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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700 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bamba/modular_bamba.py | transformers.models.bamba.modular_bamba.BambaForCausalLM | from transformers.models.jamba.modeling_jamba import HybridMambaAttentionDynamicCache, JambaAttentionDecoderLayer
import torch
from transformers.models.llama.modeling_llama import LlamaAttention, LlamaForCausalLM, LlamaMLP, LlamaRMSNorm, LlamaRotaryEmbedding, rotate_half
from typing import Optional, TypedDict, Union
fr... |
class BambaForCausalLM(LlamaForCausalLM):
def __init__(self, config):
pass
def forward(self, input_ids: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.Tensor]=None, position_ids: Optional[torch.LongTensor]=None, past_key_values: Optional[HybridMambaAttentionDynamicCache]=None, inputs... | 4 | 1 | 60 | 6 | 38 | 19 | 4 | 0.47 | 1 | 6 | 2 | 0 | 2 | 2 | 2 | 11 | 125 | 13 | 79 | 32 | 48 | 37 | 20 | 5 | 17 | 7 | 3 | 2 | 8 |
701 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bamba/modular_bamba.py | transformers.models.bamba.modular_bamba.BambaMLP | from transformers.models.llama.modeling_llama import LlamaAttention, LlamaForCausalLM, LlamaMLP, LlamaRMSNorm, LlamaRotaryEmbedding, rotate_half
class BambaMLP(LlamaMLP):
pass |
class BambaMLP(LlamaMLP):
pass | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 12 | 2 | 0 | 2 | 1 | 1 | 0 | 2 | 1 | 1 | 0 | 2 | 0 | 0 |
702 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bamba/modular_bamba.py | transformers.models.bamba.modular_bamba.BambaMixer | from transformers.models.jamba.modeling_jamba import HybridMambaAttentionDynamicCache, JambaAttentionDecoderLayer
import torch
from .configuration_bamba import BambaConfig
from transformers.activations import ACT2FN
from typing import Optional, TypedDict, Union
from torch import nn
from transformers.models.mamba2.model... |
class BambaMixer(nn.Module):
'''
Compute ∆, A, B, C, and D the state space parameters and compute the `contextualized_states`.
A, D are input independent (see Mamba paper [1] Section 3.5.2 "Interpretation of A" for why A isn't selective)
∆, B, C are input-dependent (this is a key difference between Mam... | 5 | 1 | 113 | 15 | 81 | 20 | 5 | 0.29 | 1 | 7 | 3 | 0 | 4 | 25 | 4 | 14 | 471 | 65 | 323 | 107 | 300 | 94 | 181 | 89 | 176 | 8 | 1 | 3 | 20 |
703 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bamba/modular_bamba.py | transformers.models.bamba.modular_bamba.BambaModel | import torch
from .configuration_bamba import BambaConfig
from ...modeling_attn_mask_utils import AttentionMaskConverter
from ...utils import auto_docstring, can_return_tuple, logging
from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
from torch import nn
from typing import Optional, TypedD... | @auto_docstring
class BambaModel(BambaPreTrainedModel):
def __init__(self, config: BambaConfig):
pass
@can_return_tuple
@auto_docstring
def forward(self, input_ids: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.Tensor]=None, position_ids: Optional[torch.LongTensor]=None, past_... | 10 | 2 | 38 | 4 | 28 | 6 | 6 | 0.24 | 1 | 14 | 7 | 0 | 6 | 8 | 7 | 137 | 279 | 38 | 196 | 71 | 157 | 47 | 102 | 40 | 94 | 23 | 3 | 3 | 41 |
704 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bamba/modular_bamba.py | transformers.models.bamba.modular_bamba.BambaPreTrainedModel | import torch
from ...modeling_utils import PreTrainedModel
from .configuration_bamba import BambaConfig
from ...utils import auto_docstring, can_return_tuple, logging
@auto_docstring
class BambaPreTrainedModel(PreTrainedModel):
config: BambaConfig
base_model_prefix = 'model'
supports_gradient_checkpointing... | @auto_docstring
class BambaPreTrainedModel(PreTrainedModel):
def _init_weights(self, module):
pass | 3 | 0 | 10 | 0 | 10 | 0 | 5 | 0.05 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 130 | 21 | 1 | 20 | 12 | 18 | 1 | 19 | 12 | 17 | 5 | 2 | 2 | 5 |
705 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bamba/modular_bamba.py | transformers.models.bamba.modular_bamba.BambaRMSNorm | from transformers.models.llama.modeling_llama import LlamaAttention, LlamaForCausalLM, LlamaMLP, LlamaRMSNorm, LlamaRotaryEmbedding, rotate_half
class BambaRMSNorm(LlamaRMSNorm):
pass |
class BambaRMSNorm(LlamaRMSNorm):
pass | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 13 | 2 | 0 | 2 | 1 | 1 | 0 | 2 | 1 | 1 | 0 | 2 | 0 | 0 |
706 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bamba/modular_bamba.py | transformers.models.bamba.modular_bamba.BambaRMSNormGated | from transformers.models.mamba2.modeling_mamba2 import MambaRMSNormGated, pad_tensor_by_size, reshape_into_chunks, segment_sum
class BambaRMSNormGated(MambaRMSNormGated):
pass |
class BambaRMSNormGated(MambaRMSNormGated):
pass | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 12 | 2 | 0 | 2 | 1 | 1 | 0 | 2 | 1 | 1 | 0 | 2 | 0 | 0 |
707 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bamba/modular_bamba.py | transformers.models.bamba.modular_bamba.BambaRotaryEmbedding | from transformers.models.llama.modeling_llama import LlamaAttention, LlamaForCausalLM, LlamaMLP, LlamaRMSNorm, LlamaRotaryEmbedding, rotate_half
class BambaRotaryEmbedding(LlamaRotaryEmbedding):
pass |
class BambaRotaryEmbedding(LlamaRotaryEmbedding):
pass | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 13 | 2 | 0 | 2 | 1 | 1 | 0 | 2 | 1 | 1 | 0 | 2 | 0 | 0 |
708 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bamba/modular_bamba.py | transformers.models.bamba.modular_bamba.HybridMambaAttentionDynamicCache | import torch
from .configuration_bamba import BambaConfig
from transformers.models.jamba.modeling_jamba import HybridMambaAttentionDynamicCache, JambaAttentionDecoderLayer
class HybridMambaAttentionDynamicCache(HybridMambaAttentionDynamicCache):
"""
A dynamic cache that can handle both the attention cache (whi... |
class HybridMambaAttentionDynamicCache(HybridMambaAttentionDynamicCache):
'''
A dynamic cache that can handle both the attention cache (which has a seq_len dimension) and the mamba cache
(which has a constant shape regardless of seq_len).
This cache has two sets of lists of tensors: `key_cache` and `va... | 2 | 1 | 38 | 2 | 36 | 1 | 3 | 0.32 | 1 | 3 | 1 | 0 | 1 | 7 | 1 | 39 | 52 | 4 | 37 | 12 | 35 | 12 | 19 | 12 | 17 | 3 | 4 | 2 | 3 |
709 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bark/configuration_bark.py | transformers.models.bark.configuration_bark.BarkCoarseConfig | from ...utils import add_start_docstrings, logging
@add_start_docstrings(BARK_SUBMODELCONFIG_START_DOCSTRING.format(config='BarkCoarseConfig', model='BarkCoarseModel'), '\n Example:\n\n ```python\n >>> from transformers import BarkCoarseConfig, BarkCoarseModel\n\n >>> # Initializing a Bark sub-module style... | @add_start_docstrings(BARK_SUBMODELCONFIG_START_DOCSTRING.format(config='BarkCoarseConfig', model='BarkCoarseModel'), '\n Example:\n\n ```python\n >>> from transformers import BarkCoarseConfig, BarkCoarseModel\n\n >>> # Initializing a Bark sub-module style configuration\n >>> configuration = BarkCoarseCo... | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 3 | 0 | 3 | 3 | 2 | 0 | 3 | 3 | 2 | 0 | 2 | 0 | 0 |
710 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bark/configuration_bark.py | transformers.models.bark.configuration_bark.BarkConfig | from ...configuration_utils import PretrainedConfig
from ..auto import CONFIG_MAPPING, AutoConfig
from typing import Optional
class BarkConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`BarkModel`]. It is used to instantiate a Bark
model according to the specif... |
class BarkConfig(PretrainedConfig):
'''
This is the configuration class to store the configuration of a [`BarkModel`]. It is used to instantiate a Bark
model according to the specified sub-models configurations, defining the model architecture.
Instantiating a configuration with the defaults will yield... | 4 | 2 | 28 | 4 | 22 | 3 | 4 | 0.88 | 1 | 4 | 3 | 0 | 1 | 5 | 2 | 2 | 118 | 20 | 52 | 27 | 33 | 46 | 25 | 11 | 22 | 6 | 1 | 1 | 7 |
711 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bark/configuration_bark.py | transformers.models.bark.configuration_bark.BarkFineConfig | from ...utils import add_start_docstrings, logging
@add_start_docstrings(BARK_SUBMODELCONFIG_START_DOCSTRING.format(config='BarkFineConfig', model='BarkFineModel'), '\n n_codes_total (`int`, *optional*, defaults to 8):\n The total number of audio codebooks predicted. Used in the fine acoustics sub-mo... | @add_start_docstrings(BARK_SUBMODELCONFIG_START_DOCSTRING.format(config='BarkFineConfig', model='BarkFineModel'), '\n n_codes_total (`int`, *optional*, defaults to 8):\n The total number of audio codebooks predicted. Used in the fine acoustics sub-model.\n n_codes_given (`int`, *optional*, defa... | 3 | 0 | 5 | 1 | 4 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 1 | 2 | 1 | 2 | 9 | 2 | 7 | 6 | 5 | 0 | 7 | 6 | 5 | 1 | 2 | 0 | 1 |
712 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bark/configuration_bark.py | transformers.models.bark.configuration_bark.BarkSemanticConfig | from ...utils import add_start_docstrings, logging
@add_start_docstrings(BARK_SUBMODELCONFIG_START_DOCSTRING.format(config='BarkSemanticConfig', model='BarkSemanticModel'), '\n Example:\n\n ```python\n >>> from transformers import BarkSemanticConfig, BarkSemanticModel\n\n >>> # Initializing a Bark sub-modu... | @add_start_docstrings(BARK_SUBMODELCONFIG_START_DOCSTRING.format(config='BarkSemanticConfig', model='BarkSemanticModel'), '\n Example:\n\n ```python\n >>> from transformers import BarkSemanticConfig, BarkSemanticModel\n\n >>> # Initializing a Bark sub-module style configuration\n >>> configuration = Bark... | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 3 | 0 | 3 | 3 | 2 | 0 | 3 | 3 | 2 | 0 | 2 | 0 | 0 |
713 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bark/configuration_bark.py | transformers.models.bark.configuration_bark.BarkSubModelConfig | from ...configuration_utils import PretrainedConfig
class BarkSubModelConfig(PretrainedConfig):
keys_to_ignore_at_inference = ['past_key_values']
attribute_map = {'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers', 'vocab_size': 'input_vocab_size', 'window_size': 'block_size'}
def __ini... |
class BarkSubModelConfig(PretrainedConfig):
def __init__(self, block_size=1024, input_vocab_size=10048, output_vocab_size=10048, num_layers=12, num_heads=12, hidden_size=768, dropout=0.0, bias=True, initializer_range=0.02, use_cache=True, **kwargs):
pass | 2 | 0 | 26 | 1 | 25 | 1 | 1 | 0.03 | 1 | 1 | 0 | 3 | 1 | 10 | 1 | 1 | 36 | 3 | 33 | 27 | 18 | 1 | 15 | 14 | 13 | 1 | 1 | 0 | 1 |
714 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bark/generation_configuration_bark.py | transformers.models.bark.generation_configuration_bark.BarkCoarseGenerationConfig | from ...generation.configuration_utils import GenerationConfig
class BarkCoarseGenerationConfig(GenerationConfig):
model_type = 'coarse_acoustics'
def __init__(self, renormalize_logits=True, output_scores=False, return_dict_in_generate=False, output_hidden_states=False, output_attentions=False, temperature=1.... |
class BarkCoarseGenerationConfig(GenerationConfig):
def __init__(self, renormalize_logits=True, output_scores=False, return_dict_in_generate=False, output_hidden_states=False, output_attentions=False, temperature=1.0, do_sample=False, coarse_semantic_pad_token=12048, coarse_rate_hz=75, n_coarse_codebooks=2, coars... | 2 | 1 | 75 | 3 | 35 | 37 | 1 | 1 | 1 | 2 | 0 | 0 | 1 | 7 | 1 | 18 | 78 | 4 | 37 | 27 | 18 | 37 | 11 | 10 | 9 | 1 | 2 | 0 | 1 |
715 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bark/generation_configuration_bark.py | transformers.models.bark.generation_configuration_bark.BarkFineGenerationConfig | from ...generation.configuration_utils import GenerationConfig
class BarkFineGenerationConfig(GenerationConfig):
model_type = 'fine_acoustics'
def __init__(self, temperature=1.0, max_fine_history_length=512, max_fine_input_length=1024, n_fine_codebooks=8, **kwargs):
"""Class that holds a generation co... |
class BarkFineGenerationConfig(GenerationConfig):
def __init__(self, temperature=1.0, max_fine_history_length=512, max_fine_input_length=1024, n_fine_codebooks=8, **kwargs):
'''Class that holds a generation configuration for [`BarkFineModel`].
[`BarkFineModel`] is an autoencoder model, so should n... | 3 | 2 | 19 | 2 | 7 | 10 | 1 | 1.19 | 1 | 1 | 0 | 0 | 2 | 3 | 2 | 19 | 41 | 6 | 16 | 14 | 6 | 19 | 9 | 7 | 6 | 1 | 2 | 0 | 2 |
716 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bark/generation_configuration_bark.py | transformers.models.bark.generation_configuration_bark.BarkGenerationConfig | from ...generation.configuration_utils import GenerationConfig
from typing import Optional
import copy
class BarkGenerationConfig(GenerationConfig):
model_type = 'bark'
def __init__(self, semantic_config: Optional[dict]=None, coarse_acoustics_config: Optional[dict]=None, fine_acoustics_config: Optional[dict]=... |
class BarkGenerationConfig(GenerationConfig):
def __init__(self, semantic_config: Optional[dict]=None, coarse_acoustics_config: Optional[dict]=None, fine_acoustics_config: Optional[dict]=None, sample_rate=24000, codebook_size=1024, **kwargs):
'''Class that holds a generation configuration for [`BarkModel`... | 5 | 3 | 27 | 4 | 14 | 9 | 2 | 0.62 | 1 | 3 | 3 | 0 | 2 | 5 | 3 | 20 | 91 | 15 | 47 | 27 | 28 | 29 | 27 | 12 | 23 | 4 | 2 | 1 | 6 |
717 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bark/generation_configuration_bark.py | transformers.models.bark.generation_configuration_bark.BarkSemanticGenerationConfig | from ...generation.configuration_utils import GenerationConfig
class BarkSemanticGenerationConfig(GenerationConfig):
model_type = 'semantic'
def __init__(self, eos_token_id=10000, renormalize_logits=True, max_new_tokens=768, output_scores=False, return_dict_in_generate=False, output_hidden_states=False, outpu... |
class BarkSemanticGenerationConfig(GenerationConfig):
def __init__(self, eos_token_id=10000, renormalize_logits=True, max_new_tokens=768, output_scores=False, return_dict_in_generate=False, output_hidden_states=False, output_attentions=False, temperature=1.0, do_sample=False, text_encoding_offset=10048, text_pad_... | 2 | 1 | 86 | 3 | 40 | 43 | 1 | 1.02 | 1 | 1 | 0 | 0 | 1 | 8 | 1 | 18 | 89 | 4 | 42 | 30 | 21 | 43 | 12 | 11 | 10 | 1 | 2 | 0 | 1 |
718 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bark/modeling_bark.py | transformers.models.bark.modeling_bark.BarkBlock | from ...modeling_layers import GradientCheckpointingLayer
from torch import nn
class BarkBlock(GradientCheckpointingLayer):
def __init__(self, config, is_causal=False, layer_idx=None):
super().__init__()
if is_causal:
self.layernorm_1 = nn.LayerNorm(config.hidden_size, bias=config.bias... |
class BarkBlock(GradientCheckpointingLayer):
def __init__(self, config, is_causal=False, layer_idx=None):
pass
def forward(self, hidden_states, past_key_values=None, attention_mask=None, head_mask=None, use_cache=False, output_attentions=False, cache_position=None):
pass | 3 | 0 | 25 | 4 | 20 | 3 | 2 | 0.13 | 1 | 3 | 2 | 0 | 2 | 4 | 2 | 12 | 52 | 9 | 40 | 19 | 29 | 5 | 21 | 11 | 18 | 2 | 1 | 1 | 4 |
719 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bark/modeling_bark.py | transformers.models.bark.modeling_bark.BarkCausalModel | from ...modeling_outputs import CausalLMOutputWithPast, MaskedLMOutput
from typing import Optional, Union
import torch
from ...modeling_attn_mask_utils import _prepare_4d_attention_mask
from .configuration_bark import BarkCoarseConfig, BarkConfig, BarkFineConfig, BarkSemanticConfig, BarkSubModelConfig
from ...utils imp... |
class BarkCausalModel(BarkPreTrainedModel, GenerationMixin):
def __init__(self, config):
pass
def get_output_embeddings(self):
pass
def get_input_embeddings(self):
pass
def set_input_embeddings(self, new_embeddings):
pass
def prepare_inputs_for_generation(self, ... | 8 | 1 | 41 | 6 | 30 | 5 | 7 | 0.17 | 2 | 12 | 3 | 2 | 5 | 9 | 6 | 9 | 254 | 43 | 183 | 55 | 159 | 31 | 110 | 38 | 103 | 30 | 2 | 2 | 43 |
720 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bark/modeling_bark.py | transformers.models.bark.modeling_bark.BarkCoarseModel | from .generation_configuration_bark import BarkCoarseGenerationConfig, BarkFineGenerationConfig, BarkSemanticGenerationConfig
from ...utils import auto_docstring, is_accelerate_available, is_torch_accelerator_available, logging
import numpy as np
import torch
from ...generation.logits_process import AlternatingCodebook... | @auto_docstring(custom_intro='\n Bark coarse acoustics model.\n It shares the same architecture as the semantic (or text) model. It is a GPT-2 like autoregressive model with a\n language modeling head on top.\n ')
class BarkCoarseModel(BarkCausalModel):
def preprocess_histories(self, max_coarse_history... | 4 | 2 | 104 | 18 | 59 | 28 | 5 | 0.45 | 1 | 10 | 3 | 0 | 2 | 0 | 2 | 11 | 213 | 37 | 121 | 48 | 101 | 55 | 60 | 31 | 57 | 5 | 3 | 3 | 9 |
721 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bark/modeling_bark.py | transformers.models.bark.modeling_bark.BarkFineModel | from torch import nn
from ...modeling_attn_mask_utils import _prepare_4d_attention_mask
from typing import Optional, Union
from .generation_configuration_bark import BarkCoarseGenerationConfig, BarkFineGenerationConfig, BarkSemanticGenerationConfig
import numpy as np
from ...utils import auto_docstring, is_accelerate_a... | @auto_docstring(custom_intro='\n Bark fine acoustics model. It is a non-causal GPT-like model with `config.n_codes_total` embedding layers and\n language modeling heads, one for each codebook.\n ')
class BarkFineModel(BarkPreTrainedModel):
def __init__(self, config):
pass
def get_input_embedd... | 15 | 4 | 35 | 6 | 21 | 8 | 5 | 0.38 | 1 | 15 | 5 | 0 | 11 | 13 | 11 | 14 | 401 | 75 | 239 | 101 | 200 | 91 | 170 | 73 | 158 | 22 | 2 | 3 | 52 |
722 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bark/modeling_bark.py | transformers.models.bark.modeling_bark.BarkMLP | from torch import nn
class BarkMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.in_proj = nn.Linear(config.hidden_size, 4 * config.hidden_size, bias=config.bias)
self.out_proj = nn.Linear(4 * config.hidden_size, config.hidden_size, bias=config.bias)
self.dropout ... |
class BarkMLP(nn.Module):
def __init__(self, config):
pass
def forward(self, hidden_states):
pass | 3 | 0 | 6 | 0 | 6 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 2 | 4 | 2 | 12 | 14 | 1 | 13 | 7 | 10 | 0 | 13 | 7 | 10 | 1 | 1 | 0 | 2 |
723 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bark/modeling_bark.py | transformers.models.bark.modeling_bark.BarkModel | import warnings
from ...modeling_utils import PreTrainedModel, get_parameter_device
from .configuration_bark import BarkCoarseConfig, BarkConfig, BarkFineConfig, BarkSemanticConfig, BarkSubModelConfig
from .generation_configuration_bark import BarkCoarseGenerationConfig, BarkFineGenerationConfig, BarkSemanticGeneration... | @auto_docstring(custom_intro="\n The full Bark model, a text-to-speech model composed of 4 sub-models:\n - [`BarkSemanticModel`] (also referred to as the 'text' model): a causal auto-regressive transformer model that\n takes\n as input tokenized text, and predicts semantic text tokens that capture the mea... | 12 | 5 | 43 | 6 | 23 | 14 | 5 | 0.58 | 1 | 14 | 7 | 0 | 5 | 7 | 6 | 9 | 270 | 43 | 145 | 52 | 121 | 84 | 90 | 36 | 82 | 15 | 2 | 3 | 27 |
724 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bark/modeling_bark.py | transformers.models.bark.modeling_bark.BarkPreTrainedModel | import torch
from ...modeling_utils import PreTrainedModel, get_parameter_device
from ...utils import auto_docstring, is_accelerate_available, is_torch_accelerator_available, logging
from torch import nn
from .configuration_bark import BarkCoarseConfig, BarkConfig, BarkFineConfig, BarkSemanticConfig, BarkSubModelConfig... | @auto_docstring
class BarkPreTrainedModel(PreTrainedModel):
def _init_weights(self, module):
'''Initialize the weights.'''
pass
def __init__(self, *inputs, **kwargs):
pass
@property
def device(self) -> torch.device:
'''
`torch.device`: The device on which the mo... | 6 | 2 | 12 | 1 | 8 | 3 | 4 | 0.4 | 1 | 1 | 0 | 3 | 3 | 0 | 3 | 3 | 48 | 6 | 30 | 9 | 25 | 12 | 23 | 8 | 19 | 6 | 1 | 2 | 11 |
725 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bark/modeling_bark.py | transformers.models.bark.modeling_bark.BarkSelfAttention | import math
import torch
from torch import nn
class BarkSelfAttention(nn.Module):
def __init__(self, config, is_causal=False, layer_idx=None):
super().__init__()
self.dropout = config.dropout
self.attn_dropout = nn.Dropout(config.dropout)
self.resid_dropout = nn.Dropout(config.drop... |
class BarkSelfAttention(nn.Module):
def __init__(self, config, is_causal=False, layer_idx=None):
pass
def _split_heads(self, tensor, num_heads, attn_head_size):
'''
Splits hidden_size dim into attn_head_size and num_heads
'''
pass
def _merge_heads(self, tensor, nu... | 6 | 2 | 23 | 4 | 15 | 4 | 3 | 0.29 | 1 | 3 | 0 | 1 | 5 | 9 | 5 | 15 | 123 | 26 | 76 | 35 | 62 | 22 | 61 | 27 | 55 | 4 | 1 | 1 | 13 |
726 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bark/modeling_bark.py | transformers.models.bark.modeling_bark.BarkSelfFlashAttention2 | from ...modeling_flash_attention_utils import flash_attn_supports_top_left_mask, is_flash_attn_available
class BarkSelfFlashAttention2(BarkSelfAttention):
"""
Bark flash attention module. This module inherits from `BarkSelfAttention` as the weights of the module stays
untouched. The only required change wo... |
class BarkSelfFlashAttention2(BarkSelfAttention):
'''
Bark flash attention module. This module inherits from `BarkSelfAttention` as the weights of the module stays
untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
flash attention and dea... | 5 | 3 | 19 | 2 | 13 | 4 | 2 | 0.42 | 1 | 1 | 0 | 0 | 4 | 2 | 4 | 19 | 87 | 13 | 52 | 23 | 39 | 22 | 34 | 15 | 29 | 5 | 2 | 1 | 8 |
727 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bark/modeling_bark.py | transformers.models.bark.modeling_bark.BarkSemanticModel | from typing import Optional, Union
from .generation_configuration_bark import BarkCoarseGenerationConfig, BarkFineGenerationConfig, BarkSemanticGenerationConfig
from ...generation.logits_process import AlternatingCodebooksLogitsProcessor, BarkEosPrioritizerLogitsProcessor, SuppressTokensLogitsProcessor
from .configurat... | @auto_docstring(custom_intro='\n Bark semantic (or text) model. It shares the same architecture as the coarse model.\n It is a GPT-2 like autoregressive model with a language modeling head on top.\n ')
class BarkSemanticModel(BarkCausalModel):
def generate(self, input_ids: torch.Tensor, semantic_generatio... | 3 | 1 | 98 | 17 | 59 | 23 | 4 | 0.37 | 1 | 10 | 3 | 0 | 1 | 1 | 1 | 10 | 102 | 18 | 62 | 22 | 53 | 23 | 26 | 14 | 24 | 4 | 3 | 1 | 4 |
728 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bark/processing_bark.py | transformers.models.bark.processing_bark.BarkProcessor | from ...utils.hub import cached_file
import json
from ...feature_extraction_utils import BatchFeature
from ...tokenization_utils_base import BatchEncoding
from ..auto import AutoTokenizer
from typing import Optional
from ...processing_utils import ProcessorMixin
import os
import numpy as np
class BarkProcessor(Process... |
class BarkProcessor(ProcessorMixin):
'''
Constructs a Bark processor which wraps a text tokenizer and optional Bark voice presets into a single processor.
Args:
tokenizer ([`PreTrainedTokenizer`]):
An instance of [`PreTrainedTokenizer`].
speaker_embeddings (`dict[dict[str]]`, *o... | 11 | 5 | 37 | 5 | 23 | 10 | 4 | 0.48 | 1 | 8 | 2 | 0 | 5 | 1 | 6 | 23 | 253 | 39 | 145 | 47 | 118 | 69 | 67 | 25 | 60 | 6 | 2 | 4 | 24 |
729 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bart/configuration_bart.py | transformers.models.bart.configuration_bart.BartConfig | from ...configuration_utils import PretrainedConfig
import warnings
class BartConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`BartModel`]. It is used to instantiate a BART
model according to the specified arguments, defining the model architecture. Instantiat... |
class BartConfig(PretrainedConfig):
'''
This is the configuration class to store the configuration of a [`BartModel`]. It is used to instantiate a BART
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar confi... | 2 | 1 | 69 | 2 | 66 | 2 | 2 | 0.97 | 1 | 1 | 0 | 0 | 1 | 21 | 1 | 1 | 149 | 12 | 70 | 55 | 39 | 68 | 29 | 26 | 27 | 2 | 1 | 1 | 2 |
730 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bart/configuration_bart.py | transformers.models.bart.configuration_bart.BartOnnxConfig | from typing import Any
from collections import OrderedDict
from ... import PreTrainedTokenizer
from ...onnx.utils import compute_effective_axis_dimension
from collections.abc import Mapping
from ...utils import is_torch_available, logging
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeq2SeqConfigWithPast
cl... |
class BartOnnxConfig(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: PreTrainedTokenizer, bat... | 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 |
731 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bart/modeling_bart.py | transformers.models.bart.modeling_bart.BartAttention | from typing import Callable, Optional, Union
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from ...processing_utils import Unpack
from torch import nn
from .configuration_bart import BartConfig
from ...utils.deprecation import deprecate_kwarg
import torch
from ...cache_utils import Cache, Dynam... |
class BartAttention(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[BartConfig]=None, layer_idx: Optional[int]=None):
... | 4 | 2 | 50 | 7 | 35 | 8 | 5 | 0.24 | 1 | 7 | 1 | 2 | 3 | 12 | 3 | 13 | 156 | 23 | 107 | 44 | 86 | 26 | 68 | 27 | 64 | 12 | 1 | 2 | 15 |
732 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bart/modeling_bart.py | transformers.models.bart.modeling_bart.BartClassificationHead | from torch import nn
import torch
class BartClassificationHead(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)
se... |
class BartClassificationHead(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 |
733 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bart/modeling_bart.py | transformers.models.bart.modeling_bart.BartDecoder | from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
from typing import Callable, Optional, Union
from ...utils import auto_docstring, is_torch_flex_attn_available, is_torchdynamo_compiling, logging
import math
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, Ca... |
class BartDecoder(BartPreTrainedModel):
'''
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`BartDecoderLayer`]
Args:
config: BartConfig
embed_tokens (nn.Embedding): output embedding
'''
def __init__(self, config: BartConfig, embed_tokens: Optiona... | 3 | 2 | 68 | 9 | 42 | 18 | 12 | 0.45 | 1 | 13 | 5 | 0 | 4 | 11 | 4 | 6 | 285 | 41 | 168 | 45 | 149 | 76 | 88 | 31 | 83 | 43 | 2 | 3 | 48 |
734 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bart/modeling_bart.py | transformers.models.bart.modeling_bart.BartDecoderLayer | from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
from ...utils.deprecation import deprecate_kwarg
from ...activations import ACT2FN
from typing import Callable, Optional, Union
from torch import nn
from .configuration_bart import BartConfig
from ...modeling_layers import GradientCheckpointingLayer
im... |
class BartDecoderLayer(GradientCheckpointingLayer):
def __init__(self, config: BartConfig, 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]=None,... | 4 | 1 | 58 | 6 | 40 | 13 | 4 | 0.31 | 1 | 4 | 1 | 0 | 2 | 11 | 2 | 12 | 118 | 12 | 81 | 32 | 67 | 25 | 44 | 21 | 41 | 6 | 1 | 1 | 7 |
735 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bart/modeling_bart.py | transformers.models.bart.modeling_bart.BartDecoderWrapper | class BartDecoderWrapper(BartPreTrainedModel):
"""
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().__init__(config)
... | class BartDecoderWrapper(BartPreTrainedModel):
'''
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
def forward(self, *arg... | 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 |
736 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bart/modeling_bart.py | transformers.models.bart.modeling_bart.BartEncoder | from typing import Callable, Optional, Union
import math
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, Seq2SeqLMOutput, Seq2SeqModelOutput, Seq2SeqQuestionAnsweringModelOutput, Seq2SeqSequenceClassifierOutput
from torch import nn
from .con... |
class BartEncoder(BartPreTrainedModel):
'''
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`BartEncoderLayer`].
Args:
config: BartConfig
embed_tokens (nn.Embedding): output embedding
'''
def __init__(self, config: BartConfig, e... | 3 | 2 | 43 | 6 | 27 | 10 | 8 | 0.43 | 1 | 12 | 5 | 0 | 4 | 11 | 4 | 6 | 186 | 30 | 110 | 35 | 96 | 47 | 71 | 26 | 66 | 27 | 2 | 3 | 32 |
737 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bart/modeling_bart.py | transformers.models.bart.modeling_bart.BartEncoderLayer | from torch import nn
from .configuration_bart import BartConfig
from ...modeling_layers import GradientCheckpointingLayer
import torch
from ...activations import ACT2FN
from typing import Callable, Optional, Union
class BartEncoderLayer(GradientCheckpointingLayer):
def __init__(self, config: BartConfig, layer_idx... |
class BartEncoderLayer(GradientCheckpointingLayer):
def __init__(self, config: BartConfig, layer_idx: Optional[int]=None):
pass
def forward(self, hidden_states: torch.FloatTensor, attention_mask: torch.FloatTensor, layer_head_mask: torch.FloatTensor, output_attentions: Optional[bool]=False) -> tuple[... | 3 | 1 | 33 | 3 | 25 | 6 | 2 | 0.22 | 1 | 3 | 1 | 0 | 2 | 9 | 2 | 12 | 68 | 7 | 50 | 22 | 41 | 11 | 32 | 16 | 29 | 3 | 1 | 1 | 4 |
738 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bart/modeling_bart.py | transformers.models.bart.modeling_bart.BartForCausalLM | from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
from typing import Callable, Optional, Union
from ...utils import auto_docstring, is_torch_flex_attn_available, is_torchdynamo_compiling, logging
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...generation import GenerationMixi... | @auto_docstring(custom_intro='\n BART decoder with a language modeling head on top (linear layer with weights tied to the input embeddings).\n ')
class BartForCausalLM(BartPreTrainedModel, GenerationMixin):
def __init__(self, config):
pass
def get_input_embeddings(self):
pass
def se... | 9 | 1 | 19 | 3 | 9 | 8 | 2 | 0.84 | 2 | 6 | 2 | 0 | 8 | 2 | 9 | 11 | 186 | 33 | 83 | 37 | 56 | 70 | 42 | 20 | 32 | 7 | 2 | 1 | 16 |
739 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bart/modeling_bart.py | transformers.models.bart.modeling_bart.BartForConditionalGeneration | from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
from typing import Callable, Optional, Union
from ...utils import auto_docstring, is_torch_flex_attn_available, is_torchdynamo_compiling, logging
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...generation import GenerationMixi... | @auto_docstring(custom_intro='\n The BART Model with a language modeling head. Can be used for summarization.\n ')
class BartForConditionalGeneration(BartPreTrainedModel, GenerationMixin):
def __init__(self, config: BartConfig):
pass
def get_encoder(self):
pass
def get_decoder(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 |
740 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bart/modeling_bart.py | transformers.models.bart.modeling_bart.BartForQuestionAnswering | from typing import Callable, Optional, Union
from ...utils import auto_docstring, is_torch_flex_attn_available, is_torchdynamo_compiling, logging
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWi... | @auto_docstring
class BartForQuestionAnswering(BartPreTrainedModel):
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, decoder_attention_mask: Op... | 5 | 1 | 52 | 5 | 41 | 7 | 5 | 0.14 | 1 | 5 | 2 | 0 | 2 | 3 | 2 | 4 | 116 | 12 | 91 | 36 | 62 | 13 | 36 | 17 | 33 | 8 | 2 | 2 | 9 |
741 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bart/modeling_bart.py | transformers.models.bart.modeling_bart.BartForSequenceClassification | from typing import Callable, Optional, Union
from ...utils import auto_docstring, is_torch_flex_attn_available, is_torchdynamo_compiling, logging
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWi... | @auto_docstring(custom_intro='\n Bart model with a sequence classification/head on top (a linear layer on top of the pooled output) e.g. for GLUE\n tasks.\n ')
class BartForSequenceClassification(BartPreTrainedModel):
def __init__(self, config: BartConfig, **kwargs):
pass
@auto_docstring
d... | 5 | 1 | 55 | 4 | 48 | 4 | 8 | 0.07 | 1 | 10 | 4 | 0 | 2 | 2 | 2 | 4 | 121 | 10 | 105 | 32 | 77 | 7 | 41 | 14 | 38 | 15 | 2 | 3 | 16 |
742 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bart/modeling_bart.py | transformers.models.bart.modeling_bart.BartLearnedPositionalEmbedding | import torch
from torch import nn
from typing import Callable, Optional, Union
class BartLearnedPositionalEmbedding(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
s... |
class BartLearnedPositionalEmbedding(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: torch.Tensor, past_key_values_length: int=0, position_ids: Option... | 3 | 2 | 7 | 1 | 5 | 2 | 1 | 0.6 | 1 | 3 | 0 | 0 | 2 | 1 | 2 | 2 | 20 | 4 | 10 | 6 | 7 | 6 | 8 | 6 | 5 | 1 | 1 | 0 | 2 |
743 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bart/modeling_bart.py | transformers.models.bart.modeling_bart.BartModel | from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
from typing import Callable, Optional, Union
from ...utils import auto_docstring, is_torch_flex_attn_available, is_torchdynamo_compiling, logging
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, CausalLMOutput... | @auto_docstring
class BartModel(BartPreTrainedModel):
def __init__(self, config: BartConfig):
pass
def _tie_weights(self):
pass
def get_input_embeddings(self):
pass
def set_input_embeddings(self, value):
pass
def get_encoder(self):
pass
@auto_docstrin... | 9 | 1 | 16 | 1 | 14 | 1 | 3 | 0.05 | 1 | 10 | 6 | 0 | 7 | 3 | 7 | 9 | 128 | 16 | 107 | 33 | 75 | 5 | 40 | 15 | 32 | 12 | 2 | 2 | 20 |
744 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bart/modeling_bart.py | transformers.models.bart.modeling_bart.BartPreTrainedModel | from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
from typing import Callable, Optional, Union
from ...modeling_attn_mask_utils import AttentionMaskConverter, _prepare_4d_attention_mask, _prepare_4d_attention_mask_for_sdpa
from ...utils import auto_docstring, is_torch_flex_attn_available, is_torchdyna... | @auto_docstring
class BartPreTrainedModel(PreTrainedModel):
def _init_weights(self, module):
pass
@property
def dummy_inputs(self):
pass
def _update_full_mask(self, attention_mask: Union[torch.Tensor, None], inputs_embeds: torch.Tensor):
pass
def _update_causal_mask(self, ... | 10 | 1 | 9 | 0 | 9 | 0 | 3 | 0 | 1 | 0 | 0 | 10 | 2 | 0 | 2 | 2 | 30 | 2 | 28 | 16 | 24 | 0 | 23 | 15 | 20 | 5 | 1 | 2 | 6 |
745 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bart/modeling_bart.py | transformers.models.bart.modeling_bart.BartPretrainedModel | import warnings
class BartPretrainedModel(BartPreTrainedModel):
def __init_subclass__(self):
warnings.warn('The class `PretrainedBartModel` has been depreciated, please use `BartPreTrainedModel` instead.', FutureWarning) |
class BartPretrainedModel(BartPreTrainedModel):
def __init_subclass__(self):
pass | 2 | 0 | 5 | 0 | 5 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 3 | 6 | 0 | 6 | 2 | 4 | 0 | 3 | 2 | 1 | 1 | 2 | 0 | 1 |
746 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bart/modeling_bart.py | transformers.models.bart.modeling_bart.BartScaledWordEmbedding | from typing import Callable, Optional, Union
import torch
from torch import nn
class BartScaledWordEmbedding(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_sca... |
class BartScaledWordEmbedding(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: torch.... | 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 |
747 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bart/modeling_bart.py | transformers.models.bart.modeling_bart.PretrainedBartModel | import warnings
class PretrainedBartModel(BartPreTrainedModel):
def __init_subclass__(self):
warnings.warn('The class `PretrainedBartModel` has been depreciated, please use `BartPreTrainedModel` instead.', FutureWarning) |
class PretrainedBartModel(BartPreTrainedModel):
def __init_subclass__(self):
pass | 2 | 0 | 5 | 0 | 5 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 3 | 6 | 0 | 6 | 2 | 4 | 0 | 3 | 2 | 1 | 1 | 2 | 0 | 1 |
748 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bart/tokenization_bart.py | transformers.models.bart.tokenization_bart.BartTokenizer | import regex as re
from typing import Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
import json
import os
class BartTokenizer(PreTrainedTokenizer):
"""
Constructs a BART tokenizer, which is smilar to the ROBERTa tokenizer, using byte-level Byte-Pair-Encoding.
This tokenizer ha... |
class BartTokenizer(PreTrainedTokenizer):
'''
Constructs a BART tokenizer, which is smilar to the ROBERTa tokenizer, using byte-level Byte-Pair-Encoding.
This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
be encoded differently whether it ... | 15 | 8 | 17 | 1 | 12 | 4 | 3 | 0.63 | 1 | 11 | 0 | 0 | 13 | 9 | 13 | 102 | 317 | 51 | 165 | 70 | 130 | 104 | 120 | 45 | 106 | 9 | 3 | 3 | 39 |
749 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bart/tokenization_bart_fast.py | transformers.models.bart.tokenization_bart_fast.BartTokenizerFast | import json
from .tokenization_bart import BartTokenizer
from tokenizers import processors
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...tokenization_utils_base import AddedToken, BatchEncoding
from typing import Optional
class BartTokenizerFast(PreTrainedTokenizerFast):
"""
Construct ... |
class BartTokenizerFast(PreTrainedTokenizerFast):
'''
Construct a "fast" BART tokenizer (backed by HuggingFace's *tokenizers* library), derived from the GPT-2 tokenizer,
using byte-level Byte-Pair-Encoding.
This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepie... | 11 | 4 | 18 | 2 | 12 | 3 | 3 | 0.84 | 1 | 6 | 1 | 0 | 8 | 1 | 8 | 96 | 234 | 43 | 104 | 45 | 75 | 87 | 59 | 25 | 50 | 8 | 3 | 2 | 22 |
750 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/barthez/tokenization_barthez.py | transformers.models.barthez.tokenization_barthez.BarthezTokenizer | import os
from ...utils.import_utils import requires
from shutil import copyfile
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
import sentencepiece as spm
from typing import Any, Optional
@requires(backends=('sentencepiece',))
class BarthezTokenizer(PreTrainedTokenizer):
"""
Adapted from [`... | @requires(backends=('sentencepiece',))
class BarthezTokenizer(PreTrainedTokenizer):
'''
Adapted from [`CamembertTokenizer`] and [`BartTokenizer`]. Construct a BARThez tokenizer. Based on
[SentencePiece](https://github.com/google/sentencepiece).
This tokenizer inherits from [`PreTrainedTokenizer`] which ... | 16 | 7 | 13 | 1 | 8 | 3 | 2 | 0.86 | 1 | 6 | 0 | 0 | 13 | 4 | 13 | 102 | 250 | 44 | 111 | 52 | 78 | 95 | 76 | 32 | 62 | 5 | 3 | 3 | 27 |
751 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/barthez/tokenization_barthez_fast.py | transformers.models.barthez.tokenization_barthez_fast.BarthezTokenizerFast | from shutil import copyfile
from ...tokenization_utils import AddedToken
import os
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from typing import Optional
class BarthezTokenizerFast(PreTrainedTokenizerFast):
"""
Adapted from [`CamembertTokenizer`] and [`BartTokenizer`]. Construct a "fast" BA... |
class BarthezTokenizerFast(PreTrainedTokenizerFast):
'''
Adapted from [`CamembertTokenizer`] and [`BartTokenizer`]. Construct a "fast" BARThez tokenizer. Based on
[SentencePiece](https://github.com/google/sentencepiece).
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of th... | 5 | 3 | 19 | 2 | 12 | 5 | 3 | 0.97 | 1 | 5 | 0 | 0 | 5 | 1 | 5 | 93 | 156 | 28 | 65 | 32 | 42 | 63 | 32 | 15 | 26 | 5 | 3 | 1 | 13 |
752 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bartpho/tokenization_bartpho.py | transformers.models.bartpho.tokenization_bartpho.BartphoTokenizer | from shutil import copyfile
import os
from ...utils.import_utils import requires
from typing import Any, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
import sentencepiece as spm
@requires(backends=('sentencepiece',))
class BartphoTokenizer(PreTrainedTokenizer):
"""
Adapted from [`... | @requires(backends=('sentencepiece',))
class BartphoTokenizer(PreTrainedTokenizer):
'''
Adapted from [`XLMRobertaTokenizer`]. Based on [SentencePiece](https://github.com/google/sentencepiece).
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to... | 16 | 7 | 15 | 2 | 10 | 3 | 3 | 0.75 | 1 | 6 | 0 | 0 | 13 | 7 | 13 | 102 | 280 | 52 | 130 | 60 | 96 | 98 | 88 | 37 | 74 | 10 | 3 | 4 | 34 |
753 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/beit/configuration_beit.py | transformers.models.beit.configuration_beit.BeitConfig | from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
from ...configuration_utils import PretrainedConfig
import warnings
class BeitConfig(BackboneConfigMixin, PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`BeitModel`]. It ... |
class BeitConfig(BackboneConfigMixin, PretrainedConfig):
'''
This is the configuration class to store the configuration of a [`BeitModel`]. It is used to instantiate an BEiT
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will... | 2 | 1 | 83 | 4 | 75 | 4 | 2 | 1.21 | 2 | 3 | 0 | 0 | 1 | 32 | 1 | 6 | 182 | 12 | 77 | 68 | 41 | 93 | 38 | 34 | 36 | 2 | 1 | 1 | 2 |
754 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/beit/configuration_beit.py | transformers.models.beit.configuration_beit.BeitOnnxConfig | from packaging import version
from collections import OrderedDict
from collections.abc import Mapping
from ...onnx import OnnxConfig
class BeitOnnxConfig(OnnxConfig):
torch_onnx_minimum_version = version.parse('1.11')
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
return OrderedDic... |
class BeitOnnxConfig(OnnxConfig):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
pass
@property
def atol_for_validation(self) -> float:
pass | 5 | 0 | 4 | 0 | 4 | 0 | 1 | 0 | 1 | 4 | 0 | 0 | 2 | 0 | 2 | 2 | 14 | 2 | 12 | 6 | 7 | 0 | 6 | 4 | 3 | 1 | 1 | 0 | 2 |
755 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/beit/feature_extraction_beit.py | transformers.models.beit.feature_extraction_beit.BeitFeatureExtractor | from .image_processing_beit import BeitImageProcessor
from ...utils.import_utils import requires
import warnings
@requires(backends=('vision',))
class BeitFeatureExtractor(BeitImageProcessor):
def __init__(self, *args, **kwargs) -> None:
warnings.warn('The class BeitFeatureExtractor is deprecated and will... | @requires(backends=('vision',))
class BeitFeatureExtractor(BeitImageProcessor):
def __init__(self, *args, **kwargs) -> None:
pass | 3 | 0 | 7 | 0 | 7 | 0 | 1 | 0 | 1 | 2 | 0 | 0 | 1 | 0 | 1 | 31 | 8 | 0 | 8 | 2 | 6 | 0 | 4 | 2 | 2 | 1 | 4 | 0 | 1 |
756 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/beit/image_processing_beit.py | transformers.models.beit.image_processing_beit.BeitImageProcessor | from ...image_processing_utils import INIT_SERVICE_KWARGS, BaseImageProcessor, BatchFeature, get_size_dict
from ...image_utils import IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD, ChannelDimension, ImageInput, PILImageResampling, infer_channel_dimension_format, is_scaled_image, make_flat_list_of_images, to_numpy_array... | @requires(backends=('vision',))
class BeitImageProcessor(BaseImageProcessor):
'''
Constructs a BEiT 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 the
... | 13 | 6 | 40 | 2 | 28 | 10 | 5 | 0.47 | 1 | 10 | 2 | 1 | 9 | 11 | 10 | 30 | 456 | 34 | 287 | 113 | 191 | 135 | 116 | 30 | 105 | 16 | 3 | 2 | 46 |
757 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/beit/modeling_beit.py | transformers.models.beit.modeling_beit.BeitAttention | from torch import Tensor, nn
import torch
from ...pytorch_utils import compile_compatible_method_lru_cache, find_pruneable_heads_and_indices, prune_linear_layer
from .configuration_beit import BeitConfig
from typing import Optional, Union
class BeitAttention(nn.Module):
def __init__(self, config: BeitConfig, wind... |
class BeitAttention(nn.Module):
def __init__(self, config: BeitConfig, window_size: Optional[tuple]=None) -> None:
pass
def prune_heads(self, heads):
pass
def forward(self, hidden_states: torch.Tensor, head_mask: Optional[torch.Tensor]=None, output_attentions: bool=False, relative_positi... | 4 | 0 | 13 | 1 | 11 | 1 | 1 | 0.09 | 1 | 8 | 2 | 0 | 3 | 3 | 3 | 13 | 42 | 6 | 34 | 19 | 22 | 3 | 22 | 11 | 18 | 2 | 1 | 1 | 4 |
758 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/beit/modeling_beit.py | transformers.models.beit.modeling_beit.BeitBackbone | from ...utils import auto_docstring, logging, torch_int
from torch import Tensor, nn
from ...modeling_outputs import BackboneOutput, BaseModelOutput, BaseModelOutputWithPooling, ImageClassifierOutput, MaskedLMOutput, SemanticSegmenterOutput
from ...utils.backbone_utils import BackboneMixin
from typing import Optional, ... | @auto_docstring(custom_intro='\n BEiT backbone, to be used with frameworks like DETR and MaskFormer.\n ')
class BeitBackbone(BeitPreTrainedModel, BackboneMixin):
def __init__(self, config):
pass
def get_input_embeddings(self):
pass
@auto_docstring
def forward(self, pixel_values: ... | 6 | 1 | 38 | 6 | 25 | 7 | 5 | 0.27 | 2 | 10 | 3 | 0 | 3 | 7 | 3 | 16 | 120 | 20 | 79 | 27 | 67 | 21 | 44 | 20 | 40 | 12 | 2 | 3 | 16 |
759 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/beit/modeling_beit.py | transformers.models.beit.modeling_beit.BeitConvModule | import torch
from typing import Optional, Union
from torch import Tensor, nn
class BeitConvModule(nn.Module):
"""
A convolutional block that bundles conv/norm/activation layers. This block simplifies the usage of convolution
layers, which are commonly used with a norm layer (e.g., BatchNorm) and activation... |
class BeitConvModule(nn.Module):
'''
A convolutional block that bundles conv/norm/activation layers. This block simplifies the usage of convolution
layers, which are commonly used with a norm layer (e.g., BatchNorm) and activation layer (e.g., ReLU).
Based on OpenMMLab's implementation, found in https:... | 3 | 1 | 13 | 1 | 13 | 0 | 1 | 0.19 | 1 | 5 | 0 | 0 | 2 | 3 | 2 | 12 | 35 | 4 | 26 | 15 | 15 | 5 | 11 | 7 | 8 | 1 | 1 | 0 | 2 |
760 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/beit/modeling_beit.py | transformers.models.beit.modeling_beit.BeitDropPath | import torch
from typing import Optional, Union
from torch import Tensor, nn
class BeitDropPath(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_p... |
class BeitDropPath(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:
pass
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
pass
def extra_repr(self) ->... | 4 | 1 | 2 | 0 | 2 | 0 | 1 | 0.13 | 1 | 4 | 0 | 0 | 3 | 1 | 3 | 13 | 12 | 3 | 8 | 5 | 4 | 1 | 8 | 5 | 4 | 1 | 1 | 0 | 3 |
761 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/beit/modeling_beit.py | transformers.models.beit.modeling_beit.BeitEmbeddings | import warnings
from ...utils import auto_docstring, logging, torch_int
from torch import Tensor, nn
import torch
import collections.abc
from .configuration_beit import BeitConfig
from typing import Optional, Union
class BeitEmbeddings(nn.Module):
"""
Construct the CLS token, position and patch embeddings. Opt... |
class BeitEmbeddings(nn.Module):
'''
Construct the CLS token, position and patch embeddings. Optionally, also the mask token.
'''
def __init__(self, config: BeitConfig) -> None:
pass
def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
... | 4 | 2 | 30 | 5 | 22 | 3 | 4 | 0.2 | 1 | 7 | 2 | 0 | 3 | 7 | 3 | 13 | 99 | 20 | 66 | 31 | 57 | 13 | 47 | 26 | 43 | 5 | 1 | 2 | 11 |
762 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/beit/modeling_beit.py | transformers.models.beit.modeling_beit.BeitEncoder | from .configuration_beit import BeitConfig
from typing import Optional, Union
from torch import Tensor, nn
from ...modeling_outputs import BackboneOutput, BaseModelOutput, BaseModelOutputWithPooling, ImageClassifierOutput, MaskedLMOutput, SemanticSegmenterOutput
import torch
class BeitEncoder(nn.Module):
def __in... |
class BeitEncoder(nn.Module):
def __init__(self, config: BeitConfig, window_size: Optional[tuple]=None) -> None:
pass
def forward(self, hidden_states: torch.Tensor, head_mask: Optional[torch.Tensor]=None, output_attentions: bool=False, output_hidden_states: bool=False, interpolate_pos_encoding: bool=... | 3 | 0 | 41 | 4 | 36 | 1 | 7 | 0.01 | 1 | 11 | 4 | 0 | 2 | 4 | 2 | 12 | 83 | 9 | 73 | 25 | 61 | 1 | 31 | 16 | 28 | 11 | 1 | 2 | 14 |
763 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/beit/modeling_beit.py | transformers.models.beit.modeling_beit.BeitFCNHead | from typing import Optional, Union
from .configuration_beit import BeitConfig
from torch import Tensor, nn
import torch
class BeitFCNHead(nn.Module):
"""
Fully Convolution Networks for Semantic Segmentation. This head is implemented of
[FCNNet](https://huggingface.co/papers/1411.4038>).
Args:
... |
class BeitFCNHead(nn.Module):
'''
Fully Convolution Networks for Semantic Segmentation. This head is implemented of
[FCNNet](https://huggingface.co/papers/1411.4038>).
Args:
config (BeitConfig): Configuration.
in_channels
kernel_size (int): The kernel size for convs in the head.... | 3 | 1 | 21 | 1 | 19 | 1 | 3 | 0.28 | 1 | 6 | 2 | 0 | 2 | 8 | 2 | 12 | 57 | 7 | 39 | 18 | 34 | 11 | 26 | 16 | 23 | 4 | 1 | 1 | 6 |
764 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/beit/modeling_beit.py | transformers.models.beit.modeling_beit.BeitForImageClassification | from ...utils import auto_docstring, logging, torch_int
from torch import Tensor, nn
from ...modeling_outputs import BackboneOutput, BaseModelOutput, BaseModelOutputWithPooling, ImageClassifierOutput, MaskedLMOutput, SemanticSegmenterOutput
import torch
from .configuration_beit import BeitConfig
from typing import Opti... | @auto_docstring(custom_intro='\n Beit Model transformer with an image classification head on top (a linear layer on top of the average of the final\n hidden states of the patch tokens) e.g. for ImageNet.\n ')
class BeitForImageClassification(BeitPreTrainedModel):
def __init__(self, config: BeitConfig) -> ... | 5 | 1 | 37 | 4 | 29 | 4 | 8 | 0.12 | 1 | 8 | 3 | 0 | 2 | 3 | 2 | 3 | 82 | 9 | 65 | 22 | 46 | 8 | 32 | 12 | 29 | 13 | 2 | 3 | 15 |
765 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/beit/modeling_beit.py | transformers.models.beit.modeling_beit.BeitForMaskedImageModeling | from torch.nn import CrossEntropyLoss
from ...utils import auto_docstring, logging, torch_int
from torch import Tensor, nn
from ...modeling_outputs import BackboneOutput, BaseModelOutput, BaseModelOutputWithPooling, ImageClassifierOutput, MaskedLMOutput, SemanticSegmenterOutput
import torch
from .configuration_beit imp... | @auto_docstring(custom_intro="\n Beit Model transformer with a 'language' modeling head on top. BEiT does masked image modeling by predicting\n visual tokens of a Vector-Quantize Variational Autoencoder (VQ-VAE), whereas other vision models like ViT and DeiT\n predict RGB pixel values. As a result, this class ... | 6 | 1 | 45 | 8 | 22 | 15 | 3 | 0.64 | 1 | 7 | 3 | 0 | 2 | 4 | 2 | 3 | 93 | 17 | 47 | 24 | 32 | 30 | 22 | 13 | 19 | 5 | 2 | 1 | 6 |
766 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/beit/modeling_beit.py | transformers.models.beit.modeling_beit.BeitForSemanticSegmentation | import torch
from typing import Optional, Union
from ...utils import auto_docstring, logging, torch_int
from torch.nn import CrossEntropyLoss
from torch import Tensor, nn
from ...modeling_outputs import BackboneOutput, BaseModelOutput, BaseModelOutputWithPooling, ImageClassifierOutput, MaskedLMOutput, SemanticSegmenter... | @auto_docstring
class BeitForSemanticSegmentation(BeitPreTrainedModel):
def __init__(self, config: BeitConfig) -> None:
pass
def compute_loss(self, logits, auxiliary_logits, labels):
pass
@auto_docstring
def forward(self, pixel_values: Optional[torch.Tensor]=None, head_mask: Optional[t... | 6 | 1 | 47 | 7 | 31 | 9 | 6 | 0.29 | 1 | 12 | 5 | 0 | 3 | 8 | 3 | 4 | 145 | 23 | 95 | 39 | 80 | 28 | 52 | 29 | 48 | 12 | 2 | 2 | 18 |
767 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/beit/modeling_beit.py | transformers.models.beit.modeling_beit.BeitIntermediate | from ...activations import ACT2FN
from .configuration_beit import BeitConfig
import torch
from torch import Tensor, nn
class BeitIntermediate(nn.Module):
def __init__(self, config: BeitConfig) -> None:
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
... |
class BeitIntermediate(nn.Module):
def __init__(self, config: BeitConfig) -> None:
pass
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
pass | 3 | 0 | 6 | 1 | 6 | 0 | 2 | 0 | 1 | 4 | 1 | 0 | 2 | 2 | 2 | 12 | 14 | 2 | 12 | 5 | 9 | 0 | 11 | 5 | 8 | 2 | 1 | 1 | 3 |
768 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/beit/modeling_beit.py | transformers.models.beit.modeling_beit.BeitLayer | from torch import Tensor, nn
import torch
from .configuration_beit import BeitConfig
from ...modeling_layers import GradientCheckpointingLayer
from typing import Optional, Union
class BeitLayer(GradientCheckpointingLayer):
"""This corresponds to the Block class in the timm implementation."""
def __init__(self... |
class BeitLayer(GradientCheckpointingLayer):
'''This corresponds to the Block class in the timm implementation.'''
def __init__(self, config: BeitConfig, window_size: Optional[tuple]=None, drop_path_rate: float=0.0) -> None:
pass
def forward(self, hidden_states: torch.Tensor, head_mask: Optional[... | 3 | 1 | 30 | 5 | 23 | 3 | 3 | 0.15 | 1 | 11 | 5 | 0 | 2 | 10 | 2 | 12 | 63 | 11 | 47 | 26 | 36 | 7 | 31 | 18 | 28 | 3 | 1 | 1 | 6 |
769 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/beit/modeling_beit.py | transformers.models.beit.modeling_beit.BeitModel | from .configuration_beit import BeitConfig
from typing import Optional, Union
from ...utils import auto_docstring, logging, torch_int
from torch import Tensor, nn
import torch
@auto_docstring
class BeitModel(BeitPreTrainedModel):
def __init__(self, config: BeitConfig, add_pooling_layer: bool=True) -> None:
... | @auto_docstring
class BeitModel(BeitPreTrainedModel):
def __init__(self, config: BeitConfig, add_pooling_layer: bool=True) -> None:
'''
add_pooling_layer (bool, *optional*, defaults to `True`):
Whether to add a pooling layer
'''
pass
def get_input_embeddings(self):
... | 7 | 3 | 20 | 2 | 14 | 4 | 3 | 0.22 | 1 | 9 | 5 | 0 | 4 | 5 | 4 | 5 | 90 | 11 | 65 | 27 | 43 | 14 | 29 | 17 | 24 | 7 | 2 | 1 | 13 |
770 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/beit/modeling_beit.py | transformers.models.beit.modeling_beit.BeitModelOutputWithPooling | from ...modeling_outputs import BackboneOutput, BaseModelOutput, BaseModelOutputWithPooling, ImageClassifierOutput, MaskedLMOutput, SemanticSegmenterOutput
from ...utils import auto_docstring, logging, torch_int
from dataclasses import dataclass
@dataclass
@auto_docstring(custom_intro='\n Class for outputs of [`Bei... | @dataclass
@auto_docstring(custom_intro='\n Class for outputs of [`BeitModel`].\n ')
class BeitModelOutputWithPooling(BaseModelOutputWithPooling):
'''
pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`):
Average of the last layer hidden states of the patch tokens (excluding th... | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 19 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 23 | 3 | 1 | 1 | 0 | 19 | 1 | 1 | 0 | 0 | 2 | 0 | 0 |
771 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/beit/modeling_beit.py | transformers.models.beit.modeling_beit.BeitOutput | from .configuration_beit import BeitConfig
import torch
from torch import Tensor, nn
class BeitOutput(nn.Module):
def __init__(self, config: BeitConfig) -> None:
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_... |
class BeitOutput(nn.Module):
def __init__(self, config: BeitConfig) -> None:
pass
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
pass | 3 | 0 | 5 | 1 | 4 | 0 | 1 | 0 | 1 | 3 | 1 | 0 | 2 | 2 | 2 | 12 | 11 | 2 | 9 | 5 | 6 | 0 | 9 | 5 | 6 | 1 | 1 | 0 | 2 |
772 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/beit/modeling_beit.py | transformers.models.beit.modeling_beit.BeitPatchEmbeddings | from torch import Tensor, nn
import collections.abc
import torch
class BeitPatchEmbeddings(nn.Module):
"""
This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
`hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed... |
class BeitPatchEmbeddings(nn.Module):
'''
This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
`hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
Transformer.
'''
def __init__(self, config):
... | 3 | 1 | 22 | 3 | 18 | 1 | 3 | 0.16 | 1 | 4 | 0 | 0 | 2 | 6 | 2 | 12 | 51 | 8 | 37 | 20 | 30 | 6 | 27 | 16 | 24 | 3 | 1 | 1 | 6 |
773 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/beit/modeling_beit.py | transformers.models.beit.modeling_beit.BeitPooler | from torch import Tensor, nn
from .configuration_beit import BeitConfig
import torch
class BeitPooler(nn.Module):
def __init__(self, config: BeitConfig) -> None:
super().__init__()
self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) if config.use_mean_pooling else None
... |
class BeitPooler(nn.Module):
def __init__(self, config: BeitConfig) -> None:
pass
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
pass | 3 | 0 | 8 | 1 | 6 | 1 | 2 | 0.15 | 1 | 3 | 1 | 0 | 2 | 1 | 2 | 12 | 17 | 2 | 13 | 6 | 10 | 2 | 10 | 6 | 7 | 2 | 1 | 1 | 4 |
774 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/beit/modeling_beit.py | transformers.models.beit.modeling_beit.BeitPreTrainedModel | from ...modeling_utils import PreTrainedModel
from .configuration_beit import BeitConfig
from ...utils import auto_docstring, logging, torch_int
from torch import Tensor, nn
@auto_docstring
class BeitPreTrainedModel(PreTrainedModel):
config: BeitConfig
base_model_prefix = 'beit'
main_input_name = 'pixel_va... | @auto_docstring
class BeitPreTrainedModel(PreTrainedModel):
def _init_weights(self, module):
'''Initialize the weights'''
pass | 3 | 1 | 15 | 0 | 12 | 3 | 6 | 0.35 | 1 | 0 | 0 | 5 | 1 | 0 | 1 | 1 | 29 | 2 | 20 | 9 | 18 | 7 | 18 | 9 | 16 | 6 | 1 | 2 | 6 |
775 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/beit/modeling_beit.py | transformers.models.beit.modeling_beit.BeitPyramidPoolingBlock | import torch
from torch import Tensor, nn
class BeitPyramidPoolingBlock(nn.Module):
def __init__(self, pool_scale: int, in_channels: int, channels: int) -> None:
super().__init__()
self.layers = [nn.AdaptiveAvgPool2d(pool_scale), BeitConvModule(in_channels, channels, kernel_size=1)]
for i,... |
class BeitPyramidPoolingBlock(nn.Module):
def __init__(self, pool_scale: int, in_channels: int, channels: int) -> None:
pass
def forward(self, input: torch.Tensor) -> torch.Tensor:
pass | 3 | 0 | 7 | 0 | 7 | 0 | 2 | 0 | 1 | 6 | 1 | 0 | 2 | 1 | 2 | 12 | 15 | 1 | 14 | 7 | 11 | 0 | 11 | 7 | 8 | 2 | 1 | 1 | 4 |
776 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/beit/modeling_beit.py | transformers.models.beit.modeling_beit.BeitPyramidPoolingModule | import torch
from torch import Tensor, nn
class BeitPyramidPoolingModule(nn.Module):
"""
Pyramid Pooling Module (PPM) used in PSPNet.
Args:
pool_scales (tuple[int]): Pooling scales used in Pooling Pyramid
Module.
in_channels (int): Input channels.
channels (int): Channe... |
class BeitPyramidPoolingModule(nn.Module):
'''
Pyramid Pooling Module (PPM) used in PSPNet.
Args:
pool_scales (tuple[int]): Pooling scales used in Pooling Pyramid
Module.
in_channels (int): Input channels.
channels (int): Channels after modules, before conv_seg.
... | 3 | 1 | 10 | 0 | 10 | 0 | 2 | 0.48 | 1 | 7 | 1 | 0 | 2 | 5 | 2 | 12 | 35 | 4 | 21 | 14 | 18 | 10 | 19 | 14 | 16 | 2 | 1 | 1 | 4 |
777 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/beit/modeling_beit.py | transformers.models.beit.modeling_beit.BeitRelativePositionBias | import torch
from ...utils import auto_docstring, logging, torch_int
from ...pytorch_utils import compile_compatible_method_lru_cache, find_pruneable_heads_and_indices, prune_linear_layer
from torch import Tensor, nn
from .configuration_beit import BeitConfig
class BeitRelativePositionBias(nn.Module):
def __init_... |
class BeitRelativePositionBias(nn.Module):
def __init__(self, config: BeitConfig, window_size: tuple) -> None:
pass
@compile_compatible_method_lru_cache(maxsize=10)
def generate_relative_position_index(self, window_size: tuple[int, int]) -> torch.Tensor:
'''
This method creates the... | 5 | 2 | 27 | 4 | 19 | 6 | 2 | 0.32 | 1 | 6 | 1 | 0 | 3 | 4 | 3 | 13 | 84 | 13 | 59 | 27 | 55 | 19 | 46 | 27 | 42 | 3 | 1 | 1 | 5 |
778 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/beit/modeling_beit.py | transformers.models.beit.modeling_beit.BeitSdpaSelfAttention | import math
import torch
from typing import Optional, Union
class BeitSdpaSelfAttention(BeitSelfAttention):
def forward(self, hidden_states: torch.Tensor, head_mask: Optional[torch.Tensor]=None, output_attentions: bool=False, relative_position_bias: Optional[torch.Tensor]=None, interpolate_pos_encoding: bool=Fals... |
class BeitSdpaSelfAttention(BeitSelfAttention):
def forward(self, hidden_states: torch.Tensor, head_mask: Optional[torch.Tensor]=None, output_attentions: bool=False, relative_position_bias: Optional[torch.Tensor]=None, interpolate_pos_encoding: bool=False, resolution: Optional[tuple[int]]=None) -> Union[tuple[tor... | 2 | 0 | 59 | 4 | 54 | 1 | 6 | 0.02 | 1 | 4 | 0 | 0 | 1 | 0 | 1 | 14 | 60 | 4 | 55 | 20 | 45 | 1 | 24 | 12 | 22 | 6 | 2 | 2 | 6 |
779 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/beit/modeling_beit.py | transformers.models.beit.modeling_beit.BeitSelfAttention | from torch import Tensor, nn
import math
import torch
from .configuration_beit import BeitConfig
from typing import Optional, Union
class BeitSelfAttention(nn.Module):
def __init__(self, config: BeitConfig, window_size: Optional[tuple]=None) -> None:
super().__init__()
self.config = config
... |
class BeitSelfAttention(nn.Module):
def __init__(self, config: BeitConfig, window_size: Optional[tuple]=None) -> None:
pass
def forward(self, hidden_states: torch.Tensor, head_mask: Optional[torch.Tensor]=None, output_attentions: bool=False, relative_position_bias: Optional[torch.Tensor]=None, interp... | 3 | 0 | 26 | 5 | 19 | 2 | 3 | 0.12 | 1 | 8 | 2 | 1 | 3 | 9 | 3 | 13 | 82 | 18 | 57 | 33 | 45 | 7 | 43 | 25 | 39 | 5 | 1 | 1 | 9 |
780 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/beit/modeling_beit.py | transformers.models.beit.modeling_beit.BeitSelfOutput | from .configuration_beit import BeitConfig
import torch
from torch import Tensor, nn
class BeitSelfOutput(nn.Module):
"""
The residual connection is defined in BeitLayer instead of here (as is the case with other models), due to the
layernorm applied before each block.
"""
def __init__(self, confi... |
class BeitSelfOutput(nn.Module):
'''
The residual connection is defined in BeitLayer instead of here (as is the case with other models), due to the
layernorm applied before each block.
'''
def __init__(self, config: BeitConfig) -> None:
pass
def forward(self, hidden_states: torch.Tens... | 3 | 1 | 5 | 1 | 4 | 0 | 1 | 0.44 | 1 | 3 | 1 | 0 | 2 | 2 | 2 | 12 | 16 | 3 | 9 | 5 | 6 | 4 | 9 | 5 | 6 | 1 | 1 | 0 | 2 |
781 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/beit/modeling_beit.py | transformers.models.beit.modeling_beit.BeitUperHead | from .configuration_beit import BeitConfig
import torch
from torch import Tensor, nn
class BeitUperHead(nn.Module):
"""
Unified Perceptual Parsing for Scene Understanding. This head is the implementation of
[UPerNet](https://huggingface.co/papers/1807.10221).
Based on OpenMMLab's implementation, found... |
class BeitUperHead(nn.Module):
'''
Unified Perceptual Parsing for Scene Understanding. This head is the implementation of
[UPerNet](https://huggingface.co/papers/1807.10221).
Based on OpenMMLab's implementation, found in https://github.com/open-mmlab/mmsegmentation.
'''
def __init__(self, conf... | 4 | 1 | 24 | 3 | 19 | 3 | 2 | 0.24 | 1 | 7 | 3 | 0 | 3 | 10 | 3 | 13 | 83 | 13 | 59 | 25 | 55 | 14 | 40 | 25 | 36 | 3 | 1 | 1 | 6 |
782 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bert/configuration_bert.py | transformers.models.bert.configuration_bert.BertConfig | from ...configuration_utils import PretrainedConfig
class BertConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`BertModel`] or a [`TFBertModel`]. It is used to
instantiate a BERT model according to the specified arguments, defining the model architecture. Insta... |
class BertConfig(PretrainedConfig):
'''
This is the configuration class to store the configuration of a [`BertModel`] or a [`TFBertModel`]. It is used to
instantiate a BERT model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will ... | 2 | 1 | 37 | 1 | 36 | 0 | 1 | 1.53 | 1 | 1 | 0 | 0 | 1 | 15 | 1 | 1 | 107 | 11 | 38 | 37 | 17 | 58 | 19 | 18 | 17 | 1 | 1 | 0 | 1 |
783 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bert/configuration_bert.py | transformers.models.bert.configuration_bert.BertOnnxConfig | from collections.abc import Mapping
from ...onnx import OnnxConfig
from collections import OrderedDict
class BertOnnxConfig(OnnxConfig):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
if self.task == 'multiple-choice':
dynamic_axis = {0: 'batch', 1: 'choice', 2: 'sequence'}... |
class BertOnnxConfig(OnnxConfig):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
pass | 3 | 0 | 12 | 0 | 12 | 0 | 2 | 0 | 1 | 3 | 0 | 0 | 1 | 0 | 1 | 1 | 14 | 0 | 14 | 4 | 11 | 0 | 6 | 3 | 4 | 2 | 1 | 1 | 2 |
784 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bert/modeling_bert.py | transformers.models.bert.modeling_bert.BertAttention | from ...cache_utils import Cache, EncoderDecoderCache
import torch
from torch import nn
from ...processing_utils import Unpack
from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
from typing import Callable, Optional, Union
from ...utils import ModelOutput, Trans... |
class BertAttention(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]=None, he... | 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 |
785 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bert/modeling_bert.py | transformers.models.bert.modeling_bert.BertEmbeddings | from typing import Callable, Optional, Union
from torch import nn
import torch
class BertEmbeddings(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, co... |
class BertEmbeddings(nn.Module):
'''Construct the embeddings from word, position and token_type embeddings.'''
def __init__(self, config):
pass
def forward(self, input_ids: Optional[torch.LongTensor]=None, token_type_ids: Optional[torch.LongTensor]=None, position_ids: Optional[torch.LongTensor]=N... | 3 | 1 | 29 | 3 | 23 | 3 | 4 | 0.15 | 1 | 3 | 0 | 0 | 2 | 6 | 2 | 12 | 62 | 8 | 47 | 23 | 37 | 7 | 34 | 16 | 31 | 7 | 1 | 2 | 8 |
786 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bert/modeling_bert.py | transformers.models.bert.modeling_bert.BertEncoder | from ...cache_utils import Cache, EncoderDecoderCache
import torch
from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, BaseModelOutputWithPoolingAndCrossAttentions, CausalLMOutputWithCrossAttentions, MaskedLMOutput, MultipleChoiceModelOutput, NextSentencePredictorOutput, QuestionAnsweringModelOut... |
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 |
787 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bert/modeling_bert.py | transformers.models.bert.modeling_bert.BertForMaskedLM | from ...utils import ModelOutput, TransformersKwargs, auto_docstring, is_torch_flex_attn_available, logging
from typing import Callable, Optional, Union
from ...utils.generic import can_return_tuple, check_model_inputs
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...processing_utils import Unp... | @auto_docstring
class BertForMaskedLM(BertPreTrainedModel):
def __init__(self, config):
pass
def get_output_embeddings(self):
pass
def set_output_embeddings(self, new_embeddings):
pass
@can_return_tuple
@auto_docstring
def forward(self, input_ids: Optional[torch.Tensor... | 11 | 2 | 18 | 2 | 14 | 2 | 2 | 0.11 | 1 | 7 | 3 | 0 | 5 | 2 | 5 | 6 | 105 | 17 | 80 | 33 | 52 | 9 | 36 | 18 | 30 | 5 | 2 | 1 | 11 |
788 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bert/modeling_bert.py | transformers.models.bert.modeling_bert.BertForMultipleChoice | import torch
from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, BaseModelOutputWithPoolingAndCrossAttentions, CausalLMOutputWithCrossAttentions, MaskedLMOutput, MultipleChoiceModelOutput, NextSentencePredictorOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput
f... | @auto_docstring
class BertForMultipleChoice(BertPreTrainedModel):
def __init__(self, config):
pass
@can_return_tuple
@auto_docstring
def forward(self, input_ids: Optional[torch.Tensor]=None, attention_mask: Optional[torch.Tensor]=None, token_type_ids: Optional[torch.Tensor]=None, position_ids: ... | 6 | 1 | 39 | 5 | 31 | 4 | 7 | 0.1 | 1 | 5 | 2 | 0 | 2 | 3 | 2 | 3 | 85 | 10 | 68 | 28 | 47 | 7 | 29 | 15 | 26 | 11 | 2 | 1 | 13 |
789 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bert/modeling_bert.py | transformers.models.bert.modeling_bert.BertForNextSentencePrediction | from ...utils import ModelOutput, TransformersKwargs, auto_docstring, is_torch_flex_attn_available, logging
from typing import Callable, Optional, Union
from ...utils.generic import can_return_tuple, check_model_inputs
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...processing_utils import Unp... | @auto_docstring(custom_intro='\n Bert Model with a `next sentence prediction (classification)` head on top.\n ')
class BertForNextSentencePrediction(BertPreTrainedModel):
def __init__(self, config):
pass
@can_return_tuple
@auto_docstring
def forward(self, input_ids: Optional[torch.Tensor]... | 6 | 1 | 46 | 9 | 27 | 11 | 4 | 0.39 | 1 | 7 | 3 | 0 | 2 | 2 | 2 | 3 | 96 | 18 | 56 | 25 | 38 | 22 | 22 | 11 | 19 | 6 | 2 | 1 | 7 |
790 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bert/modeling_bert.py | transformers.models.bert.modeling_bert.BertForPreTraining | import torch
from ...processing_utils import Unpack
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...utils.generic import can_return_tuple, check_model_inputs
from typing import Callable, Optional, Union
from ...utils import ModelOutput, TransformersKwargs, auto_docstring, is_torch_flex_attn_av... | @auto_docstring(custom_intro='\n Bert Model with two heads on top as done during the pretraining: a `masked language modeling` head and a `next\n sentence prediction (classification)` head.\n ')
class BertForPreTraining(BertPreTrainedModel):
def __init__(self, config):
pass
def get_output_emb... | 8 | 1 | 24 | 4 | 14 | 7 | 2 | 0.45 | 1 | 6 | 3 | 0 | 4 | 2 | 4 | 5 | 102 | 18 | 58 | 30 | 38 | 26 | 27 | 16 | 22 | 5 | 2 | 1 | 8 |
791 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bert/modeling_bert.py | transformers.models.bert.modeling_bert.BertForPreTrainingOutput | import torch
from ...utils import ModelOutput, TransformersKwargs, auto_docstring, is_torch_flex_attn_available, logging
from typing import Callable, Optional, Union
from dataclasses import dataclass
@dataclass
@auto_docstring(custom_intro='\n Output type of [`BertForPreTraining`].\n ')
class BertForPreTrainingO... | @dataclass
@auto_docstring(custom_intro='\n Output type of [`BertForPreTraining`].\n ')
class BertForPreTrainingOutput(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 the ne... | 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 |
792 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bert/modeling_bert.py | transformers.models.bert.modeling_bert.BertForQuestionAnswering | import torch
from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, BaseModelOutputWithPoolingAndCrossAttentions, CausalLMOutputWithCrossAttentions, MaskedLMOutput, MultipleChoiceModelOutput, NextSentencePredictorOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput
f... | @auto_docstring
class BertForQuestionAnswering(BertPreTrainedModel):
def __init__(self, config):
pass
@can_return_tuple
@auto_docstring
def forward(self, input_ids: Optional[torch.Tensor]=None, attention_mask: Optional[torch.Tensor]=None, token_type_ids: Optional[torch.Tensor]=None, position_id... | 6 | 0 | 41 | 5 | 30 | 7 | 4 | 0.18 | 1 | 5 | 2 | 0 | 2 | 3 | 2 | 3 | 94 | 10 | 71 | 30 | 45 | 13 | 32 | 16 | 29 | 7 | 2 | 2 | 8 |
793 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bert/modeling_bert.py | transformers.models.bert.modeling_bert.BertForSequenceClassification | import torch
from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, BaseModelOutputWithPoolingAndCrossAttentions, CausalLMOutputWithCrossAttentions, MaskedLMOutput, MultipleChoiceModelOutput, NextSentencePredictorOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput
f... | @auto_docstring(custom_intro='\n Bert Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled\n output) e.g. for GLUE tasks.\n ')
class BertForSequenceClassification(BertPreTrainedModel):
def __init__(self, config):
pass
@can_return_tuple
... | 6 | 1 | 42 | 4 | 35 | 4 | 7 | 0.09 | 1 | 6 | 2 | 0 | 2 | 5 | 2 | 3 | 94 | 9 | 78 | 28 | 55 | 7 | 36 | 15 | 33 | 12 | 2 | 3 | 14 |
794 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bert/modeling_bert.py | transformers.models.bert.modeling_bert.BertForTokenClassification | import torch
from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, BaseModelOutputWithPoolingAndCrossAttentions, CausalLMOutputWithCrossAttentions, MaskedLMOutput, MultipleChoiceModelOutput, NextSentencePredictorOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput
f... | @auto_docstring
class BertForTokenClassification(BertPreTrainedModel):
def __init__(self, config):
pass
@can_return_tuple
@auto_docstring
def forward(self, input_ids: Optional[torch.Tensor]=None, attention_mask: Optional[torch.Tensor]=None, token_type_ids: Optional[torch.Tensor]=None, position_... | 6 | 1 | 32 | 4 | 26 | 3 | 4 | 0.08 | 1 | 5 | 2 | 0 | 2 | 4 | 2 | 3 | 74 | 9 | 60 | 27 | 37 | 5 | 23 | 14 | 20 | 5 | 2 | 1 | 7 |
795 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bert/modeling_bert.py | transformers.models.bert.modeling_bert.BertIntermediate | from ...activations import ACT2FN
from torch import nn
import torch
class BertIntermediate(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_ac... |
class BertIntermediate(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 |
796 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bert/modeling_bert.py | transformers.models.bert.modeling_bert.BertLMHeadModel | from ...utils.generic import can_return_tuple, check_model_inputs
from typing import Callable, Optional, Union
from ...utils import ModelOutput, TransformersKwargs, auto_docstring, is_torch_flex_attn_available, logging
from ...cache_utils import Cache, EncoderDecoderCache
import torch
from ...modeling_outputs import Ba... | @auto_docstring(custom_intro='\n Bert Model with a `language modeling` head on top for CLM fine-tuning.\n ')
class BertLMHeadModel(BertPreTrainedModel, GenerationMixin):
def __init__(self, config):
pass
def get_output_embeddings(self):
pass
def set_output_embeddings(self, new_embedd... | 8 | 1 | 21 | 2 | 14 | 5 | 2 | 0.29 | 2 | 7 | 3 | 0 | 5 | 2 | 5 | 6 | 117 | 15 | 79 | 34 | 50 | 23 | 33 | 16 | 27 | 6 | 2 | 1 | 12 |
797 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bert/modeling_bert.py | transformers.models.bert.modeling_bert.BertLMPredictionHead | import torch
from torch import nn
class BertLMPredictionHead(nn.Module):
def __init__(self, config):
super().__init__()
self.transform = BertPredictionHeadTransform(config)
self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.bias = nn.Parameter(torch.ze... |
class BertLMPredictionHead(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 |
798 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bert/modeling_bert.py | transformers.models.bert.modeling_bert.BertLayer | from ...modeling_layers import GradientCheckpointingLayer
from ...utils import ModelOutput, TransformersKwargs, auto_docstring, is_torch_flex_attn_available, logging
from typing import Callable, Optional, Union
from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
... |
class BertLayer(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, encoder_... | 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 |
799 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/models/bert/modeling_bert.py | transformers.models.bert.modeling_bert.BertModel | import torch
from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, BaseModelOutputWithPoolingAndCrossAttentions, CausalLMOutputWithCrossAttentions, MaskedLMOutput, MultipleChoiceModelOutput, NextSentencePredictorOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput
f... | null | 12 | 2 | 37 | 4 | 25 | 8 | 5 | 0.35 | 1 | 8 | 4 | 0 | 5 | 6 | 5 | 6 | 211 | 29 | 135 | 45 | 108 | 47 | 65 | 29 | 59 | 21 | 2 | 2 | 27 |
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