text stringlengths 31 243k | type stringclasses 1
value | start int64 36 275k | end int64 286 280k | depth int64 0 1 | filepath stringlengths 85 188 | parent_class stringclasses 3
values | class_index int64 0 10.8k |
|---|---|---|---|---|---|---|---|
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:
... | class_definition | 26,319 | 28,234 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py | null | 3,900 |
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 ... | class_definition | 28,237 | 33,853 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py | null | 3,901 |
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... | class_definition | 33,924 | 34,530 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py | null | 3,902 |
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 ... | class_definition | 34,533 | 36,095 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py | null | 3,903 |
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... | class_definition | 36,168 | 36,733 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py | null | 3,904 |
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... | class_definition | 36,800 | 37,408 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py | null | 3,905 |
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... | class_definition | 37,411 | 39,198 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py | null | 3,906 |
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,
... | class_definition | 39,201 | 42,089 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py | null | 3,907 |
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... | class_definition | 42,156 | 42,715 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py | null | 3,908 |
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... | class_definition | 42,718 | 43,398 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py | null | 3,909 |
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... | class_definition | 43,401 | 43,944 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py | null | 3,910 |
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 = ["... | class_definition | 43,947 | 45,106 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py | null | 3,911 |
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... | class_definition | 50,101 | 59,416 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py | null | 3,912 |
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... | class_definition | 60,114 | 61,173 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py | null | 3,913 |
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... | class_definition | 61,390 | 66,625 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py | null | 3,914 |
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... | class_definition | 66,877 | 71,814 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py | null | 3,915 |
class LukeForEntityPairClassification(LukePreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.luke = LukeModel(config)
self.num_labels = config.num_labels
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden... | class_definition | 72,066 | 77,205 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py | null | 3,916 |
class LukeForEntitySpanClassification(LukePreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.luke = LukeModel(config)
self.num_labels = config.num_labels
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden... | class_definition | 77,434 | 84,231 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py | null | 3,917 |
class LukeForSequenceClassification(LukePreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.luke = LukeModel(config)
self.dropout = nn.Dropout(
config.classifier_dropout if config.classifier_dropout is not None e... | class_definition | 84,457 | 89,138 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py | null | 3,918 |
class LukeForTokenClassification(LukePreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.luke = LukeModel(config, add_pooling_layer=False)
self.dropout = nn.Dropout(
config.classifier_dropout if config.classifie... | class_definition | 89,454 | 93,129 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py | null | 3,919 |
class LukeForQuestionAnswering(LukePreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.luke = LukeModel(config, add_pooling_layer=False)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
# Initial... | class_definition | 93,420 | 98,357 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py | null | 3,920 |
class LukeForMultipleChoice(LukePreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.luke = LukeModel(config)
self.dropout = nn.Dropout(
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
)
se... | class_definition | 98,592 | 103,761 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/modeling_luke.py | null | 3,921 |
class LukeTokenizer(PreTrainedTokenizer):
"""
Constructs a LUKE tokenizer, derived from the GPT-2 tokenizer, using byte-level Byte-Pair-Encoding.
This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
be encoded differently whether it is at th... | class_definition | 7,487 | 85,647 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py | null | 3,922 |
class DebertaLayerNorm(nn.Module):
"""LayerNorm module in the TF style (epsilon inside the square root)."""
def __init__(self, size, eps=1e-12):
super().__init__()
self.weight = nn.Parameter(torch.ones(size))
self.bias = nn.Parameter(torch.zeros(size))
self.variance_epsilon = ep... | class_definition | 1,733 | 2,520 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py | null | 3,923 |
class DebertaSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = DebertaLayerNorm(config.hidden_size, config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
de... | class_definition | 2,523 | 3,088 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py | null | 3,924 |
class DisentangledSelfAttention(nn.Module):
"""
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 [`DebertaConfig`]
... | class_definition | 5,993 | 15,362 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py | null | 3,925 |
class DebertaEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config):
super().__init__()
pad_token_id = getattr(config, "pad_token_id", 0)
self.embedding_size = getattr(config, "embedding_size", config.hidden_size)
... | class_definition | 15,365 | 18,548 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py | null | 3,926 |
class DebertaAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.self = DisentangledSelfAttention(config)
self.output = DebertaSelfOutput(config)
self.config = config
def forward(
self,
hidden_states,
attention_mask,
output_a... | class_definition | 18,551 | 19,573 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py | null | 3,927 |
class DebertaIntermediate(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... | class_definition | 19,665 | 20,233 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py | null | 3,928 |
class DebertaOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = DebertaLayerNorm(config.hidden_size, config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
... | class_definition | 20,236 | 20,832 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py | null | 3,929 |
class DebertaLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.attention = DebertaAttention(config)
self.intermediate = DebertaIntermediate(config)
self.output = DebertaOutput(config)
def forward(
self,
hidden_states,
attention_mask,
... | class_definition | 20,835 | 21,892 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py | null | 3,930 |
class DebertaEncoder(PreTrainedModel):
"""Modified BertEncoder with relative position bias support"""
def __init__(self, config):
super().__init__(config)
self.layer = nn.ModuleList([DebertaLayer(config) for _ in range(config.num_hidden_layers)])
self.relative_attention = getattr(config... | class_definition | 21,895 | 25,723 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py | null | 3,931 |
class DebertaPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = DebertaConfig
base_model_prefix = "deberta"
_keys_to_ignore_on_load_unexpected = ["position_embeddin... | class_definition | 25,726 | 26,779 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py | null | 3,932 |
class DebertaModel(DebertaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.embeddings = DebertaEmbeddings(config)
self.encoder = DebertaEncoder(config)
self.z_steps = 0
self.config = config
# Initialize weights and apply final processing
... | class_definition | 30,322 | 34,830 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py | null | 3,933 |
class LegacyDebertaPredictionHeadTransform(nn.Module):
def __init__(self, config):
super().__init__()
self.embedding_size = getattr(config, "embedding_size", config.hidden_size)
self.dense = nn.Linear(config.hidden_size, self.embedding_size)
if isinstance(config.hidden_act, str):
... | class_definition | 34,833 | 35,599 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py | null | 3,934 |
class LegacyDebertaLMPredictionHead(nn.Module):
def __init__(self, config):
super().__init__()
self.transform = LegacyDebertaPredictionHeadTransform(config)
self.embedding_size = getattr(config, "embedding_size", config.hidden_size)
# The output weights are the same as the input emb... | class_definition | 35,602 | 36,537 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py | null | 3,935 |
class LegacyDebertaOnlyMLMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = LegacyDebertaLMPredictionHead(config)
def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
prediction_scores = self.predictions(sequence_output)
return predi... | class_definition | 36,634 | 36,966 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py | null | 3,936 |
class DebertaLMPredictionHead(nn.Module):
"""https://github.com/microsoft/DeBERTa/blob/master/DeBERTa/deberta/bert.py#L270"""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
if isinstance(config.hidden_act, str):
... | class_definition | 36,969 | 38,089 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py | null | 3,937 |
class DebertaOnlyMLMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.lm_head = DebertaLMPredictionHead(config)
# note that the input embeddings must be passed as an argument
def forward(self, sequence_output, word_embeddings):
prediction_scores = self.lm_head(seq... | class_definition | 38,092 | 38,475 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py | null | 3,938 |
class DebertaForMaskedLM(DebertaPreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder.weight", "cls.predictions.decoder.bias"]
def __init__(self, config):
super().__init__(config)
self.legacy = config.legacy
self.deberta = DebertaModel(config)
if self.legacy:
... | class_definition | 38,586 | 42,432 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py | null | 3,939 |
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"... | class_definition | 42,435 | 43,176 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py | null | 3,940 |
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... | class_definition | 43,404 | 48,329 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py | null | 3,941 |
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.... | class_definition | 48,564 | 51,121 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py | null | 3,942 |
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... | class_definition | 51,414 | 55,701 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py | null | 3,943 |
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... | class_definition | 1,076 | 10,216 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/tokenization_deberta_fast.py | null | 3,944 |
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... | class_definition | 1,010 | 7,442 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/configuration_deberta.py | null | 3,945 |
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... | class_definition | 7,535 | 8,945 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/configuration_deberta.py | null | 3,946 |
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... | class_definition | 1,685 | 2,994 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py | null | 3,947 |
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... | class_definition | 2,997 | 3,823 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py | null | 3,948 |
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_... | class_definition | 3,826 | 5,122 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py | null | 3,949 |
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... | class_definition | 5,125 | 5,962 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py | null | 3,950 |
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")
... | class_definition | 5,965 | 7,310 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py | null | 3,951 |
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
d... | class_definition | 7,313 | 8,978 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py | null | 3,952 |
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"
)
... | class_definition | 8,981 | 10,009 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py | null | 3,953 |
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... | class_definition | 10,012 | 11,493 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py | null | 3,954 |
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... | class_definition | 11,496 | 13,478 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py | null | 3,955 |
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... | class_definition | 13,481 | 17,917 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py | null | 3,956 |
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 [... | class_definition | 20,357 | 33,444 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py | null | 3,957 |
class TFDebertaEmbeddings(keras.layers.Layer):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.config = config
self.embedding_size = getattr(config, "embedding_size", config.hidden_size)... | class_definition | 33,447 | 38,409 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py | null | 3,958 |
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,
... | class_definition | 38,412 | 39,889 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py | null | 3,959 |
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... | class_definition | 39,892 | 41,901 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py | null | 3,960 |
class TFDebertaOnlyMLMHead(keras.layers.Layer):
def __init__(self, config: DebertaConfig, input_embeddings: keras.layers.Layer, **kwargs):
super().__init__(**kwargs)
self.predictions = TFDebertaLMPredictionHead(config, input_embeddings, name="predictions")
def call(self, sequence_output: tf.Ten... | class_definition | 41,904 | 42,619 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py | null | 3,961 |
class TFDebertaMainLayer(keras.layers.Layer):
config_class = DebertaConfig
def __init__(self, config: DebertaConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.embeddings = TFDebertaEmbeddings(config, name="embeddings")
self.encoder = TFDebertaEncoder(conf... | class_definition | 42,644 | 46,031 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py | null | 3,962 |
class TFDebertaPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = DebertaConfig
base_model_prefix = "deberta" | class_definition | 46,034 | 46,297 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py | null | 3,963 |
class TFDebertaModel(TFDebertaPreTrainedModel):
def __init__(self, config: DebertaConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.deberta = TFDebertaMainLayer(config, name="deberta")
@unpack_inputs
@add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING... | class_definition | 51,573 | 53,346 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py | null | 3,964 |
class TFDebertaForMaskedLM(TFDebertaPreTrainedModel, TFMaskedLanguageModelingLoss):
def __init__(self, config: DebertaConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
if config.is_decoder:
logger.warning(
"If you want to use `TFDebertaForMaskedLM` ... | class_definition | 53,457 | 56,866 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py | null | 3,965 |
class TFDebertaForSequenceClassification(TFDebertaPreTrainedModel, TFSequenceClassificationLoss):
def __init__(self, config: DebertaConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.deberta = TFDebertaMainLayer(config, name="de... | class_definition | 57,094 | 61,100 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py | null | 3,966 |
class TFDebertaForTokenClassification(TFDebertaPreTrainedModel, TFTokenClassificationLoss):
def __init__(self, config: DebertaConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.deberta = TFDebertaMainLayer(config, name="deberta"... | class_definition | 61,335 | 64,551 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py | null | 3,967 |
class TFDebertaForQuestionAnswering(TFDebertaPreTrainedModel, TFQuestionAnsweringLoss):
def __init__(self, config: DebertaConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.deberta = TFDebertaMainLayer(config, name="deberta")
... | class_definition | 64,844 | 69,026 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_tf_deberta.py | null | 3,968 |
class DebertaTokenizer(PreTrainedTokenizer):
"""
Construct a DeBERTa tokenizer. 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 encoded differently whether it is at the beginning of the sentenc... | class_definition | 2,375 | 17,050 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/tokenization_deberta.py | null | 3,969 |
class GraniteAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: GraniteConfig, layer_idx: Optional[int] = None):
super().__init__()
self.config = config
self.layer_idx = layer_idx
self.head_dim = getattr(config, "... | class_definition | 5,616 | 9,212 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modeling_granite.py | null | 3,970 |
class GraniteRMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
GraniteRMSNorm is equivalent to T5LayerNorm
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
... | class_definition | 9,215 | 9,939 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modeling_granite.py | null | 3,971 |
class GraniteMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.intermediate_size = config.intermediate_size
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
... | class_definition | 9,942 | 10,642 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modeling_granite.py | null | 3,972 |
class GraniteDecoderLayer(nn.Module):
def __init__(self, config: GraniteConfig, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = GraniteAttention(config=config, layer_idx=layer_idx)
self.mlp = GraniteMLP(config)
self.input_layernorm ... | class_definition | 10,645 | 14,434 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modeling_granite.py | null | 3,973 |
class GraniteRotaryEmbedding(nn.Module):
def __init__(self, config: GraniteConfig, device=None):
super().__init__()
# BC: "rope_type" was originally "type"
if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
self.rope_type = config.rope_scaling.get("rope_type"... | class_definition | 14,437 | 17,636 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modeling_granite.py | null | 3,974 |
class GranitePreTrainedModel(PreTrainedModel):
config_class = GraniteConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["GraniteDecoderLayer"]
_skip_keys_device_placement = ["past_key_values"]
_supports_flash_attn_2 = True
_supports_sdpa = True
... | class_definition | 18,666 | 19,595 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modeling_granite.py | null | 3,975 |
class GraniteModel(GranitePreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`GraniteDecoderLayer`]
Args:
config: GraniteConfig
"""
def __init__(self, config: GraniteConfig):
super().__init__(config)
self.padding_idx ... | class_definition | 24,405 | 35,803 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modeling_granite.py | null | 3,976 |
class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ... | class_definition | 35,806 | 35,868 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modeling_granite.py | null | 3,977 |
class GraniteForCausalLM(GranitePreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
_tp_plan = {"lm_head": "colwise_rep"}
def __init__(self, config):
super().__init__(config)
self.model = GraniteModel(config)
self.vocab_size = config.vocab_size
self.lm... | class_definition | 35,871 | 41,095 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modeling_granite.py | null | 3,978 |
class GraniteConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`GraniteModel`]. It is used to instantiate an Granite
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a sim... | class_definition | 1,147 | 9,086 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/configuration_granite.py | null | 3,979 |
class GraniteAttention(LlamaAttention):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: GraniteConfig, layer_idx: Optional[int] = None):
super().__init__(config, layer_idx)
self.scaling = config.attention_multiplier | class_definition | 1,214 | 1,501 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modular_granite.py | null | 3,980 |
class GraniteDecoderLayer(LlamaDecoderLayer):
def __init__(self, config: GraniteConfig, layer_idx: int):
super().__init__(config, layer_idx)
self.residual_multiplier = config.residual_multiplier
self.self_attn = GraniteAttention(config=config, layer_idx=layer_idx)
def forward(
s... | class_definition | 1,504 | 5,042 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modular_granite.py | null | 3,981 |
class GraniteModel(LlamaModel):
def __init__(self, config: GraniteConfig):
super().__init__(config)
self.embedding_multiplier = config.embedding_multiplier
self.layers = nn.ModuleList(
[GraniteDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
... | class_definition | 5,045 | 9,829 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modular_granite.py | null | 3,982 |
class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ... | class_definition | 9,832 | 9,894 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modular_granite.py | null | 3,983 |
class GraniteForCausalLM(LlamaForCausalLM):
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
... | class_definition | 9,897 | 12,466 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modular_granite.py | null | 3,984 |
class Gemma2RMSNorm(nn.Module):
def __init__(self, dim: int, eps: float = 1e-6):
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.zeros(dim))
def _norm(self, x):
return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
def forward(self, x):
... | class_definition | 2,395 | 3,068 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.py | null | 3,985 |
class Gemma2MLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.intermediate_size = config.intermediate_size
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
sel... | class_definition | 3,071 | 3,747 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.py | null | 3,986 |
class Gemma2Attention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: Gemma2Config, layer_idx: int):
super().__init__()
self.config = config
self.layer_idx = layer_idx
self.head_dim = getattr(config, "head_dim", config.h... | class_definition | 7,423 | 11,267 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.py | null | 3,987 |
class Gemma2DecoderLayer(nn.Module):
def __init__(self, config: Gemma2Config, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.config = config
self.is_sliding = not bool(layer_idx % 2)
self.self_attn = Gemma2Attention(config=config, layer_idx=lay... | class_definition | 11,270 | 14,516 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.py | null | 3,988 |
class Gemma2RotaryEmbedding(nn.Module):
def __init__(self, config: Gemma2Config, device=None):
super().__init__()
# BC: "rope_type" was originally "type"
if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
self.rope_type = config.rope_scaling.get("rope_type", ... | class_definition | 14,519 | 17,716 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.py | null | 3,989 |
class Gemma2PreTrainedModel(PreTrainedModel):
config_class = Gemma2Config
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["Gemma2DecoderLayer"]
_skip_keys_device_placement = ["past_key_values"]
_supports_flash_attn_2 = True
_supports_sdpa = True
_s... | class_definition | 18,742 | 19,668 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.py | null | 3,990 |
class Gemma2Model(Gemma2PreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Gemma2DecoderLayer`]
Args:
config: Gemma2Config
"""
def __init__(self, config: Gemma2Config):
super().__init__(config)
self.padding_idx = con... | class_definition | 24,475 | 34,889 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.py | null | 3,991 |
class Gemma2ForCausalLM(Gemma2PreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
_tp_plan = {"lm_head": "colwise_rep"}
def __init__(self, config):
super().__init__(config)
self.model = Gemma2Model(config)
self.vocab_size = config.vocab_size
self.lm_he... | class_definition | 34,892 | 44,277 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.py | null | 3,992 |
class Gemma2ForSequenceClassification(Gemma2PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = Gemma2Model(config)
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
# Initialize weight... | class_definition | 45,073 | 48,889 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.py | null | 3,993 |
class Gemma2ForTokenClassification(Gemma2PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = Gemma2Model(config)
if getattr(config, "classifier_dropout", None) is not None:
classifier_dropout = config.cla... | class_definition | 49,138 | 52,354 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.py | null | 3,994 |
class Gemma2Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Gemma2Model`]. It is used to instantiate an Gemma2
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a simila... | class_definition | 1,507 | 8,709 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/configuration_gemma2.py | null | 3,995 |
class Gemma2Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Gemma2Model`]. It is used to instantiate an Gemma2
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a simila... | class_definition | 1,492 | 8,694 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modular_gemma2.py | null | 3,996 |
class Gemma2RMSNorm(GemmaRMSNorm):
pass | class_definition | 8,697 | 8,740 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modular_gemma2.py | null | 3,997 |
class Gemma2MLP(GemmaMLP):
def __init__(self, config):
super().__init__()
self.act_fn = ACT2FN[config.hidden_activation] | class_definition | 8,743 | 8,883 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modular_gemma2.py | null | 3,998 |
class Gemma2Attention(GemmaAttention):
def __init__(self, config: Gemma2Config, layer_idx: int):
super().__init__(config, layer_idx)
self.attn_logit_softcapping = self.config.attn_logit_softcapping
self.attention_dropout = self.config.attention_dropout
self.is_causal = True
s... | class_definition | 10,225 | 13,174 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modular_gemma2.py | null | 3,999 |
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