
Base class for model's outputs, with potential hidden states and attentions. | 3,891 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
The sum of masked language modeling (MLM) loss and entity prediction loss.
mlm_loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Masked lang... | 3,891 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
shape `(batch_size, sequence_length, h... | 3,891 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output o... | 3,891 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
loss: Optional[torch.FloatTensor] = None
mlm_loss: Optional[torch.FloatTensor] = None
mep_loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
entity_logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
entity_hidden_states: Optional[Tupl... | 3,891 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
class EntityClassificationOutput(ModelOutput):
"""
Outputs of entity classification models. | 3,892 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification loss.
logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
Classification scores (before SoftMax).
hidden_states (`tuple(torch.FloatTensor)... | 3,892 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each
layer plus the initial entity embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
... | 3,892 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
entity_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
attentions: Optional[Tuple[torch.FloatTensor, ...]] = None | 3,892 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
class EntityPairClassificationOutput(ModelOutput):
"""
Outputs of entity pair classification models. | 3,893 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification loss.
logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
Classification scores (before SoftMax).
hidden_states (`tuple(torch.FloatTensor)... | 3,893 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each
layer plus the initial entity embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
... | 3,893 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
entity_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
attentions: Optional[Tuple[torch.FloatTensor, ...]] = None | 3,893 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
class EntitySpanClassificationOutput(ModelOutput):
"""
Outputs of entity span classification models. | 3,894 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification loss.
logits (`torch.FloatTensor` of shape `(batch_size, entity_length, config.num_labels)`):
Classification scores (before SoftMax).
hidden_states (`tuple(tor... | 3,894 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each
layer plus the initial entity embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
... | 3,894 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
entity_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
attentions: Optional[Tuple[torch.FloatTensor, ...]] = None | 3,894 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
class LukeSequenceClassifierOutput(ModelOutput):
"""
Outputs of sentence classification models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
logits (`torch.FloatTens... | 3,895 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the... | 3,895 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
entity_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
attentions: Optional[Tuple[torch.FloatTensor, ...]] = None | 3,895 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
class LukeTokenClassifierOutput(ModelOutput):
"""
Base class for outputs of token classification models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) :
Classification loss.
logits (`torch.FloatTensor` of shape `(batch_size, seq... | 3,896 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the... | 3,896 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
entity_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
attentions: Optional[Tuple[torch.FloatTensor, ...]] = None | 3,896 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
class LukeQuestionAnsweringModelOutput(ModelOutput):
"""
Outputs of question answering models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
... | 3,897 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the... | 3,897 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
loss: Optional[torch.FloatTensor] = None
start_logits: torch.FloatTensor = None
end_logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
entity_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
attentions: Optional[Tuple[torch.FloatTensor, ...]] ... | 3,897 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
class LukeMultipleChoiceModelOutput(ModelOutput):
"""
Outputs of multiple choice models.
Args:
loss (`torch.FloatTensor` of shape *(1,)*, *optional*, returned when `labels` is provided):
Classification loss.
logits (`torch.FloatTensor` of shape `(batch_size, num_choices)`):
... | 3,898 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the... | 3,898 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
entity_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
attentions: Optional[Tuple[torch.FloatTensor, ...]] = None | 3,898 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
class LukeEmbeddings(nn.Module):
"""
Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
"""
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.p... | 3,899 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
def forward(
self,
input_ids=None,
token_type_ids=None,
position_ids=None,
inputs_embeds=None,
):
if position_ids is None:
if input_ids is not None:
# Create the position ids from the input token ids. Any padded tokens remain padded.
... | 3,899 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
position_embeddings = self.position_embeddings(position_ids)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embeddings = inputs_embeds + position_embeddings + token_type_embeddings
embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddings)
... | 3,899 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
class LukeEntityEmbeddings(nn.Module):
def __init__(self, config: LukeConfig):
super().__init__()
self.config = config
self.entity_embeddings = nn.Embedding(config.entity_vocab_size, config.entity_emb_size, padding_idx=0)
if config.entity_emb_size != config.hidden_size:
... | 3,900 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
entity_embeddings = self.entity_embeddings(entity_ids)
if self.config.entity_emb_size != self.config.hidden_size:
entity_embeddings = self.entity_embedding_dense(entity_embeddings)
position_embeddings = self.position_embeddings(position_ids.clamp(min=0))
position_embedding_mask = (p... | 3,900 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
class LukeSelfAttention(nn.Module):
def __init__(self, config):
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,} is not a multiple of the number ... | 3,901 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
if self.use_entity_aware_attention:
self.w2e_query = nn.Linear(config.hidden_size, self.all_head_size)
self.e2w_query = nn.Linear(config.hidden_size, self.all_head_size)
self.e2e_query = nn.Linear(config.hidden_size, self.all_head_size)
self.dropout = nn.Dropout(config.atten... | 3,901 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
key_layer = self.transpose_for_scores(self.key(concat_hidden_states))
value_layer = self.transpose_for_scores(self.value(concat_hidden_states))
if self.use_entity_aware_attention and entity_hidden_states is not None:
# compute query vectors using word-word (w2w), word-entity (w2e), entity-w... | 3,901 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
# compute w2w, w2e, e2w, and e2e key vectors used with the query vectors computed above
w2w_key_layer = key_layer[:, :, :word_size, :]
e2w_key_layer = key_layer[:, :, :word_size, :]
w2e_key_layer = key_layer[:, :, word_size:, :]
e2e_key_layer = key_layer[:, :, word_size:,... | 3,901 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
# combine attention scores to create the final attention score matrix
word_attention_scores = torch.cat([w2w_attention_scores, w2e_attention_scores], dim=3)
entity_attention_scores = torch.cat([e2w_attention_scores, e2e_attention_scores], dim=3)
attention_scores = torch.cat([word_att... | 3,901 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(attention_probs)
# Mask heads if we want to
if head_mask is not None:
attention_probs = attention_pr... | 3,901 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
if output_attentions:
outputs = (output_word_hidden_states, output_entity_hidden_states, attention_probs)
else:
outputs = (output_word_hidden_states, output_entity_hidden_states)
return outputs | 3,901 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
class LukeSelfOutput(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)
def f... | 3,902 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
class LukeAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.self = LukeSelfAttention(config)
self.output = LukeSelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads):
raise NotImplementedError("LUKE does not support the pruning ... | 3,903 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
def forward(
self,
word_hidden_states,
entity_hidden_states,
attention_mask=None,
head_mask=None,
output_attentions=False,
):
word_size = word_hidden_states.size(1)
self_outputs = self.self(
word_hidden_states,
entity_hidden_sta... | 3,903 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
word_attention_output = attention_output[:, :word_size, :]
if entity_hidden_states is None:
entity_attention_output = None
else:
entity_attention_output = attention_output[:, word_size:, :]
# add attentions if we output them
outputs = (word_attention_output, enti... | 3,903 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
class LukeIntermediate(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.interm... | 3,904 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
class LukeOutput(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)
def... | 3,905 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
class LukeLayer(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 = LukeAttention(config)
self.intermediate = LukeIntermediate(config)
self.output = LukeOutput(c... | 3,906 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
outputs = self_attention_outputs[2:] # add self attentions if we output attention weights
layer_output = apply_chunking_to_forward(
self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, concat_attention_output
)
word_layer_output = layer_output[:, :word_size, :]
... | 3,906 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
class LukeEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([LukeLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
word_hidden_states,
... | 3,907 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
layer_head_mask = head_mask[i] if head_mask is not None else None
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
layer_module.__call__,
word_hidden_states,
entity_hidden_states,
... | 3,907 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
if output_hidden_states:
all_word_hidden_states = all_word_hidden_states + (word_hidden_states,)
all_entity_hidden_states = all_entity_hidden_states + (entity_hidden_states,)
if not return_dict:
return tuple(
v
for v in [
w... | 3,907 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
class LukePooler(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
# We "pool" the model by simply taking the hidde... | 3,908 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
class EntityPredictionHeadTransform(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.entity_emb_size)
if isinstance(config.hidden_act, str):
self.transform_act_fn = ACT2FN[config.hidden_act]
else:
sel... | 3,909 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
class EntityPredictionHead(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.transform = EntityPredictionHeadTransform(config)
self.decoder = nn.Linear(config.entity_emb_size, config.entity_vocab_size, bias=False)
self.bias = nn.Parameter(to... | 3,910 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
class LukePreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = LukeConfig
base_model_prefix = "luke"
supports_gradient_checkpointing = True
_no_split_modules = ["... | 3,911 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
def _init_weights(self, module: nn.Module):
"""Initialize the weights"""
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, ... | 3,911 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
class LukeModel(LukePreTrainedModel):
def __init__(self, config: LukeConfig, add_pooling_layer: bool = True):
super().__init__(config)
self.config = config
self.embeddings = LukeEmbeddings(config)
self.entity_embeddings = LukeEntityEmbeddings(config)
self.encoder = LukeEncod... | 3,912 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
@add_start_docstrings_to_model_forward(LUKE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=BaseLukeModelOutputWithPooling, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Option... | 3,912 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
) -> Union[Tuple, BaseLukeModelOutputWithPooling]:
r""" | 3,912 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
Returns:
Examples:
```python
>>> from transformers import AutoTokenizer, LukeModel
>>> tokenizer = AutoTokenizer.from_pretrained("studio-ousia/luke-base")
>>> model = LukeModel.from_pretrained("studio-ousia/luke-base")
# Compute the contextualized entity representation... | 3,912 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
>>> text = "Beyoncé lives in Los Angeles."
>>> entities = [
... "Beyoncé",
... "Los Angeles",
... ] # Wikipedia entity titles corresponding to the entity mentions "Beyoncé" and "Los Angeles"
>>> entity_spans = [
... (0, 7),
... (17, 28),
.... | 3,912 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
>>> encoding = tokenizer(
... text, entities=entities, entity_spans=entity_spans, add_prefix_space=True, return_tensors="pt"
... )
>>> outputs = model(**encoding)
>>> word_last_hidden_state = outputs.last_hidden_state
>>> entity_last_hidden_state = outputs.entity_last_hidden_... | 3,912 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.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()
... | 3,912 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
if attention_mask is None:
attention_mask = torch.ones((batch_size, seq_length), device=device)
if token_type_ids is None:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
if entity_ids is not None:
entity_seq_length = entity_ids.size(1)
... | 3,912 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.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... | 3,912 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
# Third, compute entity embeddings and concatenate with word embeddings
if entity_ids is None:
entity_embedding_output = None
else:
entity_embedding_output = self.entity_embeddings(entity_ids, entity_position_ids, entity_token_type_ids)
# Fourth, send embeddings through ... | 3,912 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
# Sixth, we compute the pooled_output, word_sequence_output and entity_sequence_output based on the sequence_output
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
if not return_dict:
return (sequence_output, pooled_output) + encoder_outputs[1:]
re... | 3,912 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
Arguments:
word_attention_mask (`torch.LongTensor`):
Attention mask for word tokens with ones indicating tokens to attend to, zeros for tokens to ignore.
entity_attention_mask (`torch.LongTensor`, *optional*):
Attention mask for entity tokens with ones indicating ... | 3,912 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
extended_attention_mask = extended_attention_mask.to(dtype=self.dtype) # fp16 compatibility
extended_attention_mask = (1.0 - extended_attention_mask) * torch.finfo(self.dtype).min
return extended_attention_mask | 3,912 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
class LukeLMHead(nn.Module):
"""Roberta Head for masked language modeling."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.decoder... | 3,913 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
def _tie_weights(self):
# To tie those two weights if they get disconnected (on TPU or when the bias is resized)
# For accelerate compatibility and to not break backward compatibility
if self.decoder.bias.device.type == "meta":
self.decoder.bias = self.bias
else:
... | 3,913 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
class LukeForMaskedLM(LukePreTrainedModel):
_tied_weights_keys = ["lm_head.decoder.weight", "lm_head.decoder.bias", "entity_predictions.decoder.weight"]
def __init__(self, config):
super().__init__(config)
self.luke = LukeModel(config)
self.lm_head = LukeLMHead(config)
self.en... | 3,914 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
@add_start_docstrings_to_model_forward(LUKE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=LukeMaskedLMOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Flo... | 3,914 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, LukeMaskedLMOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should be... | 3,914 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
Returns:
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.luke(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
ent... | 3,914 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
mlm_loss = None
logits = self.lm_head(outputs.last_hidden_state)
if labels is not None:
# move labels to correct device to enable model parallelism
labels = labels.to(logits.device)
mlm_loss = self.loss_fn(logits.view(-1, self.config.vocab_size), labels.view(-1))
... | 3,914 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
if not return_dict:
return tuple(
v
for v in [
loss,
mlm_loss,
mep_loss,
logits,
entity_logits,
outputs.hidden_states,
outputs.entity_hi... | 3,914 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
class LukeForEntityClassification(LukePreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.luke = LukeModel(config)
self.num_labels = config.num_labels
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_siz... | 3,915 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
@add_start_docstrings_to_model_forward(LUKE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=EntityClassificationOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[t... | 3,915 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
return_dict: Optional[bool] = None,
) -> Union[Tuple, EntityClassificationOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)` or `(batch_size, num_labels)`, *optional*):
Labels for computing the classification loss. If the shape is `(batch_size,)`, the cross entropy loss is... | 3,915 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py |
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