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 PatchTSMixerForRegressionOutput(ModelOutput):
"""
Output type of [`PatchTSMixerForRegressionOutput`].
Args:
regression_outputs (`torch.FloatTensor` of shape `(batch_size, num_targets)`):
Prediction output from the regression head.
last_hidden_state (`torch.FloatTensor` of ... | class_definition | 77,329 | 78,283 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtsmixer/modeling_patchtsmixer.py | null | 8,800 |
class InjectScalerStatistics4D(nn.Module):
def __init__(self, d_model: int, num_patches: int, expansion: int = 2):
super().__init__()
self.inverse_trans_expansion = nn.Linear(d_model + 2, expansion * d_model)
self.inverse_trans_compression = nn.Linear(expansion * d_model, d_model)
s... | class_definition | 78,286 | 80,433 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtsmixer/modeling_patchtsmixer.py | null | 8,801 |
class PatchTSMixerForRegression(PatchTSMixerPreTrainedModel):
r"""
`PatchTSMixer` for regression application.
Args:
config (`PatchTSMixerConfig`):
Configuration.
Returns:
`None`.
"""
def __init__(self, config: PatchTSMixerConfig):
super().__init__(config)
... | class_definition | 80,436 | 87,694 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtsmixer/modeling_patchtsmixer.py | null | 8,802 |
class PatchTSMixerConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`PatchTSMixerModel`]. It is used to instantiate a
PatchTSMixer model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults wi... | class_definition | 835 | 12,530 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtsmixer/configuration_patchtsmixer.py | null | 8,803 |
class SeamlessM4Tv2Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`~SeamlessM4Tv2Model`]. It is used to instantiate
an SeamlessM4Tv2 model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaul... | class_definition | 790 | 24,319 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t_v2/configuration_seamless_m4t_v2.py | null | 8,804 |
class SeamlessM4Tv2GenerationOutput(ModelOutput):
"""
Class defining the generated outputs from [`SeamlessM4Tv2Model`], [`SeamlessM4Tv2ForTextToText`],
[`SeamlessM4Tv2ForTextToSpeech`], [`SeamlessM4Tv2ForSpeechToSpeech`] and [`SeamlessM4Tv2ForTextToSpeech`].
Args:
waveform (`torch.FloatTensor` ... | class_definition | 1,832 | 3,397 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t_v2/modeling_seamless_m4t_v2.py | null | 8,805 |
class SeamlessM4Tv2TextToUnitDecoderOutput(ModelOutput):
"""
Class defining the outputs from [`SeamlessM4Tv2TextToUnitDecoder`].
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... | class_definition | 3,411 | 5,132 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t_v2/modeling_seamless_m4t_v2.py | null | 8,806 |
class SeamlessM4Tv2TextToUnitOutput(ModelOutput):
"""
Class defining the outputs from [`SeamlessM4Tv2TextToUnitForConditionalGeneration`] and
[`SeamlessM4Tv2TextToUnitModel`].
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
... | class_definition | 5,146 | 8,705 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t_v2/modeling_seamless_m4t_v2.py | null | 8,807 |
class SeamlessM4Tv2ConformerFeatureProjection(nn.Module):
# Copied from transformers.models.seamless_m4t.modeling_seamless_m4t.SeamlessM4TConformerFeatureProjection.__init__
def __init__(self, config):
super().__init__()
self.layer_norm = nn.LayerNorm(config.feature_projection_input_dim, eps=con... | class_definition | 24,206 | 25,042 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t_v2/modeling_seamless_m4t_v2.py | null | 8,808 |
class SeamlessM4Tv2ConformerFeedForward(nn.Module):
def __init__(self, config, act_fn=None, dropout=None):
super().__init__()
dropout = dropout if dropout is not None else config.speech_encoder_dropout
act_fn = act_fn if act_fn is not None else config.speech_encoder_hidden_act
self.... | class_definition | 25,178 | 26,261 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t_v2/modeling_seamless_m4t_v2.py | null | 8,809 |
class SeamlessM4Tv2ConformerConvolutionModule(nn.Module):
"""Convolution block used in the conformer block. Uses a causal depthwise convolution similar to that
described in Section 2.1 of `https://doi.org/10.48550/arxiv.1609.03499"""
def __init__(self, config):
super().__init__()
if (config... | class_definition | 26,264 | 29,043 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t_v2/modeling_seamless_m4t_v2.py | null | 8,810 |
class SeamlessM4Tv2ConformerSelfAttention(nn.Module):
"""Construct a SeamlessM4Tv2ConformerSelfAttention object.
Can be enhanced with relative position embeddings.
"""
def __init__(self, config, use_position_embeddings=True):
super().__init__()
self.head_size = config.hidden_size // co... | class_definition | 29,046 | 33,089 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t_v2/modeling_seamless_m4t_v2.py | null | 8,811 |
class SeamlessM4Tv2ConformerEncoderLayer(nn.Module):
"""Conformer block based on https://arxiv.org/abs/2005.08100."""
# Copied from transformers.models.wav2vec2_conformer.modeling_wav2vec2_conformer.Wav2Vec2ConformerEncoderLayer.__init__ with Wav2Vec2->SeamlessM4Tv2, attention_dropout->speech_encoder_dropout, ... | class_definition | 33,092 | 35,702 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t_v2/modeling_seamless_m4t_v2.py | null | 8,812 |
class SeamlessM4Tv2ConformerEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.dropout = nn.Dropout(config.speech_encoder_dropout)
self.layers = nn.ModuleList(
[SeamlessM4Tv2ConformerEncoderLayer(config) for _ in range(config.spe... | class_definition | 35,705 | 41,188 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t_v2/modeling_seamless_m4t_v2.py | null | 8,813 |
class SeamlessM4Tv2ConformerAdapterLayer(nn.Module):
def __init__(self, config):
super().__init__()
embed_dim = config.hidden_size
dropout = config.adaptor_dropout
self.kernel_size = config.adaptor_kernel_size
self.stride = config.adaptor_stride
# 1. residual convol... | class_definition | 41,325 | 45,067 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t_v2/modeling_seamless_m4t_v2.py | null | 8,814 |
class SeamlessM4Tv2ConformerAdapter(nn.Module):
def __init__(self, config):
super().__init__()
self.layers = nn.ModuleList(
SeamlessM4Tv2ConformerAdapterLayer(config) for _ in range(config.num_adapter_layers)
)
def forward(self, hidden_states, attention_mask):
# dow... | class_definition | 45,199 | 45,685 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t_v2/modeling_seamless_m4t_v2.py | null | 8,815 |
class SeamlessM4Tv2ScaledWordEmbedding(nn.Embedding):
"""
This module overrides nn.Embeddings' forward by multiplying with embeddings scale.
"""
def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, embed_scale: Optional[float] = 1.0):
super().__init__(num_embeddings, em... | class_definition | 45,858 | 46,352 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t_v2/modeling_seamless_m4t_v2.py | null | 8,816 |
class SeamlessM4Tv2SinusoidalPositionalEmbedding(nn.Module):
"""This module produces sinusoidal positional embeddings of any length."""
def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int] = None):
super().__init__()
self.offset = 2
self.embedding_dim = ... | class_definition | 46,450 | 50,058 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t_v2/modeling_seamless_m4t_v2.py | null | 8,817 |
class SeamlessM4Tv2Attention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
# Copied from transformers.models.bart.modeling_bart.BartAttention.__init__ with Bart->SeamlessM4Tv2
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: floa... | class_definition | 50,061 | 55,057 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t_v2/modeling_seamless_m4t_v2.py | null | 8,818 |
class SeamlessM4Tv2FeedForwardNetwork(nn.Module):
def __init__(self, config: SeamlessM4Tv2Config, ffn_dim: int):
super().__init__()
self.fc1 = nn.Linear(config.hidden_size, ffn_dim)
self.fc2 = nn.Linear(ffn_dim, config.hidden_size)
self.dropout = nn.Dropout(config.activation_dropout)... | class_definition | 55,226 | 56,166 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t_v2/modeling_seamless_m4t_v2.py | null | 8,819 |
class SeamlessM4Tv2EncoderLayer(nn.Module):
def __init__(self, config: SeamlessM4Tv2Config, encoder_ffn_dim=None, encoder_attention_heads=None):
super().__init__()
encoder_ffn_dim = config.encoder_ffn_dim if encoder_ffn_dim is None else encoder_ffn_dim
encoder_attention_heads = (
... | class_definition | 56,294 | 58,607 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t_v2/modeling_seamless_m4t_v2.py | null | 8,820 |
class SeamlessM4Tv2DecoderLayer(nn.Module):
def __init__(self, config: SeamlessM4Tv2Config, decoder_ffn_dim=None, decoder_attention_heads=None):
super().__init__()
decoder_ffn_dim = config.decoder_ffn_dim if decoder_ffn_dim is None else decoder_ffn_dim
decoder_attention_heads = (
... | class_definition | 58,735 | 63,905 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t_v2/modeling_seamless_m4t_v2.py | null | 8,821 |
class SeamlessM4Tv2TextToUnitDecoderLayer(nn.Module):
def __init__(self, config: SeamlessM4Tv2Config, decoder_ffn_dim=None, decoder_attention_heads=None):
super().__init__()
decoder_ffn_dim = config.decoder_ffn_dim if decoder_ffn_dim is None else decoder_ffn_dim
decoder_attention_heads = (
... | class_definition | 63,908 | 67,516 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t_v2/modeling_seamless_m4t_v2.py | null | 8,822 |
class SeamlessM4Tv2PreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = SeamlessM4Tv2Config
base_model_prefix = "seamless_m4t_v2"
supports_gradient_checkpointing = Tr... | class_definition | 67,575 | 78,789 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t_v2/modeling_seamless_m4t_v2.py | null | 8,823 |
class SeamlessM4Tv2SpeechEncoder(SeamlessM4Tv2PreTrainedModel):
main_input_name = "input_features"
def __init__(self, config: SeamlessM4Tv2Config):
super().__init__(config)
self.feature_projection = SeamlessM4Tv2ConformerFeatureProjection(config)
self.encoder = SeamlessM4Tv2ConformerEn... | class_definition | 79,156 | 81,775 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t_v2/modeling_seamless_m4t_v2.py | null | 8,824 |
class SeamlessM4Tv2Encoder(SeamlessM4Tv2PreTrainedModel):
def __init__(
self,
config: SeamlessM4Tv2Config,
embed_tokens: Optional[nn.Embedding] = None,
is_t2u_encoder: bool = False,
):
super().__init__(config)
self.dropout = config.dropout
self.layerdrop ... | class_definition | 82,400 | 89,970 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t_v2/modeling_seamless_m4t_v2.py | null | 8,825 |
class SeamlessM4Tv2Decoder(SeamlessM4Tv2PreTrainedModel):
def __init__(
self,
config: SeamlessM4Tv2Config,
embed_tokens: Optional[nn.Embedding] = None,
):
super().__init__(config)
self.dropout = config.dropout
self.layerdrop = config.decoder_layerdrop
self... | class_definition | 90,371 | 101,880 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t_v2/modeling_seamless_m4t_v2.py | null | 8,826 |
class SeamlessM4Tv2TextToUnitDecoder(SeamlessM4Tv2PreTrainedModel):
def __init__(
self,
config: SeamlessM4Tv2Config,
embed_tokens: Optional[nn.Embedding] = None,
):
super().__init__(config)
self.dropout = config.dropout
self.layerdrop = config.decoder_layerdrop
... | class_definition | 102,161 | 110,137 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t_v2/modeling_seamless_m4t_v2.py | null | 8,827 |
class SeamlessM4Tv2TextToUnitModel(SeamlessM4Tv2PreTrainedModel):
# Copied from transformers.models.seamless_m4t.modeling_seamless_m4t.SeamlessM4TTextToUnitModel.__init__ with SeamlessM4T->SeamlessM4Tv2, Decoder->TextToUnitDecoder
def __init__(
self,
config: SeamlessM4Tv2Config,
embed_to... | class_definition | 110,482 | 113,927 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t_v2/modeling_seamless_m4t_v2.py | null | 8,828 |
class SeamlessM4Tv2TextToUnitForConditionalGeneration(SeamlessM4Tv2PreTrainedModel, GenerationMixin):
_keys_to_ignore_on_load_missing = [
"vocoder",
"speech_encoder",
"text_encoder",
"text_decoder",
]
_tied_weights_keys = ["decoder.embed_tokens.weight", "lm_head.weight"]
... | class_definition | 114,246 | 119,305 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t_v2/modeling_seamless_m4t_v2.py | null | 8,829 |
class HifiGanResidualBlock(nn.Module):
def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5), leaky_relu_slope=0.1):
super().__init__()
self.leaky_relu_slope = leaky_relu_slope
self.convs1 = nn.ModuleList(
[
nn.Conv1d(
channels,
... | class_definition | 120,329 | 122,458 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t_v2/modeling_seamless_m4t_v2.py | null | 8,830 |
class SeamlessM4Tv2VariancePredictor(nn.Module):
def __init__(self, embed_dim, hidden_dim, kernel_size, var_pred_dropout):
super().__init__()
self.conv1 = nn.Conv1d(
embed_dim,
hidden_dim,
kernel_size=kernel_size,
padding="same",
)
sel... | class_definition | 122,461 | 124,037 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t_v2/modeling_seamless_m4t_v2.py | null | 8,831 |
class SeamlessM4Tv2HifiGan(nn.Module):
def __init__(self, config: SeamlessM4Tv2Config):
super().__init__()
model_in_dim = config.unit_embed_dim + config.lang_embed_dim + config.spkr_embed_dim
self.leaky_relu_slope = config.leaky_relu_slope
self.num_kernels = len(config.resblock_kerne... | class_definition | 124,160 | 127,556 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t_v2/modeling_seamless_m4t_v2.py | null | 8,832 |
class SeamlessM4Tv2CodeHifiGan(PreTrainedModel):
config_class = SeamlessM4Tv2Config
main_input_name = "input_embeds"
_no_split_modules = []
def __init__(self, config):
super().__init__(config)
self.pad_token_id = config.t2u_pad_token_id
embed_dim = config.unit_embed_dim
... | class_definition | 127,737 | 135,596 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t_v2/modeling_seamless_m4t_v2.py | null | 8,833 |
class SeamlessM4Tv2ForTextToText(SeamlessM4Tv2PreTrainedModel, GenerationMixin):
_keys_to_ignore_on_load_missing = ["speech_encoder", "t2u_model", "vocoder"]
main_input_name = "input_ids"
_tied_weights_keys = [
"lm_head.weight",
"text_encoder.embed_tokens.weight",
"text_decoder.embe... | class_definition | 136,019 | 149,149 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t_v2/modeling_seamless_m4t_v2.py | null | 8,834 |
class SeamlessM4Tv2ForSpeechToText(SeamlessM4Tv2PreTrainedModel):
_keys_to_ignore_on_load_missing = ["text_decoder", "t2u_model", "vocoder"]
main_input_name = "input_features"
_tied_weights_keys = [
"lm_head.weight",
"text_decoder.embed_tokens.weight",
]
# Copied from transformers.... | class_definition | 149,300 | 164,074 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t_v2/modeling_seamless_m4t_v2.py | null | 8,835 |
class SeamlessM4Tv2ForTextToSpeech(SeamlessM4Tv2PreTrainedModel):
_keys_to_ignore_on_load_missing = ["speech_encoder"]
main_input_name = "input_ids"
_tied_weights_keys = [
"lm_head.weight",
"text_encoder.embed_tokens.weight",
"text_decoder.embed_tokens.weight",
]
# Copied f... | class_definition | 164,225 | 182,264 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t_v2/modeling_seamless_m4t_v2.py | null | 8,836 |
class SeamlessM4Tv2ForSpeechToSpeech(SeamlessM4Tv2PreTrainedModel):
_keys_to_ignore_on_load_missing = ["text_encoder"]
main_input_name = "input_features"
_tied_weights_keys = [
"lm_head.weight",
"text_decoder.embed_tokens.weight",
]
# Copied from transformers.models.seamless_m4t.mo... | class_definition | 182,417 | 200,984 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t_v2/modeling_seamless_m4t_v2.py | null | 8,837 |
class SeamlessM4Tv2Model(SeamlessM4Tv2PreTrainedModel):
_tied_weights_keys = [
"lm_head.weight",
"text_encoder.embed_tokens.weight",
"text_decoder.embed_tokens.weight",
]
# Copied from transformers.models.seamless_m4t.modeling_seamless_m4t.SeamlessM4TModel.__init__ with SeamlessM4T-... | class_definition | 201,532 | 225,486 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t_v2/modeling_seamless_m4t_v2.py | null | 8,838 |
class Dinov2WithRegistersConfig(BackboneConfigMixin, PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Dinov2WithRegistersModel`]. It is used to instantiate an
Dinov2WithRegisters model according to the specified arguments, defining the model architecture. Instantiati... | class_definition | 1,225 | 8,156 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2_with_registers/modular_dinov2_with_registers.py | null | 8,839 |
class Dinov2WithRegistersPatchEmbeddings(Dinov2PatchEmbeddings):
pass | class_definition | 8,159 | 8,232 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2_with_registers/modular_dinov2_with_registers.py | null | 8,840 |
class Dinov2WithRegistersEmbeddings(nn.Module):
"""
Construct the CLS token, mask token, register tokens, position and patch embeddings.
"""
def __init__(self, config: Dinov2WithRegistersConfig) -> None:
super().__init__()
self.cls_token = nn.Parameter(torch.randn(1, 1, config.hidden_s... | class_definition | 8,235 | 12,785 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2_with_registers/modular_dinov2_with_registers.py | null | 8,841 |
class Dinov2WithRegistersEncoder(Dinov2Encoder):
pass | class_definition | 12,788 | 12,845 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2_with_registers/modular_dinov2_with_registers.py | null | 8,842 |
class Dinov2WithRegistersPreTrainedModel(Dinov2PreTrainedModel):
pass | class_definition | 12,848 | 12,921 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2_with_registers/modular_dinov2_with_registers.py | null | 8,843 |
class Dinov2WithRegistersModel(Dinov2Model):
pass | class_definition | 12,924 | 12,977 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2_with_registers/modular_dinov2_with_registers.py | null | 8,844 |
class Dinov2WithRegistersForImageClassification(Dinov2ForImageClassification):
pass | class_definition | 12,980 | 13,067 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2_with_registers/modular_dinov2_with_registers.py | null | 8,845 |
class Dinov2WithRegistersBackbone(Dinov2Backbone):
def __init__(self, config):
super().__init__(config)
super()._init_backbone(config)
self.num_register_tokens = config.num_register_tokens
self.num_features = [config.hidden_size for _ in range(config.num_hidden_layers + 1)]
... | class_definition | 13,070 | 16,939 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2_with_registers/modular_dinov2_with_registers.py | null | 8,846 |
class Dinov2WithRegistersConfig(BackboneConfigMixin, PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Dinov2WithRegistersModel`]. It is used to instantiate an
Dinov2WithRegisters model according to the specified arguments, defining the model architecture. Instantiati... | class_definition | 1,659 | 8,590 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2_with_registers/configuration_dinov2_with_registers.py | null | 8,847 |
class Dinov2WithRegistersPatchEmbeddings(nn.Module):
"""
This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
`hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
Transformer.
"""
def __init__(se... | class_definition | 2,475 | 4,060 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2_with_registers/modeling_dinov2_with_registers.py | null | 8,848 |
class Dinov2WithRegistersEmbeddings(nn.Module):
"""
Construct the CLS token, mask token, register tokens, position and patch embeddings.
"""
def __init__(self, config: Dinov2WithRegistersConfig) -> None:
super().__init__()
self.cls_token = nn.Parameter(torch.randn(1, 1, config.hidden_s... | class_definition | 4,063 | 8,613 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2_with_registers/modeling_dinov2_with_registers.py | null | 8,849 |
class Dinov2WithRegistersSelfAttention(nn.Module):
def __init__(self, config: Dinov2WithRegistersConfig) -> None:
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size {con... | class_definition | 8,616 | 11,488 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2_with_registers/modeling_dinov2_with_registers.py | null | 8,850 |
class Dinov2WithRegistersSdpaSelfAttention(Dinov2WithRegistersSelfAttention):
def __init__(self, config: Dinov2WithRegistersConfig) -> None:
super().__init__(config)
self.attention_probs_dropout_prob = config.attention_probs_dropout_prob
def forward(
self, hidden_states, head_mask: Opti... | class_definition | 11,491 | 13,590 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2_with_registers/modeling_dinov2_with_registers.py | null | 8,851 |
class Dinov2WithRegistersSelfOutput(nn.Module):
"""
The residual connection is defined in Dinov2WithRegistersLayer instead of here (as is the case with other models), due to the
layernorm applied before each block.
"""
def __init__(self, config: Dinov2WithRegistersConfig) -> None:
super()._... | class_definition | 13,593 | 14,284 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2_with_registers/modeling_dinov2_with_registers.py | null | 8,852 |
class Dinov2WithRegistersAttention(nn.Module):
def __init__(self, config: Dinov2WithRegistersConfig) -> None:
super().__init__()
self.attention = Dinov2WithRegistersSelfAttention(config)
self.output = Dinov2WithRegistersSelfOutput(config)
self.pruned_heads = set()
def prune_head... | class_definition | 14,287 | 16,028 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2_with_registers/modeling_dinov2_with_registers.py | null | 8,853 |
class Dinov2WithRegistersSdpaAttention(Dinov2WithRegistersAttention):
def __init__(self, config: Dinov2WithRegistersConfig) -> None:
super().__init__(config)
self.attention = Dinov2WithRegistersSdpaSelfAttention(config) | class_definition | 16,031 | 16,270 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2_with_registers/modeling_dinov2_with_registers.py | null | 8,854 |
class Dinov2WithRegistersLayerScale(nn.Module):
def __init__(self, config) -> None:
super().__init__()
self.lambda1 = nn.Parameter(config.layerscale_value * torch.ones(config.hidden_size))
def forward(self, hidden_state: torch.Tensor) -> torch.Tensor:
return hidden_state * self.lambda1 | class_definition | 16,273 | 16,592 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2_with_registers/modeling_dinov2_with_registers.py | null | 8,855 |
class Dinov2WithRegistersDropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
def __init__(self, drop_prob: Optional[float] = None) -> None:
super().__init__()
self.drop_prob = drop_prob
def forward(self, hidden_states: torch.T... | class_definition | 17,687 | 18,180 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2_with_registers/modeling_dinov2_with_registers.py | null | 8,856 |
class Dinov2WithRegistersMLP(nn.Module):
def __init__(self, config) -> None:
super().__init__()
in_features = out_features = config.hidden_size
hidden_features = int(config.hidden_size * config.mlp_ratio)
self.fc1 = nn.Linear(in_features, hidden_features, bias=True)
if isinst... | class_definition | 18,183 | 18,962 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2_with_registers/modeling_dinov2_with_registers.py | null | 8,857 |
class Dinov2WithRegistersSwiGLUFFN(nn.Module):
def __init__(self, config) -> None:
super().__init__()
in_features = out_features = config.hidden_size
hidden_features = int(config.hidden_size * config.mlp_ratio)
hidden_features = (int(hidden_features * 2 / 3) + 7) // 8 * 8
se... | class_definition | 18,965 | 19,687 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2_with_registers/modeling_dinov2_with_registers.py | null | 8,858 |
class Dinov2WithRegistersLayer(nn.Module):
"""This corresponds to the Block class in the original implementation."""
def __init__(self, config: Dinov2WithRegistersConfig) -> None:
super().__init__()
self.norm1 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.attention... | class_definition | 19,827 | 21,958 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2_with_registers/modeling_dinov2_with_registers.py | null | 8,859 |
class Dinov2WithRegistersEncoder(nn.Module):
def __init__(self, config: Dinov2WithRegistersConfig) -> None:
super().__init__()
self.config = config
self.layer = nn.ModuleList([Dinov2WithRegistersLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = Fa... | class_definition | 21,961 | 23,930 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2_with_registers/modeling_dinov2_with_registers.py | null | 8,860 |
class Dinov2WithRegistersPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = Dinov2WithRegistersConfig
base_model_prefix = "dinov2_with_registers"
main_input_name = ... | class_definition | 23,933 | 25,688 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2_with_registers/modeling_dinov2_with_registers.py | null | 8,861 |
class Dinov2WithRegistersModel(Dinov2WithRegistersPreTrainedModel):
def __init__(self, config: Dinov2WithRegistersConfig):
super().__init__(config)
self.config = config
self.embeddings = Dinov2WithRegistersEmbeddings(config)
self.encoder = Dinov2WithRegistersEncoder(config)
... | class_definition | 27,951 | 31,410 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2_with_registers/modeling_dinov2_with_registers.py | null | 8,862 |
class Dinov2WithRegistersForImageClassification(Dinov2WithRegistersPreTrainedModel):
def __init__(self, config: Dinov2WithRegistersConfig) -> None:
super().__init__(config)
self.num_labels = config.num_labels
self.dinov2_with_registers = Dinov2WithRegistersModel(config)
# Classifie... | class_definition | 32,993 | 36,875 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2_with_registers/modeling_dinov2_with_registers.py | null | 8,863 |
class Dinov2WithRegistersBackbone(Dinov2WithRegistersPreTrainedModel, BackboneMixin):
def __init__(self, config):
super().__init__(config)
super()._init_backbone(config)
self.num_features = [config.hidden_size for _ in range(config.num_hidden_layers + 1)]
self.embeddings = Dinov2With... | class_definition | 37,050 | 41,163 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2_with_registers/modeling_dinov2_with_registers.py | null | 8,864 |
class XLNetRelativeAttention(nn.Module):
def __init__(self, config):
super().__init__()
if config.d_model % config.n_head != 0:
raise ValueError(
f"The hidden size ({config.d_model}) is not a multiple of the number of attention "
f"heads ({config.n_head}"... | class_definition | 7,895 | 16,946 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlnet/modeling_xlnet.py | null | 8,865 |
class XLNetFeedForward(nn.Module):
def __init__(self, config):
super().__init__()
self.layer_norm = nn.LayerNorm(config.d_model, eps=config.layer_norm_eps)
self.layer_1 = nn.Linear(config.d_model, config.d_inner)
self.layer_2 = nn.Linear(config.d_inner, config.d_model)
self.d... | class_definition | 16,949 | 17,817 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlnet/modeling_xlnet.py | null | 8,866 |
class XLNetLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.rel_attn = XLNetRelativeAttention(config)
self.ff = XLNetFeedForward(config)
self.dropout = nn.Dropout(config.dropout)
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.s... | class_definition | 17,820 | 19,286 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlnet/modeling_xlnet.py | null | 8,867 |
class XLNetPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = XLNetConfig
load_tf_weights = load_tf_weights_in_xlnet
base_model_prefix = "transformer"
def _ini... | class_definition | 19,289 | 20,980 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlnet/modeling_xlnet.py | null | 8,868 |
class XLNetModelOutput(ModelOutput):
"""
Output type of [`XLNetModel`].
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_predict, hidden_size)`):
Sequence of hidden-states at the last layer of the model.
`num_predict` corresponds to `target_mapping.sh... | class_definition | 20,994 | 22,863 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlnet/modeling_xlnet.py | null | 8,869 |
class XLNetLMHeadModelOutput(ModelOutput):
"""
Output type of [`XLNetLMHeadModel`].
Args:
loss (`torch.FloatTensor` of shape *(1,)*, *optional*, returned when `labels` is provided)
Language modeling loss (for next-token prediction).
logits (`torch.FloatTensor` of shape `(batch_s... | class_definition | 22,877 | 24,998 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlnet/modeling_xlnet.py | null | 8,870 |
class XLNetForSequenceClassificationOutput(ModelOutput):
"""
Output type of [`XLNetForSequenceClassification`].
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `label` is provided):
Classification (or regression if config.num_labels==1) loss.
logits (`... | class_definition | 25,012 | 26,973 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlnet/modeling_xlnet.py | null | 8,871 |
class XLNetForTokenClassificationOutput(ModelOutput):
"""
Output type of [`XLNetForTokenClassificationOutput`].
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) :
Classification loss.
logits (`torch.FloatTensor` of shape `(batch_si... | class_definition | 26,987 | 28,887 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlnet/modeling_xlnet.py | null | 8,872 |
class XLNetForMultipleChoiceOutput(ModelOutput):
"""
Output type of [`XLNetForMultipleChoice`].
Args:
loss (`torch.FloatTensor` of shape *(1,)*, *optional*, returned when `labels` is provided):
Classification loss.
logits (`torch.FloatTensor` of shape `(batch_size, num_choices)`... | class_definition | 28,901 | 30,859 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlnet/modeling_xlnet.py | null | 8,873 |
class XLNetForQuestionAnsweringSimpleOutput(ModelOutput):
"""
Output type of [`XLNetForQuestionAnsweringSimple`].
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 ... | class_definition | 30,873 | 33,004 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlnet/modeling_xlnet.py | null | 8,874 |
class XLNetForQuestionAnsweringOutput(ModelOutput):
"""
Output type of [`XLNetForQuestionAnswering`].
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned if both `start_positions` and `end_positions` are provided):
Classification loss as the sum of start token, end tok... | class_definition | 33,018 | 36,508 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlnet/modeling_xlnet.py | null | 8,875 |
class XLNetModel(XLNetPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.mem_len = config.mem_len
self.reuse_len = config.reuse_len
self.d_model = config.d_model
self.same_length = config.same_length
self.attn_type = config.attn_type
... | class_definition | 41,741 | 57,179 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlnet/modeling_xlnet.py | null | 8,876 |
class XLNetLMHeadModel(XLNetPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_loss.weight"]
def __init__(self, config):
super().__init__(config)
self.attn_type = config.attn_type
self.same_length = config.same_length
self.transformer = XLNetModel(config)
self... | class_definition | 57,362 | 66,369 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlnet/modeling_xlnet.py | null | 8,877 |
class XLNetForSequenceClassification(XLNetPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.transformer = XLNetModel(config)
self.sequence_summary = SequenceSummary(config)
self.logits... | class_definition | 66,581 | 70,952 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlnet/modeling_xlnet.py | null | 8,878 |
class XLNetForTokenClassification(XLNetPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.transformer = XLNetModel(config)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights an... | class_definition | 71,183 | 74,291 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlnet/modeling_xlnet.py | null | 8,879 |
class XLNetForMultipleChoice(XLNetPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.transformer = XLNetModel(config)
self.sequence_summary = SequenceSummary(config)
self.logits_proj = nn.Linear(config.d_model, 1)
# Initialize weights and apply fina... | class_definition | 74,518 | 78,554 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlnet/modeling_xlnet.py | null | 8,880 |
class XLNetForQuestionAnsweringSimple(XLNetPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.transformer = XLNetModel(config)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weight... | class_definition | 78,843 | 83,495 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlnet/modeling_xlnet.py | null | 8,881 |
class XLNetForQuestionAnswering(XLNetPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.start_n_top = config.start_n_top
self.end_n_top = config.end_n_top
self.transformer = XLNetModel(config)
self.start_logits = PoolerStartLogits(config)
sel... | class_definition | 83,784 | 92,965 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlnet/modeling_xlnet.py | null | 8,882 |
class TFXLNetRelativeAttention(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
if config.d_model % config.n_head != 0:
raise ValueError(
f"The hidden size ({config.d_model}) is not a multiple of the number of attention "
... | class_definition | 1,787 | 11,329 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlnet/modeling_tf_xlnet.py | null | 8,883 |
class TFXLNetFeedForward(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.layer_norm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm")
self.layer_1 = keras.layers.Dense(
config.d_inner, kernel_initializer... | class_definition | 11,332 | 13,187 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlnet/modeling_tf_xlnet.py | null | 8,884 |
class TFXLNetLayer(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.rel_attn = TFXLNetRelativeAttention(config, name="rel_attn")
self.ff = TFXLNetFeedForward(config, name="ff")
self.dropout = keras.layers.Dropout(config.dropout)
def call... | class_definition | 13,190 | 14,907 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlnet/modeling_tf_xlnet.py | null | 8,885 |
class TFXLNetLMHead(keras.layers.Layer):
def __init__(self, config, input_embeddings, **kwargs):
super().__init__(**kwargs)
self.config = config
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.input_embeddings... | class_definition | 14,910 | 16,046 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlnet/modeling_tf_xlnet.py | null | 8,886 |
class TFXLNetMainLayer(keras.layers.Layer):
config_class = XLNetConfig
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.config = config
self.output_hidden_states = config.output_hidden_states
self.output_attentions = config.output_attentions
self.re... | class_definition | 16,069 | 31,637 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlnet/modeling_tf_xlnet.py | null | 8,887 |
class TFXLNetPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = XLNetConfig
base_model_prefix = "transformer" | class_definition | 31,640 | 31,903 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlnet/modeling_tf_xlnet.py | null | 8,888 |
class TFXLNetModelOutput(ModelOutput):
"""
Output type of [`TFXLNetModel`].
Args:
last_hidden_state (`tf.Tensor` of shape `(batch_size, num_predict, hidden_size)`):
Sequence of hidden-states at the last layer of the model.
`num_predict` corresponds to `target_mapping.shape[... | class_definition | 31,917 | 33,708 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlnet/modeling_tf_xlnet.py | null | 8,889 |
class TFXLNetLMHeadModelOutput(ModelOutput):
"""
Output type of [`TFXLNetLMHeadModel`].
Args:
loss (`tf.Tensor` of shape *(1,)*, *optional*, returned when `labels` is provided)
Language modeling loss (for next-token prediction).
logits (`tf.Tensor` of shape `(batch_size, num_pre... | class_definition | 33,722 | 35,739 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlnet/modeling_tf_xlnet.py | null | 8,890 |
class TFXLNetForSequenceClassificationOutput(ModelOutput):
"""
Output type of [`TFXLNetForSequenceClassification`].
Args:
loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `label` is provided):
Classification (or regression if config.num_labels==1) loss.
logits (`tf.T... | class_definition | 35,753 | 37,610 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlnet/modeling_tf_xlnet.py | null | 8,891 |
class TFXLNetForTokenClassificationOutput(ModelOutput):
"""
Output type of [`TFXLNetForTokenClassificationOutput`].
Args:
loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `labels` is provided) :
Classification loss.
logits (`tf.Tensor` of shape `(batch_size, sequence... | class_definition | 37,624 | 39,420 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlnet/modeling_tf_xlnet.py | null | 8,892 |
class TFXLNetForMultipleChoiceOutput(ModelOutput):
"""
Output type of [`TFXLNetForMultipleChoice`].
Args:
loss (`tf.Tensor` of shape *(1,)*, *optional*, returned when `labels` is provided):
Classification loss.
logits (`tf.Tensor` of shape `(batch_size, num_choices)`):
... | class_definition | 39,434 | 41,288 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlnet/modeling_tf_xlnet.py | null | 8,893 |
class TFXLNetForQuestionAnsweringSimpleOutput(ModelOutput):
"""
Output type of [`TFXLNetForQuestionAnsweringSimple`].
Args:
loss (`tf.Tensor` 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 ... | class_definition | 41,302 | 43,313 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlnet/modeling_tf_xlnet.py | null | 8,894 |
class TFXLNetModel(TFXLNetPreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.transformer = TFXLNetMainLayer(config, name="transformer")
@unpack_inputs
@add_start_docstrings_to_model_forward(XLNET_INPUTS_DOCSTRING.format("batch_s... | class_definition | 50,142 | 52,333 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlnet/modeling_tf_xlnet.py | null | 8,895 |
class TFXLNetLMHeadModel(TFXLNetPreTrainedModel, TFCausalLanguageModelingLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.transformer = TFXLNetMainLayer(config, name="transformer")
self.lm_loss = TFXLNetLMHead(config, self.transformer.wor... | class_definition | 52,516 | 59,558 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlnet/modeling_tf_xlnet.py | null | 8,896 |
class TFXLNetForSequenceClassification(TFXLNetPreTrainedModel, TFSequenceClassificationLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.transformer = TFXLNetMainLayer(config, name="transformer")
... | class_definition | 59,770 | 63,895 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlnet/modeling_tf_xlnet.py | null | 8,897 |
class TFXLNetForMultipleChoice(TFXLNetPreTrainedModel, TFMultipleChoiceLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.transformer = TFXLNetMainLayer(config, name="transformer")
self.sequence_summary = TFSequenceSummary(
con... | class_definition | 64,128 | 68,929 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlnet/modeling_tf_xlnet.py | null | 8,898 |
class TFXLNetForTokenClassification(TFXLNetPreTrainedModel, TFTokenClassificationLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.transformer = TFXLNetMainLayer(config, name="transformer")
self... | class_definition | 69,160 | 72,719 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlnet/modeling_tf_xlnet.py | null | 8,899 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.