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 GraphormerGraphAttnBias(nn.Module):
"""
Compute attention bias for each head.
"""
def __init__(self, config: GraphormerConfig):
super().__init__()
self.num_heads = config.num_attention_heads
self.multi_hop_max_dist = config.multi_hop_max_dist
# We do not change ed... | class_definition | 7,923 | 11,610 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/modeling_graphormer.py | null | 10,100 |
class GraphormerMultiheadAttention(nn.Module):
"""Multi-headed attention.
See "Attention Is All You Need" for more details.
"""
def __init__(self, config: GraphormerConfig):
super().__init__()
self.embedding_dim = config.embedding_dim
self.kdim = config.kdim if config.kdim is n... | class_definition | 11,613 | 20,112 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/modeling_graphormer.py | null | 10,101 |
class GraphormerGraphEncoderLayer(nn.Module):
def __init__(self, config: GraphormerConfig) -> None:
super().__init__()
# Initialize parameters
self.embedding_dim = config.embedding_dim
self.num_attention_heads = config.num_attention_heads
self.q_noise = config.q_noise
... | class_definition | 20,115 | 23,402 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/modeling_graphormer.py | null | 10,102 |
class GraphormerGraphEncoder(nn.Module):
def __init__(self, config: GraphormerConfig):
super().__init__()
self.dropout_module = torch.nn.Dropout(p=config.dropout, inplace=False)
self.layerdrop = config.layerdrop
self.embedding_dim = config.embedding_dim
self.apply_graphormer... | class_definition | 23,405 | 27,597 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/modeling_graphormer.py | null | 10,103 |
class GraphormerDecoderHead(nn.Module):
def __init__(self, embedding_dim: int, num_classes: int):
super().__init__()
"""num_classes should be 1 for regression, or the number of classes for classification"""
self.lm_output_learned_bias = nn.Parameter(torch.zeros(1))
self.classifier = ... | class_definition | 27,600 | 28,227 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/modeling_graphormer.py | null | 10,104 |
class GraphormerPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = GraphormerConfig
base_model_prefix = "graphormer"
main_input_name_nodes = "input_nodes"
main_... | class_definition | 28,230 | 31,084 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/modeling_graphormer.py | null | 10,105 |
class GraphormerModel(GraphormerPreTrainedModel):
"""The Graphormer model is a graph-encoder model.
It goes from a graph to its representation. If you want to use the model for a downstream classification task, use
GraphormerForGraphClassification instead. For any other downstream task, feel free to add a ... | class_definition | 31,087 | 34,048 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/modeling_graphormer.py | null | 10,106 |
class GraphormerForGraphClassification(GraphormerPreTrainedModel):
"""
This model can be used for graph-level classification or regression tasks.
It can be trained on
- regression (by setting config.num_classes to 1); there should be one float-type label per graph
- one task classification (by sett... | class_definition | 34,051 | 37,005 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/modeling_graphormer.py | null | 10,107 |
class Parser(utils.Parser):
dataset: str = "halfcheetah-medium-expert-v2"
config: str = "config.offline" | class_definition | 832 | 944 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/trajectory_transformer/convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch.py | null | 10,108 |
class TrajectoryTransformerOutput(ModelOutput):
"""
Base class for model's outputs that also contains a pooling of the last hidden states.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss.
logits (`torch.Flo... | class_definition | 4,377 | 6,503 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/trajectory_transformer/modeling_trajectory_transformer.py | null | 10,109 |
class TrajectoryTransformerPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = TrajectoryTransformerConfig
load_tf_weights = load_tf_weights_in_trajectory_transformer
... | class_definition | 6,506 | 7,861 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/trajectory_transformer/modeling_trajectory_transformer.py | null | 10,110 |
class EinLinear(nn.Module):
def __init__(self, n_models, in_features, out_features, bias):
super().__init__()
self.n_models = n_models
self.out_features = out_features
self.in_features = in_features
self.weight = nn.Parameter(torch.Tensor(n_models, out_features, in_features))... | class_definition | 10,445 | 11,680 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/trajectory_transformer/modeling_trajectory_transformer.py | null | 10,111 |
class CausalSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
if config.n_embd % config.n_head != 0:
raise ValueError(f"n_head ({config.n_head}) should be a divisor of n_embd ({config.n_embd})")
# key, query, value projections for all heads
self.k... | class_definition | 11,683 | 15,293 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/trajectory_transformer/modeling_trajectory_transformer.py | null | 10,112 |
class Block(nn.Module):
def __init__(self, config):
super().__init__()
self.ln1 = nn.LayerNorm(config.n_embd)
self.ln2 = nn.LayerNorm(config.n_embd)
self.attn = CausalSelfAttention(config)
# MLP
self.l1 = nn.Linear(config.n_embd, 4 * config.n_embd)
self.act =... | class_definition | 15,296 | 16,789 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/trajectory_transformer/modeling_trajectory_transformer.py | null | 10,113 |
class TrajectoryTransformerModel(TrajectoryTransformerPreTrainedModel):
"""the full GPT language model, with a context size of block_size"""
def __init__(self, config):
super().__init__(config)
# input embedding stem (+1 for stop token)
self.tok_emb = nn.Embedding(config.vocab_size * c... | class_definition | 16,980 | 25,592 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/trajectory_transformer/modeling_trajectory_transformer.py | null | 10,114 |
class TrajectoryTransformerConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`TrajectoryTransformerModel`]. It is used to
instantiate an TrajectoryTransformer model according to the specified arguments, defining the model architecture.
Instantiating a config... | class_definition | 846 | 7,060 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/trajectory_transformer/configuration_trajectory_transformer.py | null | 10,115 |
class XLMProphetNetSeq2SeqLMOutput(ModelOutput):
"""
Base class for sequence-to-sequence language models outputs.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss.
logits (`torch.FloatTensor` of shape `(batc... | class_definition | 12,033 | 17,988 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py | null | 10,116 |
class XLMProphetNetSeq2SeqModelOutput(ModelOutput):
"""
Base class for model encoder's outputs that also contains : pre-computed hidden states that can speed up sequential
decoding.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, decoder_sequence_length, hidden_size)`):
... | class_definition | 18,002 | 23,982 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py | null | 10,117 |
class XLMProphetNetDecoderModelOutput(ModelOutput):
"""
Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding).
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, decoder_sequence_length, hidden_size)`):
Sequence of main... | class_definition | 23,996 | 28,215 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py | null | 10,118 |
class XLMProphetNetDecoderLMOutput(ModelOutput):
"""
Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding).
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss.
... | class_definition | 28,229 | 32,494 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py | null | 10,119 |
class XLMProphetNetPreTrainedModel(PreTrainedModel):
config_class = XLMProphetNetConfig
base_model_prefix = "prophetnet"
supports_gradient_checkpointing = True
def _init_weights(self, module):
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=self.config.ini... | class_definition | 32,497 | 34,163 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py | null | 10,120 |
class XLMProphetNetPositionalEmbeddings(nn.Embedding):
"""
This module learns positional embeddings up to a fixed maximum size. Padding ids are ignored by either offsetting
based on padding_idx or by setting padding_idx to None and ensuring that the appropriate position ids are passed to
the forward fun... | class_definition | 34,166 | 36,240 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py | null | 10,121 |
class XLMProphetNetAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
config: XLMProphetNetConfig,
num_attn_heads: int,
):
super().__init__()
hidden_size = config.hidden_size
self.attention_dropout =... | class_definition | 36,243 | 42,364 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py | null | 10,122 |
class XLMProphetNetFeedForward(nn.Module):
"""
This is the residual two feed-forward layer block based on the original Transformer implementation.
"""
def __init__(self, config: XLMProphetNetConfig, ffn_dim: int):
super().__init__()
self.activation_fn = ACT2FN[config.activation_function... | class_definition | 42,367 | 43,362 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py | null | 10,123 |
class XLMProphetNetNgramSelfAttention(nn.Module):
def __init__(self, config: XLMProphetNetConfig):
super().__init__()
self.hidden_size = config.hidden_size
self.num_buckets = config.num_buckets
self.relative_max_distance = config.relative_max_distance
self.num_attn_heads = c... | class_definition | 43,365 | 59,725 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py | null | 10,124 |
class XLMProphetNetEncoderLayer(nn.Module):
"""
Encoder block for XLMProphetnet
"""
def __init__(self, config: XLMProphetNetConfig):
super().__init__()
# 1st residual block
self.self_attn = XLMProphetNetAttention(config, config.num_encoder_attention_heads)
self.self_attn... | class_definition | 59,728 | 61,101 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py | null | 10,125 |
class XLMProphetNetDecoderLayer(nn.Module):
"""
Decoder block for XLMProphetnet
"""
def __init__(self, config: XLMProphetNetConfig):
super().__init__()
# 1st residual block
self.self_attn = XLMProphetNetNgramSelfAttention(config)
self.self_attn_layer_norm = LayerNorm(con... | class_definition | 61,104 | 64,650 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py | null | 10,126 |
class XLMProphetNetEncoder(XLMProphetNetPreTrainedModel):
r"""
word_embeddings (`torch.nn.Embeddings` of shape `(config.vocab_size, config.hidden_size)`, *optional*):
The word embedding parameters. This can be used to initialize [`XLMProphetNetEncoder`] with pre-defined word
embeddings instead ... | class_definition | 64,776 | 70,622 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py | null | 10,127 |
class XLMProphetNetDecoder(XLMProphetNetPreTrainedModel):
r"""
word_embeddings (`torch.nn.Embeddings` of shape `(config.vocab_size, config.hidden_size)`, *optional*):
The word embedding parameters. This can be used to initialize [`XLMProphetNetEncoder`] with pre-defined word
embeddings instead ... | class_definition | 70,748 | 88,388 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py | null | 10,128 |
class XLMProphetNetModel(XLMProphetNetPreTrainedModel):
_tied_weights_keys = ["encoder.word_embeddings.weight", "decoder.word_embeddings.weight"]
def __init__(self, config: XLMProphetNetConfig):
super().__init__(config)
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, ... | class_definition | 88,551 | 94,525 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py | null | 10,129 |
class XLMProphetNetForConditionalGeneration(XLMProphetNetPreTrainedModel):
_tied_weights_keys = ["encoder.word_embeddings.weight", "decoder.word_embeddings.weight", "lm_head.weight"]
def __init__(self, config: XLMProphetNetConfig):
super().__init__(config)
self.prophetnet = XLMProphetNetModel(c... | class_definition | 94,694 | 103,738 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py | null | 10,130 |
class XLMProphetNetForCausalLM(XLMProphetNetPreTrainedModel):
_tied_weights_keys = [
"prophetnet.word_embeddings.weight",
"prophetnet.decoder.word_embeddings.weight",
"lm_head.weight",
]
def __init__(self, config: XLMProphetNetConfig):
# set config for CLM
config = c... | class_definition | 103,945 | 114,791 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py | null | 10,131 |
class XLMProphetNetDecoderWrapper(XLMProphetNetPreTrainedModel):
"""
This is a wrapper class, so that [`XLMProphetNetForCausalLM`] can correctly be loaded from pretrained XLMProphetNet
classes.
"""
def __init__(self, config: XLMProphetNetConfig):
super().__init__(config)
self.word_... | class_definition | 114,794 | 115,592 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/modeling_xlm_prophetnet.py | null | 10,132 |
class XLMProphetNetTokenizer(PreTrainedTokenizer):
"""
Adapted from [`RobertaTokenizer`] and [`XLNetTokenizer`]. Based on
[SentencePiece](https://github.com/google/sentencepiece).
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
thi... | class_definition | 1,296 | 13,271 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/tokenization_xlm_prophetnet.py | null | 10,133 |
class XLMProphetNetConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`XLMProphetNetModel`]. It is used to instantiate a
XLMProphetNet model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults... | class_definition | 844 | 8,915 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/xlm_prophetnet/configuration_xlm_prophetnet.py | null | 10,134 |
class QDQBertConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`QDQBertModel`]. It is used to instantiate an
QDQBERT model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a sim... | class_definition | 804 | 5,688 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/configuration_qdqbert.py | null | 10,135 |
class QDQBertEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.position_embe... | class_definition | 5,342 | 8,518 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py | null | 10,136 |
class QDQBertSelfAttention(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 num... | class_definition | 8,521 | 15,853 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py | null | 10,137 |
class QDQBertSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
# Quantize Linear layer
self.dense = quant_nn.QuantLinear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn... | class_definition | 15,856 | 16,910 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py | null | 10,138 |
class QDQBertAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.self = QDQBertSelfAttention(config)
self.output = QDQBertSelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads):
if len(heads) == 0:
return
heads... | class_definition | 16,998 | 18,765 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py | null | 10,139 |
class QDQBertIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
# Quantize Linear layer
self.dense = quant_nn.QuantLinear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hid... | class_definition | 18,768 | 19,349 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py | null | 10,140 |
class QDQBertOutput(nn.Module):
def __init__(self, config):
super().__init__()
# Quantize Linear layer
self.dense = quant_nn.QuantLinear(config.intermediate_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = n... | class_definition | 19,352 | 20,407 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py | null | 10,141 |
class QDQBertLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.seq_len_dim = 1
self.attention = QDQBertAttention(config)
self.is_decoder = config.is_decoder
self.add_cross_attention = config.add_cross_attention
if self.add_cross_attention:
... | class_definition | 20,491 | 23,986 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py | null | 10,142 |
class QDQBertEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([QDQBertLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
hidden_states,
... | class_definition | 24,072 | 27,501 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py | null | 10,143 |
class QDQBertPooler(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 hi... | class_definition | 27,504 | 28,066 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py | null | 10,144 |
class QDQBertPredictionHeadTransform(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:
self.t... | class_definition | 28,069 | 28,772 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py | null | 10,145 |
class QDQBertLMPredictionHead(nn.Module):
def __init__(self, config):
super().__init__()
self.transform = QDQBertPredictionHeadTransform(config)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.decoder = nn.Li... | class_definition | 28,867 | 29,705 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py | null | 10,146 |
class QDQBertOnlyMLMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = QDQBertLMPredictionHead(config)
def forward(self, sequence_output):
prediction_scores = self.predictions(sequence_output)
return prediction_scores | class_definition | 29,795 | 30,085 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py | null | 10,147 |
class QDQBertOnlyNSPHead(nn.Module):
def __init__(self, config):
super().__init__()
self.seq_relationship = nn.Linear(config.hidden_size, 2)
def forward(self, pooled_output):
seq_relationship_score = self.seq_relationship(pooled_output)
return seq_relationship_score | class_definition | 30,088 | 30,395 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py | null | 10,148 |
class QDQBertPreTrainingHeads(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = QDQBertLMPredictionHead(config)
self.seq_relationship = nn.Linear(config.hidden_size, 2)
def forward(self, sequence_output, pooled_output):
prediction_scores = self.predic... | class_definition | 30,490 | 30,959 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py | null | 10,149 |
class QDQBertPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = QDQBertConfig
load_tf_weights = load_tf_weights_in_qdqbert
base_model_prefix = "bert"
supports_g... | class_definition | 31,053 | 32,212 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py | null | 10,150 |
class QDQBertModel(QDQBertPreTrainedModel):
"""
The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
cross-attention is added between the self-attention layers, following the architecture described in [Attention is
all you need](https://arxiv.org/... | class_definition | 35,892 | 45,209 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py | null | 10,151 |
class QDQBertLMHeadModel(QDQBertPreTrainedModel):
_tied_weights_keys = ["predictions.decoder.weight", "predictions.decoder.bias"]
def __init__(self, config):
super().__init__(config)
if not config.is_decoder:
logger.warning("If you want to use `QDQBertLMHeadModel` as a standalone, ... | class_definition | 45,346 | 53,045 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py | null | 10,152 |
class QDQBertForMaskedLM(QDQBertPreTrainedModel):
_tied_weights_keys = ["predictions.decoder.weight", "predictions.decoder.bias"]
def __init__(self, config):
super().__init__(config)
if config.is_decoder:
logger.warning(
"If you want to use `QDQBertForMaskedLM` make... | class_definition | 53,156 | 57,588 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py | null | 10,153 |
class QDQBertForNextSentencePrediction(QDQBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.bert = QDQBertModel(config)
self.cls = QDQBertOnlyNSPHead(config)
# Initialize weights and apply final processing
self.post_init()
@add_start_docst... | class_definition | 57,731 | 61,659 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py | null | 10,154 |
class QDQBertForSequenceClassification(QDQBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.bert = QDQBertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.c... | class_definition | 61,884 | 65,760 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py | null | 10,155 |
class QDQBertForMultipleChoice(QDQBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.bert = QDQBertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, 1)
# Initialize weights and ap... | class_definition | 65,994 | 69,511 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py | null | 10,156 |
class QDQBertForTokenClassification(QDQBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.bert = QDQBertModel(config, add_pooling_layer=False)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifi... | class_definition | 69,746 | 72,473 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py | null | 10,157 |
class QDQBertForQuestionAnswering(QDQBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.bert = QDQBertModel(config, add_pooling_layer=False)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
#... | class_definition | 72,766 | 77,001 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py | null | 10,158 |
class NatConfig(BackboneConfigMixin, PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`NatModel`]. It is used to instantiate a Nat model
according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yie... | class_definition | 914 | 6,974 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/configuration_nat.py | null | 10,159 |
class NatEncoderOutput(ModelOutput):
"""
Nat encoder's outputs, with potential hidden states and attentions.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
... | class_definition | 2,162 | 4,125 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py | null | 10,160 |
class NatModelOutput(ModelOutput):
"""
Nat 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 layer of the model.... | class_definition | 4,139 | 6,366 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py | null | 10,161 |
class NatImageClassifierOutput(ModelOutput):
"""
Nat outputs for image classification.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
logits (`torch.FloatTensor` of sh... | class_definition | 6,380 | 8,514 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py | null | 10,162 |
class NatEmbeddings(nn.Module):
"""
Construct the patch and position embeddings.
"""
def __init__(self, config):
super().__init__()
self.patch_embeddings = NatPatchEmbeddings(config)
self.norm = nn.LayerNorm(config.embed_dim)
self.dropout = nn.Dropout(config.hidden_dro... | class_definition | 8,517 | 9,111 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py | null | 10,163 |
class NatPatchEmbeddings(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, height, width, hidden_size)` to be consumed by a
Transformer.
"""
def __init__(self, config):
... | class_definition | 9,114 | 10,559 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py | null | 10,164 |
class NatDownsampler(nn.Module):
"""
Convolutional Downsampling Layer.
Args:
dim (`int`):
Number of input channels.
norm_layer (`nn.Module`, *optional*, defaults to `nn.LayerNorm`):
Normalization layer class.
"""
def __init__(self, dim: int, norm_layer: nn.M... | class_definition | 10,562 | 11,355 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py | null | 10,165 |
class NatDropPath(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.Tensor) -> torch.... | class_definition | 12,450 | 12,927 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py | null | 10,166 |
class NeighborhoodAttention(nn.Module):
def __init__(self, config, dim, num_heads, kernel_size):
super().__init__()
if dim % num_heads != 0:
raise ValueError(
f"The hidden size ({dim}) is not a multiple of the number of attention heads ({num_heads})"
)
... | class_definition | 12,930 | 15,928 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py | null | 10,167 |
class NeighborhoodAttentionOutput(nn.Module):
def __init__(self, config, dim):
super().__init__()
self.dense = nn.Linear(dim, dim)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
... | class_definition | 15,931 | 16,381 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py | null | 10,168 |
class NeighborhoodAttentionModule(nn.Module):
def __init__(self, config, dim, num_heads, kernel_size):
super().__init__()
self.self = NeighborhoodAttention(config, dim, num_heads, kernel_size)
self.output = NeighborhoodAttentionOutput(config, dim)
self.pruned_heads = set()
def p... | class_definition | 16,384 | 17,951 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py | null | 10,169 |
class NatIntermediate(nn.Module):
def __init__(self, config, dim):
super().__init__()
self.dense = nn.Linear(dim, int(config.mlp_ratio * dim))
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.intermediate_a... | class_definition | 17,954 | 18,511 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py | null | 10,170 |
class NatOutput(nn.Module):
def __init__(self, config, dim):
super().__init__()
self.dense = nn.Linear(int(config.mlp_ratio * dim), dim)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.den... | class_definition | 18,514 | 18,932 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py | null | 10,171 |
class NatLayer(nn.Module):
def __init__(self, config, dim, num_heads, drop_path_rate=0.0):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.kernel_size = config.kernel_size
self.layernorm_before = nn.LayerNorm(dim, eps=config.layer_norm_eps)
... | class_definition | 18,935 | 21,845 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py | null | 10,172 |
class NatStage(nn.Module):
def __init__(self, config, dim, depth, num_heads, drop_path_rate, downsample):
super().__init__()
self.config = config
self.dim = dim
self.layers = nn.ModuleList(
[
NatLayer(
config=config,
... | class_definition | 21,848 | 23,310 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py | null | 10,173 |
class NatEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.num_levels = len(config.depths)
self.config = config
dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, sum(config.depths))]
self.levels = nn.ModuleList(
[
... | class_definition | 23,313 | 26,359 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py | null | 10,174 |
class NatPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = NatConfig
base_model_prefix = "nat"
main_input_name = "pixel_values"
def _init_weights(self, module... | class_definition | 26,362 | 27,229 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py | null | 10,175 |
class NatModel(NatPreTrainedModel):
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
requires_backends(self, ["natten"])
self.config = config
self.num_levels = len(config.depths)
self.num_features = int(config.embed_dim * 2 ** (self.num_levels - ... | class_definition | 28,809 | 31,956 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py | null | 10,176 |
class NatForImageClassification(NatPreTrainedModel):
def __init__(self, config):
super().__init__(config)
requires_backends(self, ["natten"])
self.num_labels = config.num_labels
self.nat = NatModel(config)
# Classifier head
self.classifier = (
nn.Linear... | class_definition | 32,185 | 35,644 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py | null | 10,177 |
class NatBackbone(NatPreTrainedModel, BackboneMixin):
def __init__(self, config):
super().__init__(config)
super()._init_backbone(config)
requires_backends(self, ["natten"])
self.embeddings = NatEmbeddings(config)
self.encoder = NatEncoder(config)
self.num_features ... | class_definition | 35,771 | 39,727 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py | null | 10,178 |
class OpenLlamaConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`OpenLlamaModel`]. It is used to instantiate an
Open-Llama model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yiel... | class_definition | 1,094 | 7,770 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/configuration_open_llama.py | null | 10,179 |
class OpenLlamaRMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
OpenLlamaRMSNorm 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 | 1,759 | 2,487 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py | null | 10,180 |
class OpenLlamaRotaryEmbedding(nn.Module):
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
super().__init__()
self.dim = dim
self.max_position_embeddings = max_position_embeddings
self.base = base
inv_freq = 1.0 / (self.base ** (torch.arange(0... | class_definition | 2,490 | 4,095 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py | null | 10,181 |
class OpenLlamaLinearScalingRotaryEmbedding(OpenLlamaRotaryEmbedding):
"""OpenLlamaRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
self.scaling_factor = scaling_... | class_definition | 4,098 | 5,104 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py | null | 10,182 |
class OpenLlamaDynamicNTKScalingRotaryEmbedding(OpenLlamaRotaryEmbedding):
"""OpenLlamaRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
self.sca... | class_definition | 5,107 | 6,529 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py | null | 10,183 |
class OpenLlamaMLP(nn.Module):
def __init__(
self,
hidden_size: int,
intermediate_size: int,
hidden_act: str,
dropout_prob: float,
):
super().__init__()
self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
self.down_proj = nn.Line... | class_definition | 8,427 | 9,098 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py | null | 10,184 |
class OpenLlamaAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: OpenLlamaConfig):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.num_heads = config.num_attention_heads
... | class_definition | 9,101 | 15,344 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py | null | 10,185 |
class OpenLlamaDecoderLayer(nn.Module):
def __init__(self, config: OpenLlamaConfig):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = OpenLlamaAttention(config=config)
self.mlp = OpenLlamaMLP(
hidden_size=self.hidden_size,
intermediate... | class_definition | 15,347 | 18,235 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py | null | 10,186 |
class OpenLlamaPreTrainedModel(PreTrainedModel):
config_class = OpenLlamaConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["OpenLlamaDecoderLayer"]
def _init_weights(self, module):
std = self.config.initializer_range
if isinstance(module,... | class_definition | 19,276 | 20,098 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py | null | 10,187 |
class OpenLlamaModel(OpenLlamaPreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`OpenLlamaDecoderLayer`]
Args:
config: OpenLlamaConfig
"""
def __init__(self, config: OpenLlamaConfig):
super().__init__(config)
self.pa... | class_definition | 24,220 | 30,661 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py | null | 10,188 |
class OpenLlamaForCausalLM(OpenLlamaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.model = OpenLlamaModel(config)
if config.shared_input_output_embedding:
self.lm_head = None
else:
self.lm_head = nn.Linear(config.hidden_size, confi... | class_definition | 30,664 | 37,730 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py | null | 10,189 |
class OpenLlamaForSequenceClassification(OpenLlamaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = OpenLlamaModel(config)
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
# Initiali... | class_definition | 38,532 | 43,379 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py | null | 10,190 |
class PositionalEmbedding(nn.Module):
def __init__(self, demb):
super().__init__()
self.demb = demb
inv_freq = 1 / (10000 ** (torch.arange(0.0, demb, 2.0) / demb))
self.register_buffer("inv_freq", inv_freq)
def forward(self, pos_seq, bsz=None):
sinusoid_inp = torch.out... | class_definition | 7,120 | 7,685 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py | null | 10,191 |
class PositionwiseFF(nn.Module):
def __init__(self, d_model, d_inner, dropout, pre_lnorm=False, layer_norm_epsilon=1e-5):
super().__init__()
self.d_model = d_model
self.d_inner = d_inner
self.dropout = dropout
self.CoreNet = nn.Sequential(
nn.Linear(d_model, d_i... | class_definition | 7,688 | 8,750 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py | null | 10,192 |
class RelPartialLearnableMultiHeadAttn(nn.Module):
def __init__(
self,
n_head,
d_model,
d_head,
dropout,
dropatt=0,
pre_lnorm=False,
r_r_bias=None,
r_w_bias=None,
layer_norm_epsilon=1e-5,
):
super().__init__()
self.... | class_definition | 8,753 | 13,621 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py | null | 10,193 |
class RelPartialLearnableDecoderLayer(nn.Module):
def __init__(self, n_head, d_model, d_head, d_inner, dropout, layer_norm_epsilon=1e-5, **kwargs):
super().__init__()
self.dec_attn = RelPartialLearnableMultiHeadAttn(
n_head, d_model, d_head, dropout, layer_norm_epsilon=layer_norm_epsilo... | class_definition | 13,624 | 14,582 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py | null | 10,194 |
class AdaptiveEmbedding(nn.Module):
def __init__(self, n_token, d_embed, d_proj, cutoffs, div_val=1, sample_softmax=False):
super().__init__()
self.n_token = n_token
self.d_embed = d_embed
self.cutoffs = cutoffs + [n_token]
self.div_val = div_val
self.d_proj = d_pro... | class_definition | 14,585 | 16,818 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py | null | 10,195 |
class TransfoXLPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = TransfoXLConfig
load_tf_weights = load_tf_weights_in_transfo_xl
base_model_prefix = "transformer"
... | class_definition | 16,821 | 23,071 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py | null | 10,196 |
class TransfoXLModelOutput(ModelOutput):
"""
Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding).
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the ou... | class_definition | 23,085 | 24,905 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py | null | 10,197 |
class TransfoXLSequenceClassifierOutputWithPast(ModelOutput):
"""
Base class for outputs of sentence classification models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
... | class_definition | 24,919 | 26,912 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py | null | 10,198 |
class TransfoXLLMHeadModelOutput(ModelOutput):
"""
Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding).
Args:
losses (`torch.FloatTensor` of shape *(batch_size, sequence_length-1)*, *optional*, returned when `labels` is provided):
Lan... | class_definition | 26,926 | 29,596 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py | null | 10,199 |
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