id int64 0 328k | repository_name stringlengths 7 58 | file_path stringlengths 9 302 | class_name stringlengths 5 256 | human_written_code stringlengths 16 2.16M | class_skeleton stringlengths 18 1.49M ⌀ | total_program_units int64 1 1.76k | total_doc_str int64 0 771 | AvgCountLine float64 0 7.89k | AvgCountLineBlank float64 0 297 | AvgCountLineCode float64 0 7.89k | AvgCountLineComment float64 0 7.89k | AvgCyclomatic float64 0 130 | CommentToCodeRatio float64 0 168 | CountClassBase float64 0 40 | CountClassCoupled float64 0 583 | CountClassCoupledModified float64 0 575 | CountClassDerived float64 0 5.35k | CountDeclInstanceMethod float64 0 529 | CountDeclInstanceVariable float64 0 296 | CountDeclMethod float64 0 599 | CountDeclMethodAll float64 0 1.12k | CountLine float64 1 40.4k | CountLineBlank float64 0 8.16k | CountLineCode float64 1 25.7k | CountLineCodeDecl float64 1 8.15k | CountLineCodeExe float64 0 24.2k | CountLineComment float64 0 16.5k | CountStmt float64 1 9.71k | CountStmtDecl float64 1 8.15k | CountStmtExe float64 0 9.69k | MaxCyclomatic float64 0 759 | MaxInheritanceTree float64 0 16 | MaxNesting float64 0 34 | SumCyclomatic float64 0 2.9k |
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800 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bert/modeling_bert.py | transformers.models.bert.modeling_bert.BertOnlyMLMHead | from torch import nn
import torch
class BertOnlyMLMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = BertLMPredictionHead(config)
def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
prediction_scores = self.predictions(sequence_output)
... |
class BertOnlyMLMHead(nn.Module):
def __init__(self, config):
pass
def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
pass | 3 | 0 | 3 | 0 | 3 | 0 | 1 | 0 | 1 | 3 | 1 | 0 | 2 | 1 | 2 | 12 | 8 | 1 | 7 | 5 | 4 | 0 | 7 | 5 | 4 | 1 | 1 | 0 | 2 |
801 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bert/modeling_bert.py | transformers.models.bert.modeling_bert.BertOnlyNSPHead | from torch import nn
class BertOnlyNSPHead(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_relationshi... |
class BertOnlyNSPHead(nn.Module):
def __init__(self, config):
pass
def forward(self, pooled_output):
pass | 3 | 0 | 3 | 0 | 3 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 2 | 1 | 2 | 12 | 8 | 1 | 7 | 5 | 4 | 0 | 7 | 5 | 4 | 1 | 1 | 0 | 2 |
802 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bert/modeling_bert.py | transformers.models.bert.modeling_bert.BertOutput | import torch
from torch import nn
class BertOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(... |
class BertOutput(nn.Module):
def __init__(self, config):
pass
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
pass | 3 | 0 | 5 | 0 | 5 | 0 | 1 | 0 | 1 | 2 | 0 | 0 | 2 | 3 | 2 | 12 | 12 | 1 | 11 | 6 | 8 | 0 | 11 | 6 | 8 | 1 | 1 | 0 | 2 |
803 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bert/modeling_bert.py | transformers.models.bert.modeling_bert.BertPooler | from torch import nn
import torch
class BertPooler(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:
first_token_... |
class BertPooler(nn.Module):
def __init__(self, config):
pass
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
pass | 3 | 0 | 6 | 0 | 5 | 1 | 1 | 0.2 | 1 | 2 | 0 | 0 | 2 | 2 | 2 | 12 | 13 | 1 | 10 | 7 | 7 | 2 | 10 | 7 | 7 | 1 | 1 | 0 | 2 |
804 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bert/modeling_bert.py | transformers.models.bert.modeling_bert.BertPreTrainedModel | from torch import nn
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from .configuration_bert import BertConfig
from ...utils import ModelOutput, TransformersKwargs, auto_docstring, is_torch_flex_attn_available, logging
@auto_docstring
class BertPreTrainedModel(PreTrainedModel):
config_class... | @auto_docstring
class BertPreTrainedModel(PreTrainedModel):
def _init_weights(self, module):
'''Initialize the weights'''
pass | 3 | 1 | 15 | 0 | 12 | 3 | 6 | 0.39 | 1 | 0 | 0 | 9 | 1 | 0 | 1 | 1 | 27 | 2 | 18 | 7 | 16 | 7 | 16 | 7 | 14 | 6 | 1 | 2 | 6 |
805 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bert/modeling_bert.py | transformers.models.bert.modeling_bert.BertPreTrainingHeads | from torch import nn
class BertPreTrainingHeads(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = BertLMPredictionHead(config)
self.seq_relationship = nn.Linear(config.hidden_size, 2)
def forward(self, sequence_output, pooled_output):
prediction_sco... |
class BertPreTrainingHeads(nn.Module):
def __init__(self, config):
pass
def forward(self, sequence_output, pooled_output):
pass | 3 | 0 | 4 | 0 | 4 | 0 | 1 | 0 | 1 | 2 | 1 | 0 | 2 | 2 | 2 | 12 | 10 | 1 | 9 | 7 | 6 | 0 | 9 | 7 | 6 | 1 | 1 | 0 | 2 |
806 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bert/modeling_bert.py | transformers.models.bert.modeling_bert.BertPredictionHeadTransform | import torch
from ...activations import ACT2FN
from torch import nn
class BertPredictionHeadTransform(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_... |
class BertPredictionHeadTransform(nn.Module):
def __init__(self, config):
pass
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
pass | 3 | 0 | 7 | 0 | 7 | 0 | 2 | 0 | 1 | 3 | 0 | 0 | 2 | 3 | 2 | 12 | 15 | 1 | 14 | 6 | 11 | 0 | 13 | 6 | 10 | 2 | 1 | 1 | 3 |
807 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bert/modeling_bert.py | transformers.models.bert.modeling_bert.BertSelfAttention | from ...cache_utils import Cache, EncoderDecoderCache
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
import torch
from torch import nn
from ...processing_utils import Unpack
from typing import Callable, Optional, Union
from ...utils import ModelOutput, TransformersKwargs, auto_docstring, is_torc... |
class BertSelfAttention(nn.Module):
def __init__(self, config, position_embedding_type=None, is_causal=False, layer_idx=None):
pass
def forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor]=None, head_mask: Optional[torch.FloatTensor]=None, past_key_value: Optional[Ca... | 3 | 0 | 43 | 7 | 31 | 6 | 6 | 0.19 | 1 | 5 | 0 | 1 | 3 | 11 | 3 | 13 | 132 | 22 | 93 | 44 | 80 | 18 | 72 | 35 | 68 | 13 | 1 | 2 | 17 |
808 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bert/modeling_bert.py | transformers.models.bert.modeling_bert.BertSelfOutput | from torch import nn
import torch
class BertSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(co... |
class BertSelfOutput(nn.Module):
def __init__(self, config):
pass
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
pass | 3 | 0 | 5 | 0 | 5 | 0 | 1 | 0 | 1 | 2 | 0 | 0 | 2 | 3 | 2 | 12 | 12 | 1 | 11 | 6 | 8 | 0 | 11 | 6 | 8 | 1 | 1 | 0 | 2 |
809 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bert/tokenization_bert.py | transformers.models.bert.tokenization_bert.BertTokenizer | import collections
from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
import os
from typing import Optional
class BertTokenizer(PreTrainedTokenizer):
"""
Construct a BERT tokenizer. Based on WordPiece.
This tokenizer inherits from [`PreTrainedTokenizer`] wh... |
class BertTokenizer(PreTrainedTokenizer):
'''
Construct a BERT tokenizer. Based on WordPiece.
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
Args:
vocab_file (`... | 14 | 6 | 15 | 1 | 10 | 4 | 2 | 0.72 | 1 | 9 | 2 | 3 | 12 | 5 | 12 | 101 | 236 | 29 | 121 | 53 | 85 | 87 | 65 | 29 | 52 | 6 | 3 | 3 | 27 |
810 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bert/tokenization_bert_fast.py | transformers.models.bert.tokenization_bert_fast.BertTokenizerFast | import json
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from tokenizers import normalizers
from typing import Optional
from .tokenization_bert import BertTokenizer
class BertTokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "fast" BERT tokenizer (backed by HuggingFace's *tokenizers* li... |
class BertTokenizerFast(PreTrainedTokenizerFast):
'''
Construct a "fast" BERT tokenizer (backed by HuggingFace's *tokenizers* library). Based on WordPiece.
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
refer to this superclass for more in... | 4 | 2 | 24 | 3 | 14 | 7 | 2 | 1.12 | 1 | 4 | 0 | 3 | 4 | 1 | 4 | 92 | 141 | 18 | 58 | 29 | 38 | 65 | 27 | 14 | 22 | 2 | 3 | 1 | 7 |
811 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bert_generation/configuration_bert_generation.py | transformers.models.bert_generation.configuration_bert_generation.BertGenerationConfig | from ...configuration_utils import PretrainedConfig
class BertGenerationConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`BertGenerationPreTrainedModel`]. It is used to
instantiate a BertGeneration model according to the specified arguments, defining the model ... |
class BertGenerationConfig(PretrainedConfig):
'''
This is the configuration class to store the configuration of a [`BertGenerationPreTrainedModel`]. It is used to
instantiate a BertGeneration model according to the specified arguments, defining the model architecture.
Instantiating a configuration with... | 2 | 1 | 35 | 1 | 34 | 0 | 1 | 1.64 | 1 | 1 | 0 | 0 | 1 | 13 | 1 | 1 | 105 | 10 | 36 | 35 | 15 | 59 | 17 | 16 | 15 | 1 | 1 | 0 | 1 |
812 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bert_generation/modeling_bert_generation.py | transformers.models.bert_generation.modeling_bert_generation.BertEncoder | import torch
from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions
from ...cache_utils import Cache, EncoderDecoderCache
from typing import Callable, Optional, Union
from ...processing_utils import Unpack
from ...utils import TransformersKwargs, auto_docstring, is_... |
class BertEncoder(nn.Module):
def __init__(self, config):
pass
def forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor]=None, head_mask: Optional[torch.FloatTensor]=None, encoder_hidden_states: Optional[torch.FloatTensor]=None, encoder_attention_mask: Optional[torch.... | 3 | 0 | 45 | 4 | 41 | 0 | 9 | 0 | 1 | 8 | 2 | 0 | 2 | 3 | 2 | 12 | 91 | 8 | 83 | 26 | 68 | 0 | 35 | 14 | 32 | 17 | 1 | 3 | 18 |
813 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bert_generation/modeling_bert_generation.py | transformers.models.bert_generation.modeling_bert_generation.BertGenerationAttention | import torch
from ...cache_utils import Cache, EncoderDecoderCache
from ...processing_utils import Unpack
from typing import Callable, Optional, Union
from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import TransformersKwargs, auto_docstring, is_... |
class BertGenerationAttention(nn.Module):
def __init__(self, config, position_embedding_type=None, is_causal=False, layer_idx=None, is_cross_attention=False):
pass
def prune_heads(self, heads):
pass
def forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor... | 4 | 0 | 15 | 1 | 14 | 1 | 1 | 0.07 | 1 | 5 | 1 | 0 | 3 | 3 | 3 | 13 | 49 | 4 | 43 | 20 | 30 | 3 | 22 | 11 | 18 | 2 | 1 | 1 | 4 |
814 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bert_generation/modeling_bert_generation.py | transformers.models.bert_generation.modeling_bert_generation.BertGenerationDecoder | from ...generation import GenerationMixin
import torch
from ...utils.generic import can_return_tuple, check_model_inputs
from ...utils import TransformersKwargs, auto_docstring, is_torch_flex_attn_available, logging
from typing import Callable, Optional, Union
from ...modeling_outputs import BaseModelOutputWithPastAndC... | @auto_docstring(custom_intro='\n BertGeneration Model with a `language modeling` head on top for CLM fine-tuning.\n ')
class BertGenerationDecoder(BertGenerationPreTrainedModel, GenerationMixin):
def __init__(self, config):
pass
def get_output_embeddings(self):
pass
def set_output_e... | 8 | 1 | 26 | 3 | 15 | 7 | 2 | 0.47 | 2 | 7 | 3 | 0 | 5 | 2 | 5 | 6 | 137 | 21 | 79 | 33 | 55 | 37 | 33 | 16 | 27 | 6 | 2 | 1 | 12 |
815 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bert_generation/modeling_bert_generation.py | transformers.models.bert_generation.modeling_bert_generation.BertGenerationEmbeddings | from torch import nn
import torch
class BertGenerationEmbeddings(nn.Module):
"""Construct the embeddings from word and position embeddings."""
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_... |
class BertGenerationEmbeddings(nn.Module):
'''Construct the embeddings from word and position embeddings.'''
def __init__(self, config):
pass
def forward(self, input_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0):
pass | 3 | 1 | 16 | 3 | 12 | 2 | 3 | 0.16 | 1 | 1 | 0 | 0 | 2 | 4 | 2 | 12 | 36 | 7 | 25 | 11 | 22 | 4 | 22 | 11 | 19 | 4 | 1 | 1 | 5 |
816 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bert_generation/modeling_bert_generation.py | transformers.models.bert_generation.modeling_bert_generation.BertGenerationEncoder | from typing import Callable, Optional, Union
from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions
from ...masking_utils import create_causal_mask
from ...modeling_attn_mask_utils import _prepare_4d_attention_mask, _prepare_4d_attention_mask_for_sdpa
from ...utils ... | @auto_docstring(custom_intro='\n The bare BertGeneration model transformer outputting raw hidden-states without any specific head on top.\n ')
class BertGenerationEncoder(BertGenerationPreTrainedModel):
'''
The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a... | 12 | 2 | 27 | 3 | 18 | 7 | 4 | 0.46 | 1 | 7 | 3 | 0 | 5 | 3 | 5 | 6 | 164 | 23 | 97 | 37 | 70 | 45 | 47 | 21 | 41 | 15 | 2 | 2 | 20 |
817 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bert_generation/modeling_bert_generation.py | transformers.models.bert_generation.modeling_bert_generation.BertGenerationIntermediate | import torch
from torch import nn
from ...activations import ACT2FN
class BertGenerationIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.inter... |
class BertGenerationIntermediate(nn.Module):
def __init__(self, config):
pass
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
pass | 3 | 0 | 6 | 0 | 6 | 0 | 2 | 0 | 1 | 3 | 0 | 0 | 2 | 2 | 2 | 12 | 13 | 1 | 12 | 5 | 9 | 0 | 11 | 5 | 8 | 2 | 1 | 1 | 3 |
818 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bert_generation/modeling_bert_generation.py | transformers.models.bert_generation.modeling_bert_generation.BertGenerationLayer | import torch
from ...utils import TransformersKwargs, auto_docstring, is_torch_flex_attn_available, logging
from ...cache_utils import Cache, EncoderDecoderCache
from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
from ...modeling_layers import GradientCheckpoint... |
class BertGenerationLayer(GradientCheckpointingLayer):
def __init__(self, config, layer_idx=None):
pass
def forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor]=None, head_mask: Optional[torch.FloatTensor]=None, encoder_hidden_states: Optional[torch.FloatTensor]=None... | 4 | 0 | 27 | 2 | 23 | 2 | 4 | 0.1 | 1 | 7 | 3 | 0 | 3 | 8 | 3 | 13 | 84 | 9 | 70 | 32 | 57 | 7 | 41 | 23 | 37 | 7 | 1 | 2 | 11 |
819 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bert_generation/modeling_bert_generation.py | transformers.models.bert_generation.modeling_bert_generation.BertGenerationOnlyLMHead | from torch import nn
import torch
class BertGenerationOnlyLMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.decoder = nn.Linear(config.hidden_size, config.vocab_size)
self.bias = nn.Parameter(torch.zeros(config.vocab_size))
self.decoder.bias = self.bias
de... |
class BertGenerationOnlyLMHead(nn.Module):
def __init__(self, config):
pass
def forward(self, hidden_states):
pass
def _tie_weights(self):
pass | 4 | 0 | 5 | 0 | 4 | 1 | 1 | 0.14 | 1 | 1 | 0 | 0 | 3 | 2 | 3 | 13 | 18 | 2 | 14 | 7 | 10 | 2 | 13 | 7 | 9 | 2 | 1 | 1 | 4 |
820 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bert_generation/modeling_bert_generation.py | transformers.models.bert_generation.modeling_bert_generation.BertGenerationOutput | import torch
from torch import nn
class BertGenerationOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = n... |
class BertGenerationOutput(nn.Module):
def __init__(self, config):
pass
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
pass | 3 | 0 | 5 | 0 | 5 | 0 | 1 | 0 | 1 | 2 | 0 | 0 | 2 | 3 | 2 | 12 | 12 | 1 | 11 | 6 | 8 | 0 | 11 | 6 | 8 | 1 | 1 | 0 | 2 |
821 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bert_generation/modeling_bert_generation.py | transformers.models.bert_generation.modeling_bert_generation.BertGenerationPreTrainedModel | from torch import nn
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from .configuration_bert_generation import BertGenerationConfig
from ...utils import TransformersKwargs, auto_docstring, is_torch_flex_attn_available, logging
@auto_docstring
class BertGenerationPreTrainedModel(PreTrainedModel)... | @auto_docstring
class BertGenerationPreTrainedModel(PreTrainedModel):
def _init_weights(self, module):
'''Initialize the weights'''
pass | 3 | 1 | 15 | 0 | 12 | 3 | 6 | 0.44 | 1 | 0 | 0 | 2 | 1 | 0 | 1 | 1 | 25 | 2 | 16 | 5 | 14 | 7 | 14 | 5 | 12 | 6 | 1 | 2 | 6 |
822 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bert_generation/modeling_bert_generation.py | transformers.models.bert_generation.modeling_bert_generation.BertGenerationSelfAttention | from ...processing_utils import Unpack
from typing import Callable, Optional, Union
from ...cache_utils import Cache, EncoderDecoderCache
from ...utils import TransformersKwargs, auto_docstring, is_torch_flex_attn_available, logging
import torch
from torch import nn
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS... |
class BertGenerationSelfAttention(nn.Module):
def __init__(self, config, position_embedding_type=None, is_causal=False, layer_idx=None):
pass
def forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor]=None, head_mask: Optional[torch.FloatTensor]=None, past_key_value: O... | 3 | 0 | 43 | 7 | 31 | 6 | 6 | 0.19 | 1 | 5 | 0 | 0 | 3 | 11 | 3 | 13 | 132 | 22 | 93 | 44 | 80 | 18 | 72 | 35 | 68 | 13 | 1 | 2 | 17 |
823 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bert_generation/modeling_bert_generation.py | transformers.models.bert_generation.modeling_bert_generation.BertGenerationSelfOutput | import torch
from torch import nn
class BertGenerationSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.... |
class BertGenerationSelfOutput(nn.Module):
def __init__(self, config):
pass
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
pass | 3 | 0 | 5 | 0 | 5 | 0 | 1 | 0 | 1 | 2 | 0 | 0 | 2 | 3 | 2 | 12 | 12 | 1 | 11 | 6 | 8 | 0 | 11 | 6 | 8 | 1 | 1 | 0 | 2 |
824 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bert_generation/tokenization_bert_generation.py | transformers.models.bert_generation.tokenization_bert_generation.BertGenerationTokenizer | from typing import Any, Optional
import sentencepiece as spm
from shutil import copyfile
import os
from ...tokenization_utils import PreTrainedTokenizer
from ...utils.import_utils import requires
@requires(backends=('sentencepiece',))
class BertGenerationTokenizer(PreTrainedTokenizer):
"""
Construct a BertGene... | @requires(backends=('sentencepiece',))
class BertGenerationTokenizer(PreTrainedTokenizer):
'''
Construct a BertGeneration tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should r... | 13 | 5 | 9 | 1 | 7 | 1 | 2 | 0.53 | 1 | 4 | 0 | 0 | 10 | 4 | 10 | 99 | 141 | 23 | 77 | 38 | 55 | 41 | 54 | 26 | 43 | 5 | 3 | 2 | 18 |
825 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bert_japanese/tokenization_bert_japanese.py | transformers.models.bert_japanese.tokenization_bert_japanese.BertJapaneseTokenizer | import copy
from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
import os
import collections
from typing import Any, Optional
class BertJapaneseTokenizer(PreTrainedTokenizer):
"""
Construct a BERT tokenizer for Japanese text.
This tokenizer inherits from [`P... |
class BertJapaneseTokenizer(PreTrainedTokenizer):
'''
Construct a BERT tokenizer for Japanese text.
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer
to: this superclass for more information regarding those methods.
Args:
vocab_... | 16 | 6 | 19 | 1 | 14 | 3 | 4 | 0.37 | 1 | 15 | 7 | 0 | 14 | 15 | 14 | 103 | 312 | 33 | 204 | 72 | 162 | 76 | 115 | 44 | 100 | 13 | 3 | 4 | 49 |
826 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bert_japanese/tokenization_bert_japanese.py | transformers.models.bert_japanese.tokenization_bert_japanese.CharacterTokenizer | import unicodedata
class CharacterTokenizer:
"""Runs Character tokenization."""
def __init__(self, vocab, unk_token, normalize_text=True):
"""
Constructs a CharacterTokenizer.
Args:
**vocab**:
Vocabulary object.
**unk_token**: str
... |
class CharacterTokenizer:
'''Runs Character tokenization.'''
def __init__(self, vocab, unk_token, normalize_text=True):
'''
Constructs a CharacterTokenizer.
Args:
**vocab**:
Vocabulary object.
**unk_token**: str
A special symbol f... | 3 | 3 | 20 | 4 | 7 | 10 | 3 | 1.33 | 0 | 0 | 0 | 0 | 2 | 3 | 2 | 2 | 44 | 9 | 15 | 8 | 12 | 20 | 15 | 8 | 12 | 4 | 0 | 2 | 5 |
827 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bert_japanese/tokenization_bert_japanese.py | transformers.models.bert_japanese.tokenization_bert_japanese.JumanppTokenizer | import unicodedata
class JumanppTokenizer:
"""Runs basic tokenization with jumanpp morphological parser."""
def __init__(self, do_lower_case=False, never_split=None, normalize_text=True, trim_whitespace=False):
"""
Constructs a JumanppTokenizer.
Args:
**do_lower_case**: (*... |
class JumanppTokenizer:
'''Runs basic tokenization with jumanpp morphological parser.'''
def __init__(self, do_lower_case=False, never_split=None, normalize_text=True, trim_whitespace=False):
'''
Constructs a JumanppTokenizer.
Args:
**do_lower_case**: (*optional*) boolean (... | 3 | 3 | 31 | 6 | 18 | 7 | 5 | 0.41 | 0 | 1 | 0 | 0 | 2 | 5 | 2 | 2 | 65 | 13 | 37 | 18 | 27 | 15 | 27 | 12 | 23 | 7 | 0 | 3 | 10 |
828 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bert_japanese/tokenization_bert_japanese.py | transformers.models.bert_japanese.tokenization_bert_japanese.MecabTokenizer | import unicodedata
from typing import Any, Optional
import os
class MecabTokenizer:
"""Runs basic tokenization with MeCab morphological parser."""
def __init__(self, do_lower_case=False, never_split=None, normalize_text=True, mecab_dic: Optional[str]='unidic_lite', mecab_option: Optional[str]=None):
"... |
class MecabTokenizer:
'''Runs basic tokenization with MeCab morphological parser.'''
def __init__(self, do_lower_case=False, never_split=None, normalize_text=True, mecab_dic: Optional[str]='unidic_lite', mecab_option: Optional[str]=None):
'''
Constructs a MecabTokenizer.
Args:
... | 3 | 3 | 51 | 9 | 34 | 9 | 8 | 0.26 | 0 | 3 | 0 | 0 | 2 | 4 | 2 | 2 | 106 | 19 | 69 | 24 | 55 | 18 | 44 | 16 | 37 | 11 | 0 | 3 | 16 |
829 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bert_japanese/tokenization_bert_japanese.py | transformers.models.bert_japanese.tokenization_bert_japanese.SentencepieceTokenizer | from typing import Any, Optional
import unicodedata
class SentencepieceTokenizer:
"""
Runs sentencepiece tokenization. Based on transformers.models.albert.tokenization_albert.AlbertTokenizer.
"""
def __init__(self, vocab, unk_token, do_lower_case=False, remove_space=True, keep_accents=True, sp_model_k... |
class SentencepieceTokenizer:
'''
Runs sentencepiece tokenization. Based on transformers.models.albert.tokenization_albert.AlbertTokenizer.
'''
def __init__(self, vocab, unk_token, do_lower_case=False, remove_space=True, keep_accents=True, sp_model_kwargs: Optional[dict[str, Any]]=None):
pass
... | 4 | 2 | 20 | 2 | 15 | 3 | 4 | 0.23 | 0 | 2 | 0 | 0 | 3 | 7 | 3 | 3 | 67 | 9 | 47 | 24 | 35 | 11 | 36 | 16 | 32 | 5 | 0 | 4 | 11 |
830 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bert_japanese/tokenization_bert_japanese.py | transformers.models.bert_japanese.tokenization_bert_japanese.SudachiTokenizer | from ...utils import is_sentencepiece_available, is_sudachi_projection_available, logging
import unicodedata
class SudachiTokenizer:
"""Runs basic tokenization with Sudachi morphological parser."""
def __init__(self, do_lower_case=False, never_split=None, normalize_text=True, trim_whitespace=False, sudachi_sp... |
class SudachiTokenizer:
'''Runs basic tokenization with Sudachi morphological parser.'''
def __init__(self, do_lower_case=False, never_split=None, normalize_text=True, trim_whitespace=False, sudachi_split_mode='A', sudachi_config_path=None, sudachi_resource_dir=None, sudachi_dict_type='core', sudachi_projecti... | 3 | 3 | 46 | 6 | 29 | 11 | 8 | 0.4 | 0 | 2 | 0 | 0 | 2 | 7 | 2 | 2 | 95 | 14 | 58 | 26 | 43 | 23 | 36 | 15 | 32 | 8 | 0 | 3 | 15 |
831 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bertweet/tokenization_bertweet.py | transformers.models.bertweet.tokenization_bertweet.BertweetTokenizer | from ...tokenization_utils import PreTrainedTokenizer
import os
import re
from typing import Optional
from shutil import copyfile
class BertweetTokenizer(PreTrainedTokenizer):
"""
Constructs a BERTweet tokenizer, using Byte-Pair-Encoding.
This tokenizer inherits from [`PreTrainedTokenizer`] which contains... |
class BertweetTokenizer(PreTrainedTokenizer):
'''
Constructs a BERTweet tokenizer, using Byte-Pair-Encoding.
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
Args:
... | 17 | 11 | 20 | 2 | 14 | 3 | 3 | 0.44 | 1 | 16 | 1 | 0 | 15 | 10 | 15 | 104 | 370 | 58 | 218 | 80 | 181 | 95 | 154 | 56 | 137 | 9 | 3 | 3 | 47 |
832 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bertweet/tokenization_bertweet.py | transformers.models.bertweet.tokenization_bertweet.TweetTokenizer | class TweetTokenizer:
"""
Examples:
```python
>>> # Tokenizer for tweets.
>>> from nltk.tokenize import TweetTokenizer
>>> tknzr = TweetTokenizer()
>>> s0 = "This is a cooool #dummysmiley: :-) :-P <3 and some arrows < > -> <--"
>>> tknzr.tokenize(s0)
['This', 'is', 'a', 'cooool', '... | class TweetTokenizer:
'''
Examples:
```python
>>> # Tokenizer for tweets.
>>> from nltk.tokenize import TweetTokenizer
>>> tknzr = TweetTokenizer()
>>> s0 = "This is a cooool #dummysmiley: :-) :-P <3 and some arrows < > -> <--"
>>> tknzr.tokenize(s0)
['This', 'is', 'a', 'cooool', '#d... | 3 | 2 | 14 | 1 | 8 | 6 | 3 | 1.69 | 0 | 0 | 0 | 0 | 2 | 3 | 2 | 2 | 49 | 6 | 16 | 8 | 13 | 27 | 16 | 8 | 13 | 5 | 0 | 1 | 6 |
833 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/big_bird/configuration_big_bird.py | transformers.models.big_bird.configuration_big_bird.BigBirdConfig | from ...configuration_utils import PretrainedConfig
class BigBirdConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`BigBirdModel`]. It is used to instantiate an
BigBird model according to the specified arguments, defining the model architecture. Instantiating a ... |
class BigBirdConfig(PretrainedConfig):
'''
This is the configuration class to store the configuration of a [`BigBirdModel`]. It is used to instantiate an
BigBird model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a sim... | 2 | 1 | 55 | 2 | 53 | 0 | 1 | 1.16 | 1 | 1 | 0 | 0 | 1 | 19 | 1 | 1 | 131 | 12 | 55 | 48 | 27 | 64 | 23 | 22 | 21 | 1 | 1 | 0 | 1 |
834 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/big_bird/configuration_big_bird.py | transformers.models.big_bird.configuration_big_bird.BigBirdOnnxConfig | from collections.abc import Mapping
from ...onnx import OnnxConfig
from collections import OrderedDict
class BigBirdOnnxConfig(OnnxConfig):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
if self.task == 'multiple-choice':
dynamic_axis = {0: 'batch', 1: 'choice', 2: 'sequenc... |
class BigBirdOnnxConfig(OnnxConfig):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
pass | 3 | 0 | 11 | 0 | 11 | 0 | 2 | 0 | 1 | 3 | 0 | 0 | 1 | 0 | 1 | 1 | 13 | 0 | 13 | 4 | 10 | 0 | 6 | 3 | 4 | 2 | 1 | 1 | 2 |
835 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/big_bird/modeling_big_bird.py | transformers.models.big_bird.modeling_big_bird.BigBirdAttention | from torch import nn
from ...utils.deprecation import deprecate_kwarg
class BigBirdAttention(nn.Module):
def __init__(self, config, seed=None):
super().__init__()
self.attention_type = config.attention_type
self.config = config
self.seed = seed
if self.config.attention_type... |
class BigBirdAttention(nn.Module):
def __init__(self, config, seed=None):
pass
def set_attention_type(self, value: str, layer_idx=None):
pass
@deprecate_kwarg('past_key_value', new_name='past_key_values', version='4.58')
def forward(self, hidden_states, attention_mask=None, head_mask=... | 5 | 0 | 28 | 2 | 24 | 2 | 5 | 0.08 | 1 | 6 | 3 | 0 | 3 | 5 | 3 | 13 | 86 | 7 | 74 | 27 | 56 | 6 | 42 | 13 | 38 | 6 | 1 | 2 | 14 |
836 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/big_bird/modeling_big_bird.py | transformers.models.big_bird.modeling_big_bird.BigBirdBlockSparseAttention | from torch import nn
import math
import numpy as np
import torch
class BigBirdBlockSparseAttention(nn.Module):
def __init__(self, config, seed=None):
super().__init__()
self.max_seqlen = config.max_position_embeddings
self.seed = seed
if config.hidden_size % config.num_attention_he... |
class BigBirdBlockSparseAttention(nn.Module):
def __init__(self, config, seed=None):
pass
def forward(self, hidden_states, band_mask=None, from_mask=None, to_mask=None, from_blocked_mask=None, to_blocked_mask=None, output_attentions=None):
pass
@staticmethod
def torch_bmm_nd(inp_1, in... | 18 | 7 | 72 | 8 | 48 | 19 | 5 | 0.39 | 1 | 7 | 0 | 0 | 6 | 10 | 12 | 22 | 886 | 108 | 582 | 189 | 497 | 225 | 274 | 115 | 261 | 20 | 1 | 5 | 65 |
837 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/big_bird/modeling_big_bird.py | transformers.models.big_bird.modeling_big_bird.BigBirdClassificationHead | from torch import nn
from ...activations import ACT2FN
class BigBirdClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
classifier_dropout = conf... |
class BigBirdClassificationHead(nn.Module):
'''Head for sentence-level classification tasks.'''
def __init__(self, config):
pass
def forward(self, features, **kwargs):
pass | 3 | 1 | 9 | 1 | 9 | 1 | 2 | 0.11 | 1 | 1 | 0 | 0 | 2 | 4 | 2 | 12 | 22 | 3 | 18 | 9 | 15 | 2 | 16 | 9 | 13 | 2 | 1 | 0 | 3 |
838 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/big_bird/modeling_big_bird.py | transformers.models.big_bird.modeling_big_bird.BigBirdEmbeddings | from torch import nn
import torch
class BigBirdEmbeddings(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_t... |
class BigBirdEmbeddings(nn.Module):
'''Construct the embeddings from word, position and token_type embeddings.'''
def __init__(self, config):
pass
def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0):
pass | 3 | 1 | 31 | 6 | 22 | 4 | 4 | 0.2 | 1 | 1 | 0 | 0 | 2 | 8 | 2 | 12 | 67 | 13 | 45 | 20 | 40 | 9 | 37 | 18 | 34 | 7 | 1 | 2 | 8 |
839 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/big_bird/modeling_big_bird.py | transformers.models.big_bird.modeling_big_bird.BigBirdEncoder | from ...cache_utils import Cache, DynamicCache
from torch import nn
from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, BaseModelOutputWithPoolingAndCrossAttentions, CausalLMOutputWithCrossAttentions, MaskedLMOutput, MultipleChoiceModelOutput, SequenceClassifierOutput, TokenClassifierOutput
from ... |
class BigBirdEncoder(nn.Module):
def __init__(self, config):
pass
def set_attention_type(self, value: str):
pass
def forward(self, hidden_states, attention_mask=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, past_key_values=None, use_cache=None, output_att... | 4 | 0 | 39 | 3 | 36 | 0 | 7 | 0.01 | 1 | 8 | 2 | 0 | 3 | 4 | 3 | 13 | 120 | 11 | 108 | 33 | 88 | 1 | 44 | 17 | 40 | 17 | 1 | 3 | 22 |
840 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/big_bird/modeling_big_bird.py | transformers.models.big_bird.modeling_big_bird.BigBirdForCausalLM | from ...cache_utils import Cache, DynamicCache
from ...utils import ModelOutput, auto_docstring, logging
from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, BaseModelOutputWithPoolingAndCrossAttentions, CausalLMOutputWithCrossAttentions, MaskedLMOutput, MultipleChoiceModelOutput, SequenceClassifi... | @auto_docstring(custom_intro='\n BigBird Model with a `language modeling` head on top for CLM fine-tuning.\n ')
class BigBirdForCausalLM(BigBirdPreTrainedModel, GenerationMixin):
def __init__(self, config):
pass
def get_output_embeddings(self):
pass
def set_output_embeddings(self, n... | 7 | 1 | 22 | 2 | 15 | 5 | 2 | 0.27 | 2 | 6 | 3 | 0 | 5 | 2 | 5 | 6 | 121 | 14 | 84 | 34 | 55 | 23 | 31 | 16 | 25 | 5 | 2 | 1 | 11 |
841 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/big_bird/modeling_big_bird.py | transformers.models.big_bird.modeling_big_bird.BigBirdForMaskedLM | from ...utils import ModelOutput, auto_docstring, logging
from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, BaseModelOutputWithPoolingAndCrossAttentions, CausalLMOutputWithCrossAttentions, MaskedLMOutput, MultipleChoiceModelOutput, SequenceClassifierOutput, TokenClassifierOutput
from typing imp... | @auto_docstring
class BigBirdForMaskedLM(BigBirdPreTrainedModel):
def __init__(self, config):
pass
def get_output_embeddings(self):
pass
def set_output_embeddings(self, new_embeddings):
pass
@auto_docstring
def forward(self, input_ids: Optional[torch.LongTensor]=None, atte... | 8 | 1 | 26 | 4 | 14 | 9 | 2 | 0.59 | 1 | 6 | 3 | 0 | 5 | 2 | 5 | 6 | 140 | 23 | 74 | 33 | 52 | 44 | 36 | 18 | 30 | 5 | 2 | 1 | 11 |
842 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/big_bird/modeling_big_bird.py | transformers.models.big_bird.modeling_big_bird.BigBirdForMultipleChoice | from ...utils import ModelOutput, auto_docstring, logging
from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, BaseModelOutputWithPoolingAndCrossAttentions, CausalLMOutputWithCrossAttentions, MaskedLMOutput, MultipleChoiceModelOutput, SequenceClassifierOutput, TokenClassifierOutput
from typing imp... | @auto_docstring
class BigBirdForMultipleChoice(BigBirdPreTrainedModel):
def __init__(self, config):
pass
@auto_docstring
def forward(self, input_ids: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.FloatTensor]=None, token_type_ids: Optional[torch.LongTensor]=None, position_ids: Opt... | 5 | 1 | 37 | 5 | 29 | 4 | 6 | 0.1 | 1 | 4 | 2 | 0 | 2 | 3 | 2 | 3 | 84 | 10 | 67 | 29 | 44 | 7 | 28 | 14 | 25 | 11 | 2 | 1 | 12 |
843 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/big_bird/modeling_big_bird.py | transformers.models.big_bird.modeling_big_bird.BigBirdForPreTraining | from ...utils import ModelOutput, auto_docstring, logging
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from typing import Optional, Union
import torch
class BigBirdForPreTraining(BigBirdPreTrainedModel):
_tied_weights_keys = ['cls.predictions.decoder.weight', 'cls.predictions.decoder.bias']
... |
class BigBirdForPreTraining(BigBirdPreTrainedModel):
def __init__(self, config):
pass
def get_output_embeddings(self):
pass
def set_output_embeddings(self, new_embeddings):
pass
@auto_docstring
def forward(self, input_ids: Optional[torch.LongTensor]=None, attention_mask: ... | 6 | 1 | 24 | 4 | 14 | 7 | 2 | 0.44 | 1 | 5 | 3 | 0 | 4 | 2 | 4 | 5 | 104 | 19 | 59 | 29 | 39 | 26 | 28 | 15 | 23 | 6 | 2 | 1 | 9 |
844 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/big_bird/modeling_big_bird.py | transformers.models.big_bird.modeling_big_bird.BigBirdForPreTrainingOutput | from ...utils import ModelOutput, auto_docstring, logging
from dataclasses import dataclass
from typing import Optional, Union
import torch
@dataclass
@auto_docstring(custom_intro='\n Output type of [`BigBirdForPreTraining`].\n ')
class BigBirdForPreTrainingOutput(ModelOutput):
"""
loss (*optional*, retu... | @dataclass
@auto_docstring(custom_intro='\n Output type of [`BigBirdForPreTraining`].\n ')
class BigBirdForPreTrainingOutput(ModelOutput):
'''
loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
Total loss as the sum of the masked language modeling loss and ... | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 3.5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 31 | 4 | 6 | 6 | 5 | 21 | 6 | 6 | 5 | 0 | 1 | 0 | 0 |
845 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/big_bird/modeling_big_bird.py | transformers.models.big_bird.modeling_big_bird.BigBirdForQuestionAnswering | from ...utils import ModelOutput, auto_docstring, logging
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from typing import Optional, Union
import torch
@auto_docstring
class BigBirdForQuestionAnswering(BigBirdPreTrainedModel):
def __init__(self, config, add_pooling_layer=False):
"""
... | @auto_docstring
class BigBirdForQuestionAnswering(BigBirdPreTrainedModel):
def __init__(self, config, add_pooling_layer=False):
'''
add_pooling_layer (bool, *optional*, defaults to `True`):
Whether to add a pooling layer
'''
pass
@auto_docstring
def forward(self,... | 7 | 2 | 51 | 8 | 28 | 16 | 5 | 0.55 | 1 | 7 | 3 | 0 | 2 | 4 | 3 | 4 | 159 | 25 | 87 | 37 | 66 | 48 | 53 | 21 | 49 | 12 | 2 | 2 | 14 |
846 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/big_bird/modeling_big_bird.py | transformers.models.big_bird.modeling_big_bird.BigBirdForQuestionAnsweringHead | from torch import nn
class BigBirdForQuestionAnsweringHead(nn.Module):
"""Head for question answering tasks."""
def __init__(self, config):
super().__init__()
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.intermediate = BigBirdIntermediate(config)
self.output = Big... |
class BigBirdForQuestionAnsweringHead(nn.Module):
'''Head for question answering tasks.'''
def __init__(self, config):
pass
def forward(self, encoder_output):
pass | 3 | 1 | 6 | 0 | 6 | 0 | 1 | 0.08 | 1 | 3 | 2 | 0 | 2 | 4 | 2 | 12 | 16 | 2 | 13 | 8 | 10 | 1 | 13 | 8 | 10 | 1 | 1 | 0 | 2 |
847 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/big_bird/modeling_big_bird.py | transformers.models.big_bird.modeling_big_bird.BigBirdForSequenceClassification | from ...utils import ModelOutput, auto_docstring, logging
from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, BaseModelOutputWithPoolingAndCrossAttentions, CausalLMOutputWithCrossAttentions, MaskedLMOutput, MultipleChoiceModelOutput, SequenceClassifierOutput, TokenClassifierOutput
from typing imp... | @auto_docstring(custom_intro='\n BigBird Model transformer with a sequence classification/regression head on top (a linear layer on top of the\n pooled output) e.g. for GLUE tasks.\n ')
class BigBirdForSequenceClassification(BigBirdPreTrainedModel):
def __init__(self, config):
pass
@auto_docst... | 5 | 1 | 58 | 7 | 32 | 19 | 7 | 0.55 | 1 | 6 | 3 | 0 | 2 | 4 | 2 | 3 | 119 | 15 | 67 | 26 | 50 | 37 | 33 | 13 | 30 | 12 | 2 | 3 | 13 |
848 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/big_bird/modeling_big_bird.py | transformers.models.big_bird.modeling_big_bird.BigBirdForTokenClassification | from ...utils import ModelOutput, auto_docstring, logging
from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, BaseModelOutputWithPoolingAndCrossAttentions, CausalLMOutputWithCrossAttentions, MaskedLMOutput, MultipleChoiceModelOutput, SequenceClassifierOutput, TokenClassifierOutput
from typing imp... | @auto_docstring
class BigBirdForTokenClassification(BigBirdPreTrainedModel):
def __init__(self, config):
pass
@auto_docstring
def forward(self, input_ids: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.FloatTensor]=None, token_type_ids: Optional[torch.LongTensor]=None, position_ids... | 5 | 1 | 32 | 4 | 26 | 3 | 4 | 0.09 | 1 | 4 | 2 | 0 | 2 | 4 | 2 | 3 | 72 | 9 | 58 | 27 | 37 | 5 | 23 | 14 | 20 | 5 | 2 | 1 | 7 |
849 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/big_bird/modeling_big_bird.py | transformers.models.big_bird.modeling_big_bird.BigBirdIntermediate | from torch import nn
from ...activations import ACT2FN
import torch
class BigBirdIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate... |
class BigBirdIntermediate(nn.Module):
def __init__(self, config):
pass
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
pass | 3 | 0 | 6 | 0 | 6 | 0 | 2 | 0 | 1 | 3 | 0 | 0 | 2 | 2 | 2 | 12 | 13 | 1 | 12 | 5 | 9 | 0 | 11 | 5 | 8 | 2 | 1 | 1 | 3 |
850 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/big_bird/modeling_big_bird.py | transformers.models.big_bird.modeling_big_bird.BigBirdLMPredictionHead | from torch import nn
import torch
class BigBirdLMPredictionHead(nn.Module):
def __init__(self, config):
super().__init__()
self.transform = BigBirdPredictionHeadTransform(config)
self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.bias = nn.Parameter(to... |
class BigBirdLMPredictionHead(nn.Module):
def __init__(self, config):
pass
def _tie_weights(self):
pass
def forward(self, hidden_states):
pass | 4 | 0 | 6 | 1 | 4 | 1 | 1 | 0.23 | 1 | 2 | 1 | 0 | 3 | 3 | 3 | 13 | 21 | 5 | 13 | 7 | 9 | 3 | 13 | 7 | 9 | 1 | 1 | 0 | 3 |
851 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/big_bird/modeling_big_bird.py | transformers.models.big_bird.modeling_big_bird.BigBirdLayer | from ...modeling_layers import GradientCheckpointingLayer
from ...utils.deprecation import deprecate_kwarg
from ...pytorch_utils import apply_chunking_to_forward
class BigBirdLayer(GradientCheckpointingLayer):
def __init__(self, config, seed=None):
super().__init__()
self.config = config
s... |
class BigBirdLayer(GradientCheckpointingLayer):
def __init__(self, config, seed=None):
pass
def set_attention_type(self, value: str, layer_idx=None):
pass
@deprecate_kwarg('past_key_value', new_name='past_key_values', version='4.58')
def forward(self, hidden_states, attention_mask=Non... | 6 | 0 | 27 | 2 | 23 | 2 | 4 | 0.09 | 1 | 7 | 3 | 0 | 4 | 10 | 4 | 14 | 112 | 12 | 94 | 39 | 76 | 8 | 52 | 26 | 47 | 7 | 1 | 2 | 15 |
852 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/big_bird/modeling_big_bird.py | transformers.models.big_bird.modeling_big_bird.BigBirdModel | from ...cache_utils import Cache, DynamicCache
from ...utils import ModelOutput, auto_docstring, logging
from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, BaseModelOutputWithPoolingAndCrossAttentions, CausalLMOutputWithCrossAttentions, MaskedLMOutput, MultipleChoiceModelOutput, SequenceClassifi... | @auto_docstring
class BigBirdModel(BigBirdPreTrainedModel):
'''
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](htt... | 12 | 4 | 42 | 4 | 29 | 8 | 5 | 0.27 | 1 | 9 | 3 | 0 | 6 | 7 | 7 | 8 | 338 | 42 | 236 | 73 | 196 | 63 | 118 | 47 | 109 | 24 | 2 | 2 | 41 |
853 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/big_bird/modeling_big_bird.py | transformers.models.big_bird.modeling_big_bird.BigBirdOnlyMLMHead | from torch import nn
import torch
class BigBirdOnlyMLMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = BigBirdLMPredictionHead(config)
def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
prediction_scores = self.predictions(sequence_outp... |
class BigBirdOnlyMLMHead(nn.Module):
def __init__(self, config):
pass
def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
pass | 3 | 0 | 3 | 0 | 3 | 0 | 1 | 0 | 1 | 3 | 1 | 0 | 2 | 1 | 2 | 12 | 8 | 1 | 7 | 5 | 4 | 0 | 7 | 5 | 4 | 1 | 1 | 0 | 2 |
854 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/big_bird/modeling_big_bird.py | transformers.models.big_bird.modeling_big_bird.BigBirdOnlyNSPHead | from torch import nn
class BigBirdOnlyNSPHead(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_relation... |
class BigBirdOnlyNSPHead(nn.Module):
def __init__(self, config):
pass
def forward(self, pooled_output):
pass | 3 | 0 | 3 | 0 | 3 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 2 | 1 | 2 | 12 | 8 | 1 | 7 | 5 | 4 | 0 | 7 | 5 | 4 | 1 | 1 | 0 | 2 |
855 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/big_bird/modeling_big_bird.py | transformers.models.big_bird.modeling_big_bird.BigBirdOutput | import torch
from torch import nn
class BigBirdOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropo... |
class BigBirdOutput(nn.Module):
def __init__(self, config):
pass
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
pass | 3 | 0 | 5 | 0 | 5 | 0 | 1 | 0 | 1 | 2 | 0 | 0 | 2 | 3 | 2 | 12 | 12 | 1 | 11 | 6 | 8 | 0 | 11 | 6 | 8 | 1 | 1 | 0 | 2 |
856 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/big_bird/modeling_big_bird.py | transformers.models.big_bird.modeling_big_bird.BigBirdPreTrainedModel | from ...utils import ModelOutput, auto_docstring, logging
from torch import nn
from ...modeling_utils import PreTrainedModel
from .configuration_big_bird import BigBirdConfig
@auto_docstring
class BigBirdPreTrainedModel(PreTrainedModel):
config: BigBirdConfig
base_model_prefix = 'bert'
supports_gradient_ch... | @auto_docstring
class BigBirdPreTrainedModel(PreTrainedModel):
def _init_weights(self, module):
'''Initialize the weights'''
pass | 3 | 1 | 15 | 0 | 12 | 3 | 6 | 0.41 | 1 | 0 | 0 | 8 | 1 | 0 | 1 | 1 | 26 | 2 | 17 | 6 | 15 | 7 | 15 | 6 | 13 | 6 | 1 | 2 | 6 |
857 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/big_bird/modeling_big_bird.py | transformers.models.big_bird.modeling_big_bird.BigBirdPreTrainingHeads | from torch import nn
class BigBirdPreTrainingHeads(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = BigBirdLMPredictionHead(config)
self.seq_relationship = nn.Linear(config.hidden_size, 2)
def forward(self, sequence_output, pooled_output):
predicti... |
class BigBirdPreTrainingHeads(nn.Module):
def __init__(self, config):
pass
def forward(self, sequence_output, pooled_output):
pass | 3 | 0 | 4 | 0 | 4 | 0 | 1 | 0 | 1 | 2 | 1 | 0 | 2 | 2 | 2 | 12 | 10 | 1 | 9 | 7 | 6 | 0 | 9 | 7 | 6 | 1 | 1 | 0 | 2 |
858 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/big_bird/modeling_big_bird.py | transformers.models.big_bird.modeling_big_bird.BigBirdPredictionHeadTransform | from torch import nn
from ...activations import ACT2FN
import torch
class BigBirdPredictionHeadTransform(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.transfo... |
class BigBirdPredictionHeadTransform(nn.Module):
def __init__(self, config):
pass
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
pass | 3 | 0 | 7 | 0 | 7 | 0 | 2 | 0 | 1 | 3 | 0 | 0 | 2 | 3 | 2 | 12 | 15 | 1 | 14 | 6 | 11 | 0 | 13 | 6 | 10 | 2 | 1 | 1 | 3 |
859 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/big_bird/modeling_big_bird.py | transformers.models.big_bird.modeling_big_bird.BigBirdSelfAttention | from torch import nn
import math
from ...utils.deprecation import deprecate_kwarg
import torch
class BigBirdSelfAttention(nn.Module):
def __init__(self, config, layer_idx=None):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and (not hasattr(config, 'embedding_size')):
... |
class BigBirdSelfAttention(nn.Module):
def __init__(self, config, layer_idx=None):
pass
@deprecate_kwarg('past_key_value', new_name='past_key_values', version='4.58')
def forward(self, hidden_states, attention_mask=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, past... | 4 | 0 | 33 | 5 | 22 | 6 | 4 | 0.25 | 1 | 3 | 0 | 0 | 3 | 8 | 3 | 13 | 102 | 18 | 67 | 32 | 54 | 17 | 52 | 23 | 48 | 9 | 1 | 1 | 12 |
860 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/big_bird/modeling_big_bird.py | transformers.models.big_bird.modeling_big_bird.BigBirdSelfOutput | from torch import nn
import torch
class BigBirdSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout... |
class BigBirdSelfOutput(nn.Module):
def __init__(self, config):
pass
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
pass | 3 | 0 | 5 | 0 | 5 | 0 | 1 | 0 | 1 | 2 | 0 | 0 | 2 | 3 | 2 | 12 | 12 | 1 | 11 | 6 | 8 | 0 | 11 | 6 | 8 | 1 | 1 | 0 | 2 |
861 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/big_bird/tokenization_big_bird.py | transformers.models.big_bird.tokenization_big_bird.BigBirdTokenizer | import re
from typing import Any, Optional
from shutil import copyfile
import os
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils.import_utils import requires
@requires(backends=('sentencepiece',))
class BigBirdTokenizer(PreTrainedTokenizer):
"""
Const... | @requires(backends=('sentencepiece',))
class BigBirdTokenizer(PreTrainedTokenizer):
'''
Construct a BigBird tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
th... | 16 | 7 | 16 | 2 | 11 | 4 | 3 | 0.58 | 1 | 6 | 0 | 0 | 14 | 5 | 14 | 103 | 289 | 41 | 157 | 69 | 116 | 91 | 108 | 42 | 93 | 9 | 3 | 3 | 42 |
862 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/big_bird/tokenization_big_bird_fast.py | transformers.models.big_bird.tokenization_big_bird_fast.BigBirdTokenizerFast | from typing import Optional
from ...tokenization_utils_fast import PreTrainedTokenizerFast
import os
from shutil import copyfile
from ...tokenization_utils import AddedToken
class BigBirdTokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "fast" BigBird tokenizer (backed by HuggingFace's *tokenizers* libra... |
class BigBirdTokenizerFast(PreTrainedTokenizerFast):
'''
Construct a "fast" BigBird tokenizer (backed by HuggingFace's *tokenizers* library). Based on
[Unigram](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=unigram#models). This
tokenizer inherits from [`PreTrainedToken... | 5 | 3 | 23 | 3 | 13 | 7 | 4 | 0.91 | 1 | 5 | 0 | 0 | 6 | 1 | 6 | 94 | 192 | 30 | 85 | 36 | 59 | 77 | 47 | 17 | 40 | 8 | 3 | 2 | 24 |
863 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bigbird_pegasus/configuration_bigbird_pegasus.py | transformers.models.bigbird_pegasus.configuration_bigbird_pegasus.BigBirdPegasusConfig | from ...configuration_utils import PretrainedConfig
class BigBirdPegasusConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`BigBirdPegasusModel`]. It is used to instantiate
an BigBirdPegasus model according to the specified arguments, defining the model architect... |
class BigBirdPegasusConfig(PretrainedConfig):
'''
This is the configuration class to store the configuration of a [`BigBirdPegasusModel`]. It is used to instantiate
an BigBirdPegasus model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defa... | 2 | 1 | 67 | 2 | 64 | 3 | 1 | 1.03 | 1 | 1 | 0 | 0 | 1 | 24 | 1 | 1 | 156 | 12 | 72 | 60 | 39 | 74 | 30 | 29 | 28 | 1 | 1 | 0 | 1 |
864 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bigbird_pegasus/configuration_bigbird_pegasus.py | transformers.models.bigbird_pegasus.configuration_bigbird_pegasus.BigBirdPegasusOnnxConfig | from collections.abc import Mapping
from collections import OrderedDict
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeq2SeqConfigWithPast
from typing import Any
from ...utils import is_torch_available, logging
from ... import PreTrainedTokenizer
from ...onnx.utils import compute_effective_axis_dimension
cl... |
class BigBirdPegasusOnnxConfig(OnnxSeq2SeqConfigWithPast):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
pass
@property
def outputs(self) -> Mapping[str, Mapping[int, str]]:
pass
def _generate_dummy_inputs_for_default_and_seq2seq_lm(self, tokenizer: PreTrainedToke... | 10 | 0 | 30 | 2 | 27 | 1 | 4 | 0.05 | 1 | 9 | 0 | 0 | 7 | 1 | 7 | 7 | 221 | 20 | 191 | 73 | 151 | 10 | 89 | 42 | 79 | 8 | 1 | 3 | 28 |
865 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py | transformers.models.bigbird_pegasus.modeling_bigbird_pegasus.BigBirdPegasusBlockSparseAttention | from torch import nn
import torch
import numpy as np
import math
class BigBirdPegasusBlockSparseAttention(nn.Module):
def __init__(self, config, seed=None):
super().__init__()
self.max_seqlen = config.max_position_embeddings
self.seed = seed
if config.hidden_size % config.num_atten... |
class BigBirdPegasusBlockSparseAttention(nn.Module):
def __init__(self, config, seed=None):
pass
def forward(self, hidden_states, band_mask=None, from_mask=None, to_mask=None, from_blocked_mask=None, to_blocked_mask=None, output_attentions=None):
pass
@staticmethod
def torch_bmm_nd(in... | 18 | 7 | 72 | 8 | 48 | 19 | 5 | 0.39 | 1 | 7 | 0 | 0 | 6 | 10 | 12 | 22 | 886 | 108 | 582 | 189 | 497 | 225 | 274 | 115 | 261 | 20 | 1 | 5 | 65 |
866 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py | transformers.models.bigbird_pegasus.modeling_bigbird_pegasus.BigBirdPegasusClassificationHead | import torch
from torch import nn
class BigBirdPegasusClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, input_dim: int, inner_dim: int, num_classes: int, pooler_dropout: float):
super().__init__()
self.dense = nn.Linear(input_dim, inner_dim)
... |
class BigBirdPegasusClassificationHead(nn.Module):
'''Head for sentence-level classification tasks.'''
def __init__(self, input_dim: int, inner_dim: int, num_classes: int, pooler_dropout: float):
pass
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
pass | 3 | 1 | 9 | 0 | 9 | 0 | 1 | 0.05 | 1 | 4 | 0 | 0 | 2 | 3 | 2 | 12 | 22 | 2 | 19 | 12 | 10 | 1 | 13 | 6 | 10 | 1 | 1 | 0 | 2 |
867 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py | transformers.models.bigbird_pegasus.modeling_bigbird_pegasus.BigBirdPegasusDecoder | from ...utils import auto_docstring, is_torch_flex_attn_available, is_torchdynamo_compiling, logging
from typing import Callable, Optional, Union
import math
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, Seq2SeqLMOutput, Seq2SeqModelOutput... |
class BigBirdPegasusDecoder(BigBirdPegasusPreTrainedModel):
'''
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`BigBirdPegasusDecoderLayer`]
Args:
config: BigBirdPegasusConfig
embed_tokens (nn.Embedding): output embedding
'''
def __init__(self, c... | 3 | 2 | 61 | 9 | 36 | 16 | 11 | 0.48 | 1 | 13 | 5 | 0 | 4 | 9 | 4 | 6 | 254 | 40 | 145 | 43 | 126 | 69 | 78 | 29 | 73 | 37 | 2 | 3 | 42 |
868 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py | transformers.models.bigbird_pegasus.modeling_bigbird_pegasus.BigBirdPegasusDecoderAttention | from ...modeling_flash_attention_utils import FlashAttentionKwargs
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from typing import Callable, Optional, Union
from ...utils.deprecation import deprecate_kwarg
from ...processing_utils import Unpack
from torch import nn
from .configuration_bigbird_... |
class BigBirdPegasusDecoderAttention(nn.Module):
'''Multi-headed attention from 'Attention Is All You Need' paper'''
def __init__(self, embed_dim: int, num_heads: int, dropout: float=0.0, is_decoder: bool=False, bias: bool=True, is_causal: bool=False, config: Optional[BigBirdPegasusConfig]=None, layer_idx: Op... | 4 | 2 | 50 | 7 | 35 | 8 | 5 | 0.24 | 1 | 7 | 1 | 0 | 3 | 12 | 3 | 13 | 156 | 23 | 107 | 44 | 86 | 26 | 68 | 27 | 64 | 12 | 1 | 2 | 15 |
869 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py | transformers.models.bigbird_pegasus.modeling_bigbird_pegasus.BigBirdPegasusDecoderLayer | from torch import nn
from .configuration_bigbird_pegasus import BigBirdPegasusConfig
from ...modeling_layers import GradientCheckpointingLayer
import torch
from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
from ...activations import ACT2FN
from typing import Callable, Optional, Union
class BigBirdPeg... |
class BigBirdPegasusDecoderLayer(GradientCheckpointingLayer):
def __init__(self, config: BigBirdPegasusConfig, layer_idx: Optional[int]=None):
pass
def forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor]=None, encoder_hidden_states: Optional[torch.Tensor]=None, encoder_a... | 3 | 1 | 57 | 5 | 40 | 13 | 4 | 0.33 | 1 | 5 | 2 | 0 | 2 | 11 | 2 | 12 | 117 | 11 | 80 | 32 | 66 | 26 | 44 | 21 | 41 | 6 | 1 | 1 | 7 |
870 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py | transformers.models.bigbird_pegasus.modeling_bigbird_pegasus.BigBirdPegasusDecoderWrapper | class BigBirdPegasusDecoderWrapper(BigBirdPegasusPreTrainedModel):
"""
This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is
used in combination with the [`EncoderDecoderModel`] framework.
"""
def __init__(self, config):
super().__in... | class BigBirdPegasusDecoderWrapper(BigBirdPegasusPreTrainedModel):
'''
This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is
used in combination with the [`EncoderDecoderModel`] framework.
'''
def __init__(self, config):
pass
de... | 3 | 1 | 3 | 0 | 3 | 0 | 1 | 0.67 | 1 | 2 | 1 | 0 | 2 | 1 | 2 | 4 | 12 | 2 | 6 | 4 | 3 | 4 | 6 | 4 | 3 | 1 | 2 | 0 | 2 |
871 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py | transformers.models.bigbird_pegasus.modeling_bigbird_pegasus.BigBirdPegasusEncoder | from ...modeling_attn_mask_utils import AttentionMaskConverter, _prepare_4d_attention_mask, _prepare_4d_attention_mask_for_sdpa
from typing import Callable, Optional, Union
import math
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, Seq2SeqL... |
class BigBirdPegasusEncoder(BigBirdPegasusPreTrainedModel):
'''
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`BigBirdPegasusEncoderLayer`].
Args:
config: BigBirdPegasusConfig
embed_tokens (nn.Embedding): output embedding
'''
... | 8 | 4 | 51 | 7 | 33 | 11 | 7 | 0.32 | 1 | 14 | 5 | 0 | 4 | 12 | 5 | 7 | 300 | 47 | 194 | 59 | 177 | 62 | 117 | 49 | 110 | 29 | 2 | 3 | 41 |
872 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py | transformers.models.bigbird_pegasus.modeling_bigbird_pegasus.BigBirdPegasusEncoderAttention | from torch import nn
class BigBirdPegasusEncoderAttention(nn.Module):
def __init__(self, config, seed=None):
super().__init__()
self.config = config
self.seed = seed
self.attention_type = config.attention_type
if self.attention_type == 'original_full':
self.self... |
class BigBirdPegasusEncoderAttention(nn.Module):
def __init__(self, config, seed=None):
pass
def set_attention_type(self, value: str):
pass
def forward(self, hidden_states, attention_mask=None, head_mask=None, output_attentions=False, band_mask=None, from_mask=None, to_mask=None, from_bl... | 4 | 0 | 25 | 3 | 21 | 2 | 4 | 0.08 | 1 | 5 | 2 | 0 | 3 | 5 | 3 | 13 | 77 | 10 | 63 | 25 | 47 | 5 | 35 | 13 | 31 | 5 | 1 | 1 | 11 |
873 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py | transformers.models.bigbird_pegasus.modeling_bigbird_pegasus.BigBirdPegasusEncoderLayer | from ...activations import ACT2FN
from torch import nn
from .configuration_bigbird_pegasus import BigBirdPegasusConfig
from ...modeling_layers import GradientCheckpointingLayer
import torch
class BigBirdPegasusEncoderLayer(GradientCheckpointingLayer):
def __init__(self, config: BigBirdPegasusConfig, seed=None):
... |
class BigBirdPegasusEncoderLayer(GradientCheckpointingLayer):
def __init__(self, config: BigBirdPegasusConfig, seed=None):
pass
def forward(self, hidden_states: torch.Tensor, attention_mask: torch.Tensor, layer_head_mask: torch.Tensor, band_mask=None, from_mask=None, to_mask=None, from_blocked_mask=N... | 4 | 1 | 27 | 3 | 21 | 3 | 2 | 0.15 | 1 | 7 | 2 | 0 | 3 | 10 | 3 | 13 | 85 | 10 | 65 | 29 | 50 | 10 | 40 | 18 | 36 | 3 | 1 | 1 | 7 |
874 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py | transformers.models.bigbird_pegasus.modeling_bigbird_pegasus.BigBirdPegasusForCausalLM | from torch import nn
import torch
from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
from ...utils import auto_docstring, is_torch_flex_attn_available, is_torchdynamo_compiling, logging
from typing import Callable, Optional, Union
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from .... |
class BigBirdPegasusForCausalLM(BigBirdPegasusPreTrainedModel, GenerationMixin):
def __init__(self, config):
pass
def get_input_embeddings(self):
pass
def set_input_embeddings(self, value):
pass
def set_decoder(self, decoder):
pass
def get_decoder(self):
... | 8 | 1 | 19 | 3 | 9 | 8 | 2 | 0.84 | 2 | 6 | 2 | 0 | 8 | 2 | 9 | 11 | 184 | 33 | 82 | 37 | 55 | 69 | 41 | 20 | 31 | 7 | 2 | 1 | 16 |
875 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py | transformers.models.bigbird_pegasus.modeling_bigbird_pegasus.BigBirdPegasusForConditionalGeneration | from ...utils import auto_docstring, is_torch_flex_attn_available, is_torchdynamo_compiling, logging
from typing import Callable, Optional, Union
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...generation import GenerationMixin
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWi... | @auto_docstring(custom_intro='\n The BigBirdPegasus Model with a language modeling head. Can be used for summarization.\n ')
class BigBirdPegasusForConditionalGeneration(BigBirdPegasusPreTrainedModel, GenerationMixin):
def __init__(self, config: BigBirdPegasusConfig):
pass
def get_encoder(self):... | 11 | 1 | 12 | 1 | 10 | 1 | 2 | 0.08 | 2 | 8 | 3 | 0 | 9 | 3 | 10 | 12 | 139 | 18 | 112 | 50 | 77 | 9 | 56 | 27 | 45 | 8 | 2 | 2 | 19 |
876 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py | transformers.models.bigbird_pegasus.modeling_bigbird_pegasus.BigBirdPegasusForQuestionAnswering | from torch import nn
import torch
from ...utils import auto_docstring, is_torch_flex_attn_available, is_torchdynamo_compiling, logging
from typing import Callable, Optional, Union
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPastAn... | @auto_docstring
class BigBirdPegasusForQuestionAnswering(BigBirdPegasusPreTrainedModel):
def __init__(self, config):
pass
@auto_docstring
def forward(self, input_ids: Optional[torch.Tensor]=None, attention_mask: Optional[torch.Tensor]=None, decoder_input_ids: Optional[torch.LongTensor]=None, decode... | 5 | 1 | 52 | 5 | 41 | 7 | 5 | 0.16 | 1 | 5 | 2 | 0 | 2 | 3 | 2 | 4 | 115 | 12 | 89 | 36 | 62 | 14 | 36 | 17 | 33 | 8 | 2 | 2 | 9 |
877 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py | transformers.models.bigbird_pegasus.modeling_bigbird_pegasus.BigBirdPegasusForSequenceClassification | from ...utils import auto_docstring, is_torch_flex_attn_available, is_torchdynamo_compiling, logging
from typing import Callable, Optional, Union
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWi... | @auto_docstring(custom_intro='\n BigBirdPegasus model with a sequence classification/head on top (a linear layer on top of the pooled output) e.g.\n for GLUE tasks.\n ')
class BigBirdPegasusForSequenceClassification(BigBirdPegasusPreTrainedModel):
def __init__(self, config: BigBirdPegasusConfig, **kwargs)... | 5 | 1 | 55 | 4 | 48 | 4 | 8 | 0.08 | 1 | 10 | 4 | 0 | 2 | 2 | 2 | 4 | 120 | 10 | 103 | 32 | 77 | 8 | 41 | 14 | 38 | 15 | 2 | 3 | 16 |
878 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py | transformers.models.bigbird_pegasus.modeling_bigbird_pegasus.BigBirdPegasusLearnedPositionalEmbedding | from typing import Callable, Optional, Union
from torch import nn
import torch
class BigBirdPegasusLearnedPositionalEmbedding(nn.Embedding):
"""
This module learns positional embeddings up to a fixed maximum size.
"""
def __init__(self, num_embeddings: int, embedding_dim: int):
super().__init_... |
class BigBirdPegasusLearnedPositionalEmbedding(nn.Embedding):
'''
This module learns positional embeddings up to a fixed maximum size.
'''
def __init__(self, num_embeddings: int, embedding_dim: int):
pass
def forward(self, input_ids_shape: torch.Size, past_key_values_length: int=0, positi... | 3 | 2 | 5 | 0 | 4 | 1 | 1 | 0.44 | 1 | 2 | 0 | 0 | 2 | 0 | 2 | 2 | 15 | 2 | 9 | 5 | 6 | 4 | 7 | 5 | 4 | 1 | 1 | 0 | 2 |
879 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py | transformers.models.bigbird_pegasus.modeling_bigbird_pegasus.BigBirdPegasusModel | from ...utils import auto_docstring, is_torch_flex_attn_available, is_torchdynamo_compiling, logging
from typing import Callable, Optional, Union
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, Seq2SeqLMOutput, Seq2SeqModelOutput, Seq2SeqQue... | @auto_docstring
class BigBirdPegasusModel(BigBirdPegasusPreTrainedModel):
def __init__(self, config: BigBirdPegasusConfig):
pass
def get_input_embeddings(self):
pass
def set_input_embeddings(self, value):
pass
def _tie_weights(self):
pass
def get_encoder(self):
... | 9 | 1 | 16 | 1 | 14 | 1 | 3 | 0.06 | 1 | 10 | 6 | 0 | 7 | 3 | 7 | 9 | 131 | 16 | 109 | 33 | 77 | 6 | 40 | 15 | 32 | 12 | 2 | 2 | 20 |
880 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py | transformers.models.bigbird_pegasus.modeling_bigbird_pegasus.BigBirdPegasusPreTrainedModel | from torch import nn
from .configuration_bigbird_pegasus import BigBirdPegasusConfig
import torch
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
from ...modeling_attn_mask_utils import AttentionMaskConverter, _prepare_4d_attenti... | @auto_docstring
class BigBirdPegasusPreTrainedModel(PreTrainedModel):
def _init_weights(self, module):
pass
@property
def dummy_inputs(self):
pass
def _update_causal_mask(self, attention_mask: Optional[Union[torch.Tensor, 'BlockMask']], input_tensor: torch.Tensor, cache_position: torch... | 9 | 1 | 9 | 0 | 9 | 0 | 3 | 0 | 1 | 0 | 0 | 8 | 2 | 0 | 2 | 2 | 28 | 2 | 26 | 14 | 22 | 0 | 21 | 13 | 18 | 5 | 1 | 2 | 6 |
881 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py | transformers.models.bigbird_pegasus.modeling_bigbird_pegasus.BigBirdPegasusScaledWordEmbedding | import torch
from torch import nn
from typing import Callable, Optional, Union
class BigBirdPegasusScaledWordEmbedding(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,... |
class BigBirdPegasusScaledWordEmbedding(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):
pass
def forward(self, input_i... | 3 | 1 | 3 | 0 | 3 | 0 | 1 | 0.5 | 1 | 4 | 0 | 0 | 2 | 1 | 2 | 2 | 11 | 2 | 6 | 4 | 3 | 3 | 6 | 4 | 3 | 1 | 1 | 0 | 2 |
882 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py | transformers.models.bigbird_pegasus.modeling_bigbird_pegasus.BigBirdPegasusSelfAttention | from torch import nn
from ...utils.deprecation import deprecate_kwarg
import math
import torch
class BigBirdPegasusSelfAttention(nn.Module):
def __init__(self, config, layer_idx=None):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and (not hasattr(config, 'embedding_si... |
class BigBirdPegasusSelfAttention(nn.Module):
def __init__(self, config, layer_idx=None):
pass
@deprecate_kwarg('past_key_value', new_name='past_key_values', version='4.58')
def forward(self, hidden_states, attention_mask=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=Non... | 4 | 0 | 33 | 5 | 22 | 6 | 4 | 0.25 | 1 | 3 | 0 | 0 | 3 | 8 | 3 | 13 | 102 | 18 | 67 | 32 | 54 | 17 | 52 | 23 | 48 | 9 | 1 | 1 | 12 |
883 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/biogpt/configuration_biogpt.py | transformers.models.biogpt.configuration_biogpt.BioGptConfig | from ...configuration_utils import PretrainedConfig
class BioGptConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`BioGptModel`]. It is used to instantiate an
BioGPT model according to the specified arguments, defining the model architecture. Instantiating a con... |
class BioGptConfig(PretrainedConfig):
'''
This is the configuration class to store the configuration of a [`BioGptModel`]. It is used to instantiate an
BioGPT model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a simila... | 2 | 1 | 38 | 0 | 38 | 0 | 1 | 1.45 | 1 | 1 | 0 | 0 | 1 | 15 | 1 | 1 | 108 | 10 | 40 | 39 | 17 | 58 | 19 | 18 | 17 | 1 | 1 | 0 | 1 |
884 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/biogpt/convert_biogpt_original_pytorch_checkpoint_to_pytorch.py | transformers.models.biogpt.convert_biogpt_original_pytorch_checkpoint_to_pytorch.Dictionary | class Dictionary:
"""A mapping from symbols to consecutive integers"""
def __init__(self, *, bos='<s>', pad='<pad>', eos='</s>', unk='<unk>', extra_special_symbols=None):
self.bos_word, self.unk_word, self.pad_word, self.eos_word = (bos, unk, pad, eos)
self.symbols = []
self.count = []
... | class Dictionary:
'''A mapping from symbols to consecutive integers'''
def __init__(self, *, bos='<s>', pad='<pad>', eos='</s>', unk='<unk>', extra_special_symbols=None):
pass
def __eq__(self, other):
pass
def __getitem__(self, idx):
pass
def __len__(self):
'''Ret... | 11 | 5 | 11 | 0 | 9 | 2 | 2 | 0.19 | 0 | 7 | 0 | 0 | 8 | 12 | 9 | 9 | 108 | 12 | 83 | 40 | 64 | 16 | 66 | 29 | 56 | 8 | 0 | 3 | 20 |
885 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/biogpt/modeling_biogpt.py | transformers.models.biogpt.modeling_biogpt.BioGptAttention | import torch.nn as nn
import torch
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from typing import Callable, Optional, Union
from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
from .configuration_biogpt import BioGptConfig
from ...modeling_flash_attention_utils import FlashAtt... |
class BioGptAttention(nn.Module):
'''Multi-headed attention from 'Attention Is All You Need' paper'''
def __init__(self, embed_dim: int, num_heads: int, dropout: float=0.0, is_decoder: bool=False, bias: bool=True, is_causal: bool=False, config: Optional[BioGptConfig]=None, layer_idx: Optional[int]=None):
... | 4 | 2 | 50 | 7 | 35 | 8 | 5 | 0.24 | 1 | 7 | 1 | 1 | 3 | 12 | 3 | 13 | 156 | 23 | 107 | 44 | 86 | 26 | 68 | 27 | 64 | 12 | 1 | 2 | 15 |
886 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/biogpt/modeling_biogpt.py | transformers.models.biogpt.modeling_biogpt.BioGptDecoderLayer | from ...processing_utils import Unpack
from typing import Callable, Optional, Union
from ...modeling_layers import GradientCheckpointingLayer
import torch.nn as nn
from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
from ...activations import ACT2FN
from .configuration_biogpt import BioGptConfig
import ... |
class BioGptDecoderLayer(GradientCheckpointingLayer):
def __init__(self, config: BioGptConfig, layer_idx: Optional[int]=None):
pass
@deprecate_kwarg('past_key_value', new_name='past_key_values', version='4.58')
def forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor]=N... | 4 | 1 | 41 | 5 | 26 | 10 | 3 | 0.36 | 1 | 4 | 1 | 0 | 2 | 9 | 2 | 12 | 83 | 11 | 53 | 24 | 42 | 19 | 33 | 16 | 30 | 4 | 1 | 1 | 5 |
887 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/biogpt/modeling_biogpt.py | transformers.models.biogpt.modeling_biogpt.BioGptForCausalLM | import torch
import torch.nn as nn
from ...processing_utils import Unpack
from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
from ...generation import GenerationMixin
from typing import Callable, Optional, Union
from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWi... | @auto_docstring(custom_intro='\n BioGPT Model with a `language modeling` head on top for CLM fine-tuning.\n ')
class BioGptForCausalLM(BioGptPreTrainedModel, GenerationMixin):
def __init__(self, config):
pass
def get_output_embeddings(self):
pass
def set_output_embeddings(self, new_... | 7 | 1 | 15 | 1 | 13 | 1 | 2 | 0.1 | 2 | 6 | 2 | 0 | 4 | 2 | 5 | 6 | 91 | 12 | 72 | 31 | 46 | 7 | 28 | 16 | 22 | 5 | 2 | 1 | 10 |
888 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/biogpt/modeling_biogpt.py | transformers.models.biogpt.modeling_biogpt.BioGptForSequenceClassification | from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, SequenceClassifierOutputWithPast, TokenClassifierOutput
import torch.nn as nn
from typing import Callable, Optional, Union
from .configuration... | null | 7 | 1 | 25 | 2 | 21 | 2 | 5 | 0.08 | 1 | 7 | 3 | 0 | 4 | 3 | 4 | 5 | 109 | 11 | 91 | 29 | 68 | 7 | 46 | 16 | 41 | 15 | 2 | 3 | 18 |
889 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/biogpt/modeling_biogpt.py | transformers.models.biogpt.modeling_biogpt.BioGptForTokenClassification | from typing import Callable, Optional, Union
from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
from ...utils import TransformersKwargs, auto_docstring, is_torch_flex_attn_available, logging
from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, Se... | @auto_docstring
class BioGptForTokenClassification(BioGptPreTrainedModel):
def __init__(self, config):
pass
@auto_docstring
def forward(self, input_ids: Optional[torch.LongTensor]=None, token_type_ids: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.FloatTensor]=None, head_mask: Opt... | 5 | 1 | 38 | 4 | 31 | 4 | 4 | 0.1 | 1 | 5 | 2 | 0 | 2 | 4 | 2 | 3 | 83 | 8 | 68 | 31 | 46 | 7 | 30 | 17 | 27 | 6 | 2 | 2 | 8 |
890 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/biogpt/modeling_biogpt.py | transformers.models.biogpt.modeling_biogpt.BioGptLearnedPositionalEmbedding | import torch
import torch.nn as nn
from typing import Callable, Optional, Union
class BioGptLearnedPositionalEmbedding(nn.Embedding):
"""
This module learns positional embeddings up to a fixed maximum size.
"""
def __init__(self, num_embeddings: int, embedding_dim: int):
self.offset = 2
... |
class BioGptLearnedPositionalEmbedding(nn.Embedding):
'''
This module learns positional embeddings up to a fixed maximum size.
'''
def __init__(self, num_embeddings: int, embedding_dim: int):
pass
def forward(self, attention_mask: torch.LongTensor, past_key_values_length: int=0, position_... | 3 | 2 | 8 | 2 | 4 | 3 | 1 | 0.89 | 1 | 2 | 0 | 0 | 2 | 1 | 2 | 2 | 22 | 5 | 9 | 5 | 6 | 8 | 9 | 5 | 6 | 1 | 1 | 0 | 2 |
891 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/biogpt/modeling_biogpt.py | transformers.models.biogpt.modeling_biogpt.BioGptModel | import torch
from ...utils import TransformersKwargs, auto_docstring, is_torch_flex_attn_available, logging
import torch.nn as nn
from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, SequenceClassifierOutputWithPast, TokenClassifierOutput
import math
from .config... | @auto_docstring
class BioGptModel(BioGptPreTrainedModel):
def __init__(self, config: BioGptConfig):
pass
@auto_docstring
def forward(self, input_ids: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.FloatTensor]=None, head_mask: Optional[torch.FloatTensor]=None, inputs_embeds: Option... | 5 | 0 | 41 | 6 | 34 | 2 | 9 | 0.06 | 1 | 12 | 5 | 0 | 4 | 11 | 4 | 5 | 175 | 26 | 141 | 43 | 118 | 9 | 75 | 30 | 70 | 32 | 2 | 3 | 36 |
892 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/biogpt/modeling_biogpt.py | transformers.models.biogpt.modeling_biogpt.BioGptPreTrainedModel | from typing import Callable, Optional, Union
import torch.nn as nn
from ...utils import TransformersKwargs, auto_docstring, is_torch_flex_attn_available, logging
from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
from .configuration_biogpt import BioGptConfig
from ...modeling_utils import ALL_ATTENTION... | @auto_docstring
class BioGptPreTrainedModel(PreTrainedModel):
def _update_causal_mask(self, attention_mask: Optional[Union[torch.Tensor, 'BlockMask']], input_tensor: torch.Tensor, cache_position: torch.Tensor, past_key_values: Cache):
pass
@staticmethod
def _prepare_4d_causal_attention_mask_with_ca... | 5 | 1 | 15 | 0 | 12 | 3 | 6 | 0.41 | 1 | 0 | 0 | 4 | 1 | 0 | 1 | 1 | 26 | 2 | 17 | 6 | 15 | 7 | 15 | 6 | 13 | 6 | 1 | 2 | 6 |
893 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/biogpt/modeling_biogpt.py | transformers.models.biogpt.modeling_biogpt.BioGptScaledWordEmbedding | import torch.nn as nn
from typing import Callable, Optional, Union
import torch
class BioGptScaledWordEmbedding(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_... |
class BioGptScaledWordEmbedding(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):
pass
def forward(self, input_ids: torc... | 3 | 1 | 3 | 0 | 3 | 0 | 1 | 0.5 | 1 | 4 | 0 | 0 | 2 | 1 | 2 | 2 | 11 | 2 | 6 | 4 | 3 | 3 | 6 | 4 | 3 | 1 | 1 | 0 | 2 |
894 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/biogpt/tokenization_biogpt.py | transformers.models.biogpt.tokenization_biogpt.BioGptTokenizer | from typing import Optional
import json
import os
from ...tokenization_utils import PreTrainedTokenizer
class BioGptTokenizer(PreTrainedTokenizer):
"""
Construct an FAIRSEQ Transformer tokenizer. Moses tokenization followed by Byte-Pair Encoding.
This tokenizer inherits from [`PreTrainedTokenizer`] which ... |
class BioGptTokenizer(PreTrainedTokenizer):
'''
Construct an FAIRSEQ Transformer tokenizer. Moses tokenization followed by Byte-Pair Encoding.
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regardi... | 17 | 8 | 16 | 1 | 11 | 3 | 3 | 0.47 | 1 | 12 | 0 | 0 | 16 | 9 | 16 | 105 | 313 | 50 | 179 | 71 | 143 | 84 | 135 | 50 | 116 | 10 | 3 | 3 | 41 |
895 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bit/configuration_bit.py | transformers.models.bit.configuration_bit.BitConfig | from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
from ...configuration_utils import PretrainedConfig
class BitConfig(BackboneConfigMixin, PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`BitModel`]. It is used to instant... |
class BitConfig(BackboneConfigMixin, PretrainedConfig):
'''
This is the configuration class to store the configuration of a [`BitModel`]. It is used to instantiate an BiT
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yi... | 2 | 1 | 43 | 1 | 42 | 0 | 4 | 1.17 | 2 | 3 | 0 | 0 | 1 | 15 | 1 | 6 | 109 | 9 | 46 | 36 | 27 | 54 | 26 | 19 | 24 | 4 | 1 | 2 | 4 |
896 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bit/image_processing_bit.py | transformers.models.bit.image_processing_bit.BitImageProcessor | from ...image_utils import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD, ChannelDimension, ImageInput, PILImageResampling, infer_channel_dimension_format, is_scaled_image, make_flat_list_of_images, to_numpy_array, valid_images, validate_preprocess_arguments
from ...image_processing_utils import BaseImageProcessor, BatchFeature, g... |
class BitImageProcessor(BaseImageProcessor):
'''
Constructs a BiT image processor.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by
`do_resize` in the `preprocess`... | 5 | 3 | 75 | 6 | 47 | 23 | 10 | 0.73 | 1 | 8 | 2 | 0 | 3 | 11 | 3 | 23 | 271 | 23 | 143 | 60 | 99 | 105 | 67 | 20 | 63 | 21 | 3 | 2 | 29 |
897 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bit/modeling_bit.py | transformers.models.bit.modeling_bit.BitBackbone | from ...modeling_outputs import BackboneOutput, BaseModelOutputWithNoAttention, BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...utils import auto_docstring, logging
from torch import Tensor, nn
from typing import Optional
from ...utils.backbone_utils import BackboneMixin
@auto_do... | @auto_docstring(custom_intro='\n BiT backbone, to be used with frameworks like DETR and MaskFormer.\n ')
class BitBackbone(BitPreTrainedModel, BackboneMixin):
def __init__(self, config):
pass
@auto_docstring
def forward(self, pixel_values: Tensor, output_hidden_states: Optional[bool]=None, re... | 5 | 1 | 29 | 6 | 15 | 8 | 5 | 0.5 | 2 | 6 | 2 | 0 | 2 | 2 | 2 | 15 | 61 | 13 | 32 | 13 | 25 | 16 | 22 | 10 | 19 | 8 | 2 | 2 | 9 |
898 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bit/modeling_bit.py | transformers.models.bit.modeling_bit.BitBottleneckLayer | from ...activations import ACT2FN
from torch import Tensor, nn
class BitBottleneckLayer(nn.Module):
"""Non Pre-activation bottleneck block, equivalent to V1.5/V1b bottleneck. Used for ViT Hybrid."""
def __init__(self, config, in_channels, out_channels=None, bottle_ratio=0.25, stride=1, dilation=1, first_dilat... |
class BitBottleneckLayer(nn.Module):
'''Non Pre-activation bottleneck block, equivalent to V1.5/V1b bottleneck. Used for ViT Hybrid.'''
def __init__(self, config, in_channels, out_channels=None, bottle_ratio=0.25, stride=1, dilation=1, first_dilation=None, groups=1, drop_path_rate=0.0, is_first_layer=False):
... | 3 | 1 | 34 | 4 | 29 | 1 | 3 | 0.05 | 1 | 5 | 4 | 0 | 2 | 9 | 2 | 12 | 71 | 10 | 58 | 26 | 43 | 3 | 30 | 14 | 27 | 3 | 1 | 1 | 5 |
899 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bit/modeling_bit.py | transformers.models.bit.modeling_bit.BitDownsampleConv | from torch import Tensor, nn
class BitDownsampleConv(nn.Module):
def __init__(self, config, in_channels, out_channels, stride=1, preact=True):
super().__init__()
self.conv = WeightStandardizedConv2d(in_channels, out_channels, 1, stride=stride, eps=1e-08, padding=config.global_padding)
self... |
class BitDownsampleConv(nn.Module):
def __init__(self, config, in_channels, out_channels, stride=1, preact=True):
pass
def forward(self, x):
pass | 3 | 0 | 10 | 0 | 10 | 0 | 2 | 0 | 1 | 3 | 2 | 0 | 2 | 2 | 2 | 12 | 21 | 1 | 20 | 12 | 10 | 0 | 7 | 5 | 4 | 2 | 1 | 0 | 3 |
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