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
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|---|---|---|---|---|---|---|---|
class TFMobileBertIntermediate(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(config.intermediate_size, name="dense")
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = get_tf_activation(confi... | class_definition | 4,386 | 5,286 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_tf_mobilebert.py | null | 6,000 |
class TFLayerNorm(keras.layers.LayerNormalization):
def __init__(self, feat_size, *args, **kwargs):
self.feat_size = feat_size
super().__init__(*args, **kwargs)
def build(self, input_shape=None):
super().build([None, None, self.feat_size]) | class_definition | 5,289 | 5,561 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_tf_mobilebert.py | null | 6,001 |
class TFNoNorm(keras.layers.Layer):
def __init__(self, feat_size, epsilon=None, **kwargs):
super().__init__(**kwargs)
self.feat_size = feat_size
def build(self, input_shape):
self.bias = self.add_weight("bias", shape=[self.feat_size], initializer="zeros")
self.weight = self.add_... | class_definition | 5,564 | 6,067 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_tf_mobilebert.py | null | 6,002 |
class TFMobileBertEmbeddings(keras.layers.Layer):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.trigram_input = config.trigram_input
self.embedding_size = config.embedding_size
... | class_definition | 6,131 | 10,900 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_tf_mobilebert.py | null | 6,003 |
class TFMobileBertSelfAttention(keras.layers.Layer):
def __init__(self, config, **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 att... | class_definition | 10,903 | 15,643 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_tf_mobilebert.py | null | 6,004 |
class TFMobileBertSelfOutput(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.use_bottleneck = config.use_bottleneck
self.dense = keras.layers.Dense(
config.true_hidden_size, kernel_initializer=get_initializer(config.initializer_range), n... | class_definition | 15,646 | 17,055 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_tf_mobilebert.py | null | 6,005 |
class TFMobileBertAttention(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.self = TFMobileBertSelfAttention(config, name="self")
self.mobilebert_output = TFMobileBertSelfOutput(config, name="output")
def prune_heads(self, heads):
raise... | class_definition | 17,058 | 18,412 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_tf_mobilebert.py | null | 6,006 |
class TFOutputBottleneck(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(config.hidden_size, name="dense")
self.LayerNorm = NORM2FN[config.normalization_type](
config.hidden_size, epsilon=config.layer_norm_eps,... | class_definition | 18,415 | 19,594 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_tf_mobilebert.py | null | 6,007 |
class TFMobileBertOutput(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.use_bottleneck = config.use_bottleneck
self.dense = keras.layers.Dense(
config.true_hidden_size, kernel_initializer=get_initializer(config.initializer_range), name=... | class_definition | 19,597 | 21,446 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_tf_mobilebert.py | null | 6,008 |
class TFBottleneckLayer(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(config.intra_bottleneck_size, name="dense")
self.LayerNorm = NORM2FN[config.normalization_type](
config.intra_bottleneck_size, epsilon=con... | class_definition | 21,449 | 22,434 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_tf_mobilebert.py | null | 6,009 |
class TFBottleneck(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.key_query_shared_bottleneck = config.key_query_shared_bottleneck
self.use_bottleneck_attention = config.use_bottleneck_attention
self.bottleneck_input = TFBottleneckLayer(con... | class_definition | 22,437 | 25,156 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_tf_mobilebert.py | null | 6,010 |
class TFFFNOutput(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(config.true_hidden_size, name="dense")
self.LayerNorm = NORM2FN[config.normalization_type](
config.true_hidden_size, epsilon=config.layer_norm_e... | class_definition | 25,159 | 26,183 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_tf_mobilebert.py | null | 6,011 |
class TFFFNLayer(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.intermediate = TFMobileBertIntermediate(config, name="intermediate")
self.mobilebert_output = TFFFNOutput(config, name="output")
def call(self, hidden_states):
intermediat... | class_definition | 26,186 | 27,108 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_tf_mobilebert.py | null | 6,012 |
class TFMobileBertLayer(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.use_bottleneck = config.use_bottleneck
self.num_feedforward_networks = config.num_feedforward_networks
self.attention = TFMobileBertAttention(config, name="attention")
... | class_definition | 27,111 | 30,269 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_tf_mobilebert.py | null | 6,013 |
class TFMobileBertEncoder(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.output_attentions = config.output_attentions
self.output_hidden_states = config.output_hidden_states
self.layer = [TFMobileBertLayer(config, name=f"layer_._{i}") for i... | class_definition | 30,272 | 32,120 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_tf_mobilebert.py | null | 6,014 |
class TFMobileBertPooler(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.do_activate = config.classifier_activation
if self.do_activate:
self.dense = keras.layers.Dense(
config.hidden_size,
kernel_initiali... | class_definition | 32,123 | 33,249 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_tf_mobilebert.py | null | 6,015 |
class TFMobileBertPredictionHeadTransform(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
if isinstance(... | class_definition | 33,252 | 34,547 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_tf_mobilebert.py | null | 6,016 |
class TFMobileBertLMPredictionHead(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.transform = TFMobileBertPredictionHeadTransform(config, name="transform")
self.config = config
def build(self, input_shape=None):
self.bias = self.add_we... | class_definition | 34,550 | 36,290 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_tf_mobilebert.py | null | 6,017 |
class TFMobileBertMLMHead(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.predictions = TFMobileBertLMPredictionHead(config, name="predictions")
def call(self, sequence_output):
prediction_scores = self.predictions(sequence_output)
retu... | class_definition | 36,293 | 36,900 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_tf_mobilebert.py | null | 6,018 |
class TFMobileBertMainLayer(keras.layers.Layer):
config_class = MobileBertConfig
def __init__(self, config, add_pooling_layer=True, **kwargs):
super().__init__(**kwargs)
self.config = config
self.num_hidden_layers = config.num_hidden_layers
self.output_attentions = config.outpu... | class_definition | 36,923 | 42,198 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_tf_mobilebert.py | null | 6,019 |
class TFMobileBertPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = MobileBertConfig
base_model_prefix = "mobilebert" | class_definition | 42,201 | 42,473 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_tf_mobilebert.py | null | 6,020 |
class TFMobileBertForPreTrainingOutput(ModelOutput):
"""
Output type of [`TFMobileBertForPreTraining`].
Args:
prediction_logits (`tf.Tensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token be... | class_definition | 42,487 | 44,117 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_tf_mobilebert.py | null | 6,021 |
class TFMobileBertModel(TFMobileBertPreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.mobilebert = TFMobileBertMainLayer(config, name="mobilebert")
@unpack_inputs
@add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRI... | class_definition | 50,016 | 51,904 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_tf_mobilebert.py | null | 6,022 |
class TFMobileBertForPreTraining(TFMobileBertPreTrainedModel, TFMobileBertPreTrainingLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.mobilebert = TFMobileBertMainLayer(config, name="mobilebert")
self.predictions = TFMobileBertMLMHead(con... | class_definition | 52,150 | 56,739 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_tf_mobilebert.py | null | 6,023 |
class TFMobileBertForMaskedLM(TFMobileBertPreTrainedModel, TFMaskedLanguageModelingLoss):
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
_keys_to_ignore_on_load_unexpected = [
r"pooler",
r"seq_relationship___cls",
r"cls.se... | class_definition | 56,856 | 60,854 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_tf_mobilebert.py | null | 6,024 |
class TFMobileBertOnlyNSPHead(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.seq_relationship = keras.layers.Dense(2, name="seq_relationship")
self.config = config
def call(self, pooled_output):
seq_relationship_score = self.seq_relati... | class_definition | 60,857 | 61,551 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_tf_mobilebert.py | null | 6,025 |
class TFMobileBertForNextSentencePrediction(TFMobileBertPreTrainedModel, TFNextSentencePredictionLoss):
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
_keys_to_ignore_on_load_unexpected = [r"predictions___cls", r"cls.predictions"]
def __init... | class_definition | 61,703 | 65,519 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_tf_mobilebert.py | null | 6,026 |
class TFMobileBertForSequenceClassification(TFMobileBertPreTrainedModel, TFSequenceClassificationLoss):
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
_keys_to_ignore_on_load_unexpected = [
r"predictions___cls",
r"seq_relationship... | class_definition | 65,753 | 69,799 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_tf_mobilebert.py | null | 6,027 |
class TFMobileBertForQuestionAnswering(TFMobileBertPreTrainedModel, TFQuestionAnsweringLoss):
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
_keys_to_ignore_on_load_unexpected = [
r"pooler",
r"predictions___cls",
r"seq_rel... | class_definition | 70,098 | 74,774 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_tf_mobilebert.py | null | 6,028 |
class TFMobileBertForMultipleChoice(TFMobileBertPreTrainedModel, TFMultipleChoiceLoss):
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
_keys_to_ignore_on_load_unexpected = [
r"predictions___cls",
r"seq_relationship___cls",
... | class_definition | 75,017 | 79,569 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_tf_mobilebert.py | null | 6,029 |
class TFMobileBertForTokenClassification(TFMobileBertPreTrainedModel, TFTokenClassificationLoss):
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
_keys_to_ignore_on_load_unexpected = [
r"pooler",
r"predictions___cls",
r"seq... | class_definition | 79,810 | 83,716 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_tf_mobilebert.py | null | 6,030 |
class NoNorm(nn.Module):
def __init__(self, feat_size, eps=None):
super().__init__()
self.bias = nn.Parameter(torch.zeros(feat_size))
self.weight = nn.Parameter(torch.ones(feat_size))
def forward(self, input_tensor: torch.Tensor) -> torch.Tensor:
return input_tensor * self.weigh... | class_definition | 6,025 | 6,358 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_mobilebert.py | null | 6,031 |
class MobileBertEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config):
super().__init__()
self.trigram_input = config.trigram_input
self.embedding_size = config.embedding_size
self.hidden_size = config.hid... | class_definition | 6,421 | 9,956 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_mobilebert.py | null | 6,032 |
class MobileBertSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.true_hidden_size / config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.att... | class_definition | 9,959 | 12,903 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_mobilebert.py | null | 6,033 |
class MobileBertSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.use_bottleneck = config.use_bottleneck
self.dense = nn.Linear(config.true_hidden_size, config.true_hidden_size)
self.LayerNorm = NORM2FN[config.normalization_type](config.true_hidden_size, eps=... | class_definition | 12,906 | 13,693 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_mobilebert.py | null | 6,034 |
class MobileBertAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.self = MobileBertSelfAttention(config)
self.output = MobileBertSelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads):
if len(heads) == 0:
return
... | class_definition | 13,696 | 15,639 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_mobilebert.py | null | 6,035 |
class MobileBertIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.true_hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
... | class_definition | 15,642 | 16,218 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_mobilebert.py | null | 6,036 |
class OutputBottleneck(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.true_hidden_size, config.hidden_size)
self.LayerNorm = NORM2FN[config.normalization_type](config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hi... | class_definition | 16,221 | 16,862 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_mobilebert.py | null | 6,037 |
class MobileBertOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.use_bottleneck = config.use_bottleneck
self.dense = nn.Linear(config.intermediate_size, config.true_hidden_size)
self.LayerNorm = NORM2FN[config.normalization_type](config.true_hidden_size)
... | class_definition | 16,865 | 17,918 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_mobilebert.py | null | 6,038 |
class BottleneckLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intra_bottleneck_size)
self.LayerNorm = NORM2FN[config.normalization_type](config.intra_bottleneck_size, eps=config.layer_norm_eps)
def forward(self, hidden... | class_definition | 17,921 | 18,405 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_mobilebert.py | null | 6,039 |
class Bottleneck(nn.Module):
def __init__(self, config):
super().__init__()
self.key_query_shared_bottleneck = config.key_query_shared_bottleneck
self.use_bottleneck_attention = config.use_bottleneck_attention
self.input = BottleneckLayer(config)
if self.key_query_shared_bott... | class_definition | 18,408 | 20,645 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_mobilebert.py | null | 6,040 |
class FFNOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.true_hidden_size)
self.LayerNorm = NORM2FN[config.normalization_type](config.true_hidden_size, eps=config.layer_norm_eps)
def forward(self, hidden_states: t... | class_definition | 20,648 | 21,179 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_mobilebert.py | null | 6,041 |
class FFNLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.intermediate = MobileBertIntermediate(config)
self.output = FFNOutput(config)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
intermediate_output = self.intermediate(hidden_states)
... | class_definition | 21,182 | 21,599 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_mobilebert.py | null | 6,042 |
class MobileBertLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.use_bottleneck = config.use_bottleneck
self.num_feedforward_networks = config.num_feedforward_networks
self.attention = MobileBertAttention(config)
self.intermediate = MobileBertIntermediat... | class_definition | 21,602 | 23,890 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_mobilebert.py | null | 6,043 |
class MobileBertEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.layer = nn.ModuleList([MobileBertLayer(config) for _ in range(config.num_hidden_layers)])
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] =... | class_definition | 23,893 | 25,494 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_mobilebert.py | null | 6,044 |
class MobileBertPooler(nn.Module):
def __init__(self, config):
super().__init__()
self.do_activate = config.classifier_activation
if self.do_activate:
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
def forward(self, hidden_states: torch.Tensor) -> torch.Tenso... | class_definition | 25,497 | 26,207 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_mobilebert.py | null | 6,045 |
class MobileBertPredictionHeadTransform(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
if isinstance(config.hidden_act, str):
self.transform_act_fn = ACT2FN[config.hidden_act]
else:
sel... | class_definition | 26,210 | 26,925 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_mobilebert.py | null | 6,046 |
class MobileBertLMPredictionHead(nn.Module):
def __init__(self, config):
super().__init__()
self.transform = MobileBertPredictionHeadTransform(config)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.dense = nn... | class_definition | 26,928 | 28,016 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_mobilebert.py | null | 6,047 |
class MobileBertOnlyMLMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = MobileBertLMPredictionHead(config)
def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
prediction_scores = self.predictions(sequence_output)
return prediction_... | class_definition | 28,019 | 28,345 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_mobilebert.py | null | 6,048 |
class MobileBertPreTrainingHeads(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = MobileBertLMPredictionHead(config)
self.seq_relationship = nn.Linear(config.hidden_size, 2)
def forward(self, sequence_output: torch.Tensor, pooled_output: torch.Tensor) -> Tup... | class_definition | 28,348 | 28,874 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_mobilebert.py | null | 6,049 |
class MobileBertPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = MobileBertConfig
load_tf_weights = load_tf_weights_in_mobilebert
base_model_prefix = "mobilebert"... | class_definition | 28,877 | 30,018 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_mobilebert.py | null | 6,050 |
class MobileBertForPreTrainingOutput(ModelOutput):
"""
Output type of [`MobileBertForPreTraining`].
Args:
loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
Total loss as the sum of the masked language modeling loss and the next sequence predicti... | class_definition | 30,032 | 31,994 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_mobilebert.py | null | 6,051 |
class MobileBertModel(MobileBertPreTrainedModel):
"""
https://arxiv.org/pdf/2004.02984.pdf
"""
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
self.config = config
self.embeddings = MobileBertEmbeddings(config)
self.encoder = MobileBertEncode... | class_definition | 35,689 | 40,426 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_mobilebert.py | null | 6,052 |
class MobileBertForPreTraining(MobileBertPreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder.weight", "cls.predictions.decoder.bias"]
def __init__(self, config):
super().__init__(config)
self.mobilebert = MobileBertModel(config)
self.cls = MobileBertPreTrainingHeads(config)... | class_definition | 40,672 | 45,677 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_mobilebert.py | null | 6,053 |
class MobileBertForMaskedLM(MobileBertPreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder.weight", "cls.predictions.decoder.bias"]
def __init__(self, config):
super().__init__(config)
self.mobilebert = MobileBertModel(config, add_pooling_layer=False)
self.cls = MobileBertOn... | class_definition | 45,794 | 49,459 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_mobilebert.py | null | 6,054 |
class MobileBertOnlyNSPHead(nn.Module):
def __init__(self, config):
super().__init__()
self.seq_relationship = nn.Linear(config.hidden_size, 2)
def forward(self, pooled_output: torch.Tensor) -> torch.Tensor:
seq_relationship_score = self.seq_relationship(pooled_output)
return se... | class_definition | 49,462 | 49,802 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_mobilebert.py | null | 6,055 |
class MobileBertForNextSentencePrediction(MobileBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.mobilebert = MobileBertModel(config)
self.cls = MobileBertOnlyNSPHead(config)
# Initialize weights and apply final processing
self.post_init()
... | class_definition | 49,954 | 53,859 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_mobilebert.py | null | 6,056 |
class MobileBertForSequenceClassification(MobileBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.mobilebert = MobileBertModel(config)
classifier_dropout = (
config.classifier_... | class_definition | 54,209 | 58,357 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_mobilebert.py | null | 6,057 |
class MobileBertForQuestionAnswering(MobileBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.mobilebert = MobileBertModel(config, add_pooling_layer=False)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_lab... | class_definition | 58,767 | 63,194 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_mobilebert.py | null | 6,058 |
class MobileBertForMultipleChoice(MobileBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.mobilebert = MobileBertModel(config)
classifier_dropout = (
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
... | class_definition | 63,545 | 67,227 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_mobilebert.py | null | 6,059 |
class MobileBertForTokenClassification(MobileBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.mobilebert = MobileBertModel(config, add_pooling_layer=False)
classifier_dropout = (
config.classifier_dropout... | class_definition | 67,581 | 70,580 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/modeling_mobilebert.py | null | 6,060 |
class MobileBertTokenizerFast(PreTrainedTokenizerFast):
r"""
Construct a "fast" MobileBERT tokenizer (backed by HuggingFace's *tokenizers* library). Based on WordPiece.
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
refer to this superclas... | class_definition | 1,152 | 7,797 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/tokenization_mobilebert_fast.py | null | 6,061 |
class MobileBertConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`MobileBertModel`] or a [`TFMobileBertModel`]. It
is used to instantiate a MobileBERT model according to the specified arguments, defining the model architecture.
Instantiating a configuration... | class_definition | 877 | 7,617 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/configuration_mobilebert.py | null | 6,062 |
class MobileBertOnnxConfig(OnnxConfig):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
else:
dynamic_axis = {0: "batch", 1: "sequence"}
return OrderedDict(
... | class_definition | 7,715 | 8,216 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilebert/configuration_mobilebert.py | null | 6,063 |
class InformerConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of an [`InformerModel`]. It is used to instantiate an
Informer model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a... | class_definition | 827 | 12,411 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/informer/configuration_informer.py | null | 6,064 |
class InformerFeatureEmbedder(nn.Module):
"""
Embed a sequence of categorical features.
Args:
cardinalities (`list[int]`):
List of cardinalities of the categorical features.
embedding_dims (`list[int]`):
List of embedding dimensions of the categorical features.
"... | class_definition | 1,619 | 2,800 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/informer/modeling_informer.py | null | 6,065 |
class InformerStdScaler(nn.Module):
"""
Standardize features by calculating the mean and scaling along the first dimension, and then normalizes it by
subtracting from the mean and dividing by the standard deviation.
"""
def __init__(self, config: InformerConfig):
super().__init__()
... | class_definition | 2,972 | 4,710 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/informer/modeling_informer.py | null | 6,066 |
class InformerMeanScaler(nn.Module):
"""
Computes a scaling factor as the weighted average absolute value along the first dimension, and scales the data
accordingly.
"""
def __init__(self, config: InformerConfig):
super().__init__()
self.dim = config.scaling_dim if hasattr(config, "... | class_definition | 4,883 | 7,282 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/informer/modeling_informer.py | null | 6,067 |
class InformerNOPScaler(nn.Module):
"""
Assigns a scaling factor equal to 1 along the first dimension, and therefore applies no scaling to the input data.
"""
def __init__(self, config: InformerConfig):
super().__init__()
self.dim = config.scaling_dim if hasattr(config, "scaling_dim") e... | class_definition | 7,454 | 8,653 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/informer/modeling_informer.py | null | 6,068 |
class InformerSinusoidalPositionalEmbedding(nn.Embedding):
"""This module produces sinusoidal positional embeddings of any length."""
def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int] = None) -> None:
super().__init__(num_positions, embedding_dim)
self.weight... | class_definition | 10,329 | 11,898 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/informer/modeling_informer.py | null | 6,069 |
class InformerValueEmbedding(nn.Module):
def __init__(self, feature_size, d_model):
super().__init__()
self.value_projection = nn.Linear(in_features=feature_size, out_features=d_model, bias=False)
def forward(self, x):
return self.value_projection(x) | class_definition | 12,039 | 12,322 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/informer/modeling_informer.py | null | 6,070 |
class InformerAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = True,
is_causal: bool = False,
... | class_definition | 12,412 | 19,810 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/informer/modeling_informer.py | null | 6,071 |
class InformerProbSparseAttention(nn.Module):
"""Probabilistic Attention mechanism to select the "active"
queries rather than the "lazy" queries and provides a sparse Transformer thus mitigating the quadratic compute and
memory requirements of vanilla attention"""
def __init__(
self,
em... | class_definition | 19,813 | 30,295 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/informer/modeling_informer.py | null | 6,072 |
class InformerConvLayer(nn.Module):
def __init__(self, c_in):
super().__init__()
self.downConv = nn.Conv1d(
in_channels=c_in,
out_channels=c_in,
kernel_size=3,
padding=1,
padding_mode="circular",
)
self.norm = nn.BatchNorm1d... | class_definition | 30,378 | 31,015 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/informer/modeling_informer.py | null | 6,073 |
class InformerEncoderLayer(nn.Module):
def __init__(self, config: InformerConfig):
super().__init__()
self.embed_dim = config.d_model
if config.attention_type == "prob":
self.self_attn = InformerProbSparseAttention(
embed_dim=self.embed_dim,
num_he... | class_definition | 31,018 | 34,518 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/informer/modeling_informer.py | null | 6,074 |
class InformerDecoderLayer(nn.Module):
def __init__(self, config: InformerConfig):
super().__init__()
self.embed_dim = config.d_model
if config.attention_type == "prob":
self.self_attn = InformerProbSparseAttention(
embed_dim=self.embed_dim,
num_h... | class_definition | 34,521 | 40,711 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/informer/modeling_informer.py | null | 6,075 |
class InformerPreTrainedModel(PreTrainedModel):
config_class = InformerConfig
base_model_prefix = "model"
main_input_name = "past_values"
supports_gradient_checkpointing = True
def _init_weights(self, module):
std = self.config.init_std
if isinstance(module, (nn.Linear, nn.Conv1d)):... | class_definition | 40,714 | 41,454 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/informer/modeling_informer.py | null | 6,076 |
class InformerEncoder(InformerPreTrainedModel):
"""
Informer encoder consisting of *config.encoder_layers* self attention layers with distillation layers. Each
attention layer is an [`InformerEncoderLayer`].
Args:
config: InformerConfig
"""
def __init__(self, config: InformerConfig):
... | class_definition | 53,478 | 60,665 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/informer/modeling_informer.py | null | 6,077 |
class InformerDecoder(InformerPreTrainedModel):
"""
Informer decoder consisting of *config.decoder_layers* layers. Each layer is a
[`InformerDecoderLayer`]
Args:
config: InformerConfig
"""
def __init__(self, config: InformerConfig):
super().__init__(config)
self.dropout... | class_definition | 60,946 | 72,184 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/informer/modeling_informer.py | null | 6,078 |
class InformerModel(InformerPreTrainedModel):
def __init__(self, config: InformerConfig):
super().__init__(config)
if config.scaling == "mean" or config.scaling is True:
self.scaler = InformerMeanScaler(config)
elif config.scaling == "std":
self.scaler = InformerStdS... | class_definition | 72,580 | 83,653 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/informer/modeling_informer.py | null | 6,079 |
class InformerForPrediction(InformerPreTrainedModel):
def __init__(self, config: InformerConfig):
super().__init__(config)
self.model = InformerModel(config)
if config.distribution_output == "student_t":
self.distribution_output = StudentTOutput(dim=config.input_size)
eli... | class_definition | 84,029 | 101,495 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/informer/modeling_informer.py | null | 6,080 |
class Kosmos2ImagesKwargs(ImagesKwargs, total=False):
bboxes: Optional[List[float]]
num_image_tokens: Optional[int]
first_image_token_id: Optional[int] | class_definition | 1,252 | 1,415 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/kosmos2/processing_kosmos2.py | null | 6,081 |
class Kosmos2TextKwargs(TextKwargs, total=False):
add_eos_token: Optional[bool] | class_definition | 1,418 | 1,501 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/kosmos2/processing_kosmos2.py | null | 6,082 |
class Kosmos2ProcessorKwargs(ProcessingKwargs, total=False):
text_kwargs: Kosmos2TextKwargs
images_kwargs: Kosmos2ImagesKwargs
_defaults = {
"text_kwargs": {
"add_special_tokens": True,
"padding": False,
"stride": 0,
"return_overflowing_tokens": False,... | class_definition | 1,504 | 2,118 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/kosmos2/processing_kosmos2.py | null | 6,083 |
class Kosmos2Processor(ProcessorMixin):
r"""
Constructs an KOSMOS-2 processor which wraps a KOSMOS-2 image processor and a KOSMOS-2 tokenizer into a single
processor.
[`Kosmos2Processor`] offers all the functionalities of [`CLIPImageProcessor`] and some functionalities of
[`XLMRobertaTokenizerFast`... | class_definition | 2,121 | 22,880 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/kosmos2/processing_kosmos2.py | null | 6,084 |
class Kosmos2ModelOutput(ModelOutput):
"""
Base class for text model's outputs that also contains a pooling of the last hidden states.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last... | class_definition | 14,465 | 17,893 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/kosmos2/modeling_kosmos2.py | null | 6,085 |
class Kosmos2ForConditionalGenerationModelOutput(ModelOutput):
"""
Model output class for `Kosmos2ForConditionalGeneration`.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss (for next-token prediction).
logi... | class_definition | 17,907 | 21,545 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/kosmos2/modeling_kosmos2.py | null | 6,086 |
class Kosmos2VisionEmbeddings(nn.Module):
def __init__(self, config: Kosmos2VisionConfig):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.image_size = config.image_size
self.patch_size = config.patch_size
self.class_embedding = nn.Pa... | class_definition | 21,641 | 25,473 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/kosmos2/modeling_kosmos2.py | null | 6,087 |
class Kosmos2VisionAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim ... | class_definition | 25,568 | 30,307 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/kosmos2/modeling_kosmos2.py | null | 6,088 |
class Kosmos2VisionMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.activation_fn = ACT2FN[config.hidden_act]
self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
self.fc2 = nn.Linear(config.intermediate_size, config.hidde... | class_definition | 30,396 | 30,975 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/kosmos2/modeling_kosmos2.py | null | 6,089 |
class Kosmos2VisionEncoderLayer(nn.Module):
def __init__(self, config: Kosmos2VisionConfig):
super().__init__()
self.embed_dim = config.hidden_size
self.self_attn = Kosmos2VisionAttention(config)
self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
self.... | class_definition | 31,085 | 33,066 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/kosmos2/modeling_kosmos2.py | null | 6,090 |
class Kosmos2VisionEncoder(nn.Module):
"""
Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
[`Kosmos2VisionEncoderLayer`].
Args:
config: Kosmos2VisionConfig
"""
def __init__(self, config: Kosmos2VisionConfig):
super().__init__(... | class_definition | 33,171 | 37,604 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/kosmos2/modeling_kosmos2.py | null | 6,091 |
class Kosmos2VisionTransformer(nn.Module):
# Copied from transformers.models.altclip.modeling_altclip.AltCLIPVisionTransformer.__init__ with AltCLIPVision->Kosmos2Vision,ALTCLIP_VISION->KOSMOS2_VISION,AltCLIP->Kosmos2Vision
def __init__(self, config: Kosmos2VisionConfig):
super().__init__()
self... | class_definition | 37,719 | 40,069 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/kosmos2/modeling_kosmos2.py | null | 6,092 |
class Kosmos2TextSinusoidalPositionalEmbedding(nn.Module):
"""This module produces sinusoidal positional embeddings of any length."""
# Copied from transformers.models.m2m_100.modeling_m2m_100.M2M100SinusoidalPositionalEmbedding.__init__
def __init__(self, num_positions: int, embedding_dim: int, padding_id... | class_definition | 40,204 | 44,443 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/kosmos2/modeling_kosmos2.py | null | 6,093 |
class KosmosTextAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
# Similar to transformers.models.bart.modeling_bart.BartAttention.__init__ except an additional `inner_attn_ln`.
def __init__(
self,
config,
embed_dim: int,
num_heads: i... | class_definition | 44,446 | 50,041 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/kosmos2/modeling_kosmos2.py | null | 6,094 |
class Kosmos2TextFFN(nn.Module):
def __init__(self, config: Kosmos2TextConfig):
super().__init__()
self.dropout = config.dropout
self.activation_fn = ACT2FN[config.activation_function]
self.activation_dropout = config.activation_dropout
self.fc1 = nn.Linear(config.embed_dim... | class_definition | 50,044 | 50,986 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/kosmos2/modeling_kosmos2.py | null | 6,095 |
class Kosmos2TextBlock(nn.Module):
def __init__(self, config: Kosmos2TextConfig):
super().__init__()
self.embed_dim = config.embed_dim
self.self_attn = KosmosTextAttention(
config,
embed_dim=self.embed_dim,
num_heads=config.attention_heads,
dr... | class_definition | 50,989 | 55,583 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/kosmos2/modeling_kosmos2.py | null | 6,096 |
class Kosmos2TextTransformer(nn.Module):
"""
Transformer decoder consisting of `config.layers` layers. Each layer is a [`Kosmos2TextBlock`].
Args:
config: Kosmos2TextConfig
"""
def __init__(self, config: Kosmos2TextConfig):
super().__init__()
self.config = config
se... | class_definition | 55,586 | 66,156 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/kosmos2/modeling_kosmos2.py | null | 6,097 |
class Kosmos2PreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = Kosmos2Config
supports_gradient_checkpointing = True
_no_split_modules = ["Kosmos2VisionEncoderLayer... | class_definition | 66,159 | 71,119 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/kosmos2/modeling_kosmos2.py | null | 6,098 |
class Kosmos2VisionModel(Kosmos2PreTrainedModel):
config_class = Kosmos2VisionConfig
main_input_name = "pixel_values"
# Copied from transformers.models.clip.modeling_clip.CLIPVisionModel.__init__ with CLIP_VISION->KOSMOS2_VISION,CLIP->Kosmos2,self.vision_model->self.model
def __init__(self, config: Kos... | class_definition | 71,122 | 72,742 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/kosmos2/modeling_kosmos2.py | null | 6,099 |
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