text stringlengths 31 243k | type stringclasses 1
value | start int64 36 275k | end int64 286 280k | depth int64 0 1 | filepath stringlengths 85 188 | parent_class stringclasses 3
values | class_index int64 0 10.8k |
|---|---|---|---|---|---|---|---|
class PhiForCausalLM(LlamaForCausalLM):
def __init__(self, config):
super().__init__(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=True) | class_definition | 11,779 | 11,966 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modular_phi.py | null | 9,500 |
class PhiForSequenceClassification(LlamaForSequenceClassification):
pass | class_definition | 11,969 | 12,045 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modular_phi.py | null | 9,501 |
class PhiForTokenClassification(LlamaForTokenClassification):
pass | class_definition | 12,048 | 12,118 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi/modular_phi.py | null | 9,502 |
class BertJapaneseTokenizer(PreTrainedTokenizer):
r"""
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:
voca... | class_definition | 1,906 | 16,166 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py | null | 9,503 |
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_definition | 16,169 | 20,238 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py | null | 9,504 |
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,
sudac... | class_definition | 20,241 | 24,229 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py | null | 9,505 |
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:
... | class_definition | 24,232 | 26,474 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py | null | 9,506 |
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... | class_definition | 26,477 | 27,831 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py | null | 9,507 |
class BasicTokenizer:
"""
Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.).
Args:
do_lower_case (`bool`, *optional*, defaults to `True`):
Whether or not to lowercase the input when tokenizing.
never_split (`Iterable`, *opti... | class_definition | 27,906 | 34,654 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py | null | 9,508 |
class WordpieceTokenizer:
"""Runs WordPiece tokenization."""
def __init__(self, vocab, unk_token, max_input_chars_per_word=100):
self.vocab = vocab
self.unk_token = unk_token
self.max_input_chars_per_word = max_input_chars_per_word
def tokenize(self, text):
"""
Toke... | class_definition | 34,733 | 36,621 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py | null | 9,509 |
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_mod... | class_definition | 36,624 | 39,007 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert_japanese/tokenization_bert_japanese.py | null | 9,510 |
class DecoderConfig(PretrainedConfig):
r"""
Configuration class for FSMT's decoder specific things. note: this is a private helper class
"""
model_type = "fsmt_decoder"
def __init__(self, vocab_size=0, bos_token_id=0):
super().__init__()
self.vocab_size = vocab_size
self.bo... | class_definition | 781 | 1,126 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/configuration_fsmt.py | null | 9,511 |
class FSMTConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`FSMTModel`]. It is used to instantiate a FSMT
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar confi... | class_definition | 1,129 | 10,062 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/configuration_fsmt.py | null | 9,512 |
class PretrainedFSMTModel(PreTrainedModel):
config_class = FSMTConfig
base_model_prefix = "model"
def _init_weights(self, module):
std = self.config.init_std
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
... | class_definition | 14,869 | 15,865 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py | null | 9,513 |
class EncoderLayer(nn.Module):
def __init__(self, config: FSMTConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = Attention(self.embed_dim, config.encoder_attention_heads, dropout=config.attention_dropout)
self.self_attn_layer_norm = LayerNorm(self.embed_dim)... | class_definition | 17,028 | 19,181 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py | null | 9,514 |
class FSMTEncoder(nn.Module):
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a [`EncoderLayer`].
Args:
config: FSMTConfig
"""
def __init__(self, config: FSMTConfig, embed_tokens):
super().__init__()
self.dropout = config.d... | class_definition | 19,184 | 24,575 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py | null | 9,515 |
class DecoderLayer(nn.Module):
def __init__(self, config: FSMTConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = Attention(
embed_dim=self.embed_dim,
num_heads=config.decoder_attention_heads,
dropout=config.attention_dropout,
... | class_definition | 24,578 | 27,607 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py | null | 9,516 |
class FSMTDecoder(nn.Module):
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`DecoderLayer`]
Args:
config: FSMTConfig
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, config: FSMTConfig, embed_tokens: nn.Embedding):
... | class_definition | 27,610 | 35,834 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py | null | 9,517 |
class Attention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
embed_dim,
num_heads,
dropout=0.0,
bias=True,
encoder_decoder_attention=False, # otherwise self_attention
):
super().__init__()
... | class_definition | 36,063 | 43,179 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py | null | 9,518 |
class FSMTModel(PretrainedFSMTModel):
_tied_weights_keys = ["decoder.embed_tokens.weight", "decoder.output_projection.weight"]
def __init__(self, config: FSMTConfig):
super().__init__(config)
padding_idx = config.pad_token_id
encoder_embed_tokens = nn.Embedding(config.src_vocab_size, c... | class_definition | 43,555 | 49,492 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py | null | 9,519 |
class FSMTForConditionalGeneration(PretrainedFSMTModel, GenerationMixin):
base_model_prefix = "model"
_tied_weights_keys = ["decoder.embed_tokens.weight", "decoder.output_projection.weight"]
def __init__(self, config: FSMTConfig):
super().__init__(config)
base_model = FSMTModel(config)
... | class_definition | 49,625 | 54,503 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py | null | 9,520 |
class SinusoidalPositionalEmbedding(nn.Embedding):
"""
This module produces sinusoidal positional embeddings of any length.
We don't want to save the weight of this embedding since it's not trained (deterministic) and it can be huge.
Padding symbols are ignored.
These embeddings get automatically... | class_definition | 54,506 | 57,716 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/modeling_fsmt.py | null | 9,521 |
class FSMTTokenizer(PreTrainedTokenizer):
"""
Construct an FAIRSEQ Transformer tokenizer. Based on Byte-Pair Encoding. The tokenization process is the following:
- Moses preprocessing and tokenization.
- Normalizing all inputs text.
- The arguments `special_tokens` and the function `set_special_tok... | class_definition | 3,315 | 19,232 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fsmt/tokenization_fsmt.py | null | 9,522 |
class XmodEmbeddings(nn.Module):
"""
Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
"""
# Copied from transformers.models.bert.modeling_bert.BertEmbeddings.__init__
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(confi... | class_definition | 1,664 | 5,841 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py | null | 9,523 |
class XmodSelfAttention(nn.Module):
def __init__(self, config, position_embedding_type=None):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size ({config.hidden_size}) i... | class_definition | 5,943 | 13,285 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py | null | 9,524 |
class XmodSelfOutput(nn.Module):
# Copied from transformers.models.roberta.modeling_roberta.RobertaSelfOutput.__init__
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=confi... | class_definition | 13,288 | 13,968 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py | null | 9,525 |
class XmodAttention(nn.Module):
def __init__(self, config, position_embedding_type=None):
super().__init__()
self.self = XmodSelfAttention(config, position_embedding_type=position_embedding_type)
self.output = XmodSelfOutput(config)
self.pruned_heads = set()
self.pre_norm = c... | class_definition | 13,971 | 16,384 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py | null | 9,526 |
class XmodIntermediate(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_act_fn = ACT2FN[config.hidden_act]
else:
self.interm... | class_definition | 16,466 | 17,031 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py | null | 9,527 |
class XmodAdapter(nn.Module):
def __init__(self, config):
super().__init__()
self.bottleneck_size = config.hidden_size // config.adapter_reduction_factor
self.dense1 = nn.Linear(config.hidden_size, self.bottleneck_size)
self.dense2 = nn.Linear(self.bottleneck_size, config.hidden_size... | class_definition | 17,034 | 17,787 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py | null | 9,528 |
class XmodOutput(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.ln_before_adapter = config.ln_before_adapter
self... | class_definition | 17,790 | 20,107 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py | null | 9,529 |
class XmodLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = XmodAttention(config)
self.is_decoder = config.is_decoder
self.add_cross_attention = config.a... | class_definition | 20,110 | 24,318 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py | null | 9,530 |
class XmodEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([XmodLayer(config) for _ in range(config.num_hidden_layers)])
self.is_pre_norm = config.pre_norm
if self.is_pre_norm:
self.LayerNorm = nn.L... | class_definition | 24,321 | 28,451 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py | null | 9,531 |
class XmodPooler(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
# We "pool" the model by simply taking the hidde... | class_definition | 28,527 | 29,086 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py | null | 9,532 |
class XmodPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = XmodConfig
base_model_prefix = "roberta"
supports_gradient_checkpointing = True
# Copied from tran... | class_definition | 29,089 | 31,617 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py | null | 9,533 |
class XmodModel(XmodPreTrainedModel):
"""
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*_ by Ashish Vaswani, Noam... | class_definition | 35,535 | 45,448 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py | null | 9,534 |
class XmodForCausalLM(XmodPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.decoder.weight", "lm_head.decoder.bias"]
# Copied from transformers.models.roberta.modeling_roberta.RobertaForCausalLM.__init__ with Roberta->Xmod
def __init__(self, config):
super().__init__(config)
... | class_definition | 45,581 | 52,623 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py | null | 9,535 |
class XmodForMaskedLM(XmodPreTrainedModel):
_tied_weights_keys = ["lm_head.decoder.weight", "lm_head.decoder.bias"]
# Copied from transformers.models.roberta.modeling_roberta.RobertaForMaskedLM.__init__ with Roberta->Xmod
def __init__(self, config):
super().__init__(config)
if config.is_de... | class_definition | 52,740 | 56,646 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py | null | 9,536 |
class XmodLMHead(nn.Module):
"""Roberta Head for masked language modeling."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.decoder... | class_definition | 56,722 | 57,781 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py | null | 9,537 |
class XmodForSequenceClassification(XmodPreTrainedModel):
# Copied from transformers.models.roberta.modeling_roberta.RobertaForSequenceClassification.__init__ with Roberta->Xmod
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
... | class_definition | 58,004 | 61,830 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py | null | 9,538 |
class XmodForMultipleChoice(XmodPreTrainedModel):
# Copied from transformers.models.roberta.modeling_roberta.RobertaForMultipleChoice.__init__ with Roberta->Xmod
def __init__(self, config):
super().__init__(config)
self.roberta = XmodModel(config)
self.dropout = nn.Dropout(config.hidden... | class_definition | 62,062 | 65,790 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py | null | 9,539 |
class XmodForTokenClassification(XmodPreTrainedModel):
# Copied from transformers.models.roberta.modeling_roberta.RobertaForTokenClassification.__init__ with Roberta->Xmod
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.roberta = XmodModel(c... | class_definition | 66,020 | 68,944 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py | null | 9,540 |
class XmodClassificationHead(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 = (
config.classifier_dropout if config.classifier_dr... | class_definition | 69,032 | 69,802 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py | null | 9,541 |
class XmodForQuestionAnswering(XmodPreTrainedModel):
# Copied from transformers.models.roberta.modeling_roberta.RobertaForQuestionAnswering.__init__ with Roberta->Xmod
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.roberta = XmodModel(confi... | class_definition | 70,090 | 74,369 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py | null | 9,542 |
class XmodConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`XmodModel`]. It is used to instantiate an X-MOD
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar con... | class_definition | 943 | 8,560 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/configuration_xmod.py | null | 9,543 |
class XmodOnnxConfig(OnnxConfig):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
else:
dynamic_axis = {0: "batch", 1: "sequence"}
return OrderedDict(
... | class_definition | 8,664 | 9,109 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/configuration_xmod.py | null | 9,544 |
class VitPoseBackboneConfig(BackboneConfigMixin, PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`VitPoseBackbone`]. It is used to instantiate a
VitPose model according to the specified arguments, defining the model architecture. Instantiating a configuration
wit... | class_definition | 887 | 6,612 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose_backbone/configuration_vitpose_backbone.py | null | 9,545 |
class VitPoseBackbonePatchEmbeddings(nn.Module):
"""Image to Patch Embedding."""
def __init__(self, config):
super().__init__()
image_size = config.image_size
patch_size = config.patch_size
num_channels = config.num_channels
embed_dim = config.hidden_size
image... | class_definition | 1,603 | 2,945 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose_backbone/modeling_vitpose_backbone.py | null | 9,546 |
class VitPoseBackboneEmbeddings(nn.Module):
"""
Construct the position and patch embeddings.
"""
def __init__(self, config: VitPoseBackboneConfig) -> None:
super().__init__()
self.patch_embeddings = VitPoseBackbonePatchEmbeddings(config)
num_patches = self.patch_embeddings.num_... | class_definition | 2,948 | 3,786 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose_backbone/modeling_vitpose_backbone.py | null | 9,547 |
class VitPoseBackboneSelfAttention(nn.Module):
def __init__(self, config: VitPoseBackboneConfig) -> None:
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size {config.hidd... | class_definition | 3,883 | 6,747 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose_backbone/modeling_vitpose_backbone.py | null | 9,548 |
class VitPoseBackboneSelfOutput(nn.Module):
"""
The residual connection is defined in VitPoseBackboneLayer instead of here (as is the case with other models), due to the
layernorm applied before each block.
"""
def __init__(self, config: VitPoseBackboneConfig) -> None:
super().__init__()
... | class_definition | 6,841 | 7,520 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose_backbone/modeling_vitpose_backbone.py | null | 9,549 |
class VitPoseBackboneAttention(nn.Module):
def __init__(self, config: VitPoseBackboneConfig) -> None:
super().__init__()
self.attention = VitPoseBackboneSelfAttention(config)
self.output = VitPoseBackboneSelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads: S... | class_definition | 7,613 | 9,338 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose_backbone/modeling_vitpose_backbone.py | null | 9,550 |
class VitPoseBackboneMoeMLP(nn.Module):
def __init__(self, config: VitPoseBackboneConfig):
super().__init__()
in_features = out_features = config.hidden_size
hidden_features = int(config.hidden_size * config.mlp_ratio)
num_experts = config.num_experts
part_features = config... | class_definition | 9,341 | 10,895 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose_backbone/modeling_vitpose_backbone.py | null | 9,551 |
class VitPoseBackboneMLP(nn.Module):
def __init__(self, config: VitPoseBackboneConfig) -> None:
super().__init__()
in_features = out_features = config.hidden_size
hidden_features = int(config.hidden_size * config.mlp_ratio)
self.fc1 = nn.Linear(in_features, hidden_features, bias=True... | class_definition | 10,898 | 11,583 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose_backbone/modeling_vitpose_backbone.py | null | 9,552 |
class VitPoseBackboneLayer(nn.Module):
def __init__(self, config: VitPoseBackboneConfig) -> None:
super().__init__()
self.num_experts = config.num_experts
self.attention = VitPoseBackboneAttention(config)
self.mlp = VitPoseBackboneMLP(config) if self.num_experts == 1 else VitPoseBack... | class_definition | 11,586 | 13,640 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose_backbone/modeling_vitpose_backbone.py | null | 9,553 |
class VitPoseBackboneEncoder(nn.Module):
def __init__(self, config: VitPoseBackboneConfig) -> None:
super().__init__()
self.config = config
self.layer = nn.ModuleList([VitPoseBackboneLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
# I... | class_definition | 13,731 | 15,810 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose_backbone/modeling_vitpose_backbone.py | null | 9,554 |
class VitPoseBackbonePreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = VitPoseBackboneConfig
base_model_prefix = "vit"
main_input_name = "pixel_values"
support... | class_definition | 15,813 | 17,325 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose_backbone/modeling_vitpose_backbone.py | null | 9,555 |
class VitPoseBackbone(VitPoseBackbonePreTrainedModel, BackboneMixin):
def __init__(self, config: VitPoseBackboneConfig):
super().__init__(config)
super()._init_backbone(config)
self.num_features = [config.hidden_size for _ in range(config.num_hidden_layers + 1)]
self.embeddings = Vi... | class_definition | 19,360 | 22,743 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose_backbone/modeling_vitpose_backbone.py | null | 9,556 |
class PersimmonConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`PersimmonModel`]. It is used to instantiate an
Persimmon model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield... | class_definition | 857 | 9,116 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/configuration_persimmon.py | null | 9,557 |
class PersimmonRotaryEmbedding(nn.Module):
def __init__(self, config: PersimmonConfig, device=None):
super().__init__()
# BC: "rope_type" was originally "type"
if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
self.rope_type = config.rope_scaling.get("rope_t... | class_definition | 2,033 | 5,236 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py | null | 9,558 |
class PersimmonMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.dense_h_to_4h = nn.Linear(config.hidden_size, config.intermediate_size)
self.dense_4h_to_h = nn.Linear(config.intermediate_size, config.hidden_size)
self.act = ACT2FN[config.hidden_act]
def forwar... | class_definition | 7,203 | 7,739 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py | null | 9,559 |
class PersimmonAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: PersimmonConfig, layer_idx: Optional[int] = None):
super().__init__()
self.config = config
self.layer_idx = layer_idx
if layer_idx is None:
... | class_definition | 7,742 | 14,081 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py | null | 9,560 |
class PersimmonDecoderLayer(nn.Module):
def __init__(self, config: PersimmonConfig, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = PersimmonAttention(config=config, layer_idx=layer_idx)
self.mlp = PersimmonMLP(config)
self.input_lay... | class_definition | 14,084 | 17,993 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py | null | 9,561 |
class PersimmonPreTrainedModel(PreTrainedModel):
config_class = PersimmonConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["PersimmonDecoderLayer"]
_skip_keys_device_placement = "past_key_values"
_supports_cache_class = True
_supports_quantized_ca... | class_definition | 19,031 | 19,873 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py | null | 9,562 |
class PersimmonModel(PersimmonPreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`PersimmonDecoderLayer`]
Args:
config: PersimmonConfig
"""
def __init__(self, config: PersimmonConfig):
super().__init__(config)
self.pa... | class_definition | 24,697 | 37,130 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py | null | 9,563 |
class PersimmonForCausalLM(PersimmonPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.__init__ with LLAMA->PERSIMMON,Llama->Persimmon
def __init__(self, config):
super().__init__(config)
self.mo... | class_definition | 37,133 | 43,197 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py | null | 9,564 |
class PersimmonForSequenceClassification(PersimmonPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = PersimmonModel(config)
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
# Initiali... | class_definition | 44,121 | 47,949 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py | null | 9,565 |
class PersimmonForTokenClassification(PersimmonPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = PersimmonModel(config)
if getattr(config, "classifier_dropout", None) is not None:
classifier_dropout = c... | class_definition | 48,327 | 51,555 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py | null | 9,566 |
class GPTNeoXJapaneseTokenizer(PreTrainedTokenizer):
"""
This tokenizer inherits from [`PreTrainedTokenizer`] and is based on Japanese special Sub-Word-Encoding that is
used in this repository (https://github.com/tanreinama/Japanese-BPEEncoder_V2). Check the repository for details.
Japanese has a relati... | class_definition | 1,740 | 8,428 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/tokenization_gpt_neox_japanese.py | null | 9,567 |
class SubWordJapaneseTokenizer:
"""
https://github.com/tanreinama/Japanese-BPEEncoder_V2 This tokenizer class is under MIT Lisence according to the
original repository.
MIT License
Copyright (c) 2020 tanreinama
Permission is hereby granted, free of charge, to any person obtaining a copy of th... | class_definition | 8,431 | 16,208 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/tokenization_gpt_neox_japanese.py | null | 9,568 |
class GPTNeoXJapaneseConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`GPTNeoXModelJapanese`]. It is used to instantiate
a GPTNeoX model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults w... | class_definition | 867 | 9,083 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/configuration_gpt_neox_japanese.py | null | 9,569 |
class GPTNeoXJapanesePreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = GPTNeoXJapaneseConfig
base_model_prefix = "gpt_neox_japanese"
_no_split_modules = ["GPTNeoXJ... | class_definition | 1,565 | 2,704 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py | null | 9,570 |
class GPTNeoXJapaneseAttention(nn.Module):
def __init__(self, config, use_bias=False, layer_idx=None):
super().__init__()
self.num_attention_heads = config.num_attention_heads
self.hidden_size = config.hidden_size
self.head_size = self.hidden_size // self.num_attention_heads
... | class_definition | 2,707 | 9,381 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py | null | 9,571 |
class GPTNeoXJapaneseRotaryEmbedding(nn.Module):
def __init__(self, config: GPTNeoXJapaneseConfig, device=None):
super().__init__()
# BC: "rope_type" was originally "type"
if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
self.rope_type = config.rope_scaling... | class_definition | 9,498 | 12,713 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py | null | 9,572 |
class GPTNeoXJapaneseMLP(nn.Module):
def __init__(self, config):
super().__init__()
intermediate_size = int(config.hidden_size * config.intermediate_multiple_size)
self.dense_h_to_4h = nn.Linear(config.hidden_size, intermediate_size, bias=False)
# Project back to h.
self.dens... | class_definition | 15,218 | 15,869 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py | null | 9,573 |
class GPTNeoXJapaneseLayer(nn.Module):
def __init__(self, config, layer_number):
super().__init__()
self.layer_number = layer_number
self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, eps... | class_definition | 15,872 | 18,555 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py | null | 9,574 |
class GPTNeoXJapaneseModel(GPTNeoXJapanesePreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.embed_in = nn.Embedding(config.vocab_size, config.hidden_size)
self.layers = nn.ModuleList(
[GPTNeoXJapaneseLayer(config=config, lay... | class_definition | 23,484 | 36,033 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py | null | 9,575 |
class GPTNeoXJapaneseForCausalLM(GPTNeoXJapanesePreTrainedModel, GenerationMixin):
_tied_weights_keys = ["embed_out.weight"]
def __init__(self, config):
super().__init__(config)
self.config = config
self.gpt_neox_japanese = GPTNeoXJapaneseModel(config)
self.embed_out = nn.Linea... | class_definition | 36,206 | 40,917 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py | null | 9,576 |
class TFLayoutLMEmbeddings(keras.layers.Layer):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config: LayoutLMConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.hidden_size = config.hidden_size
self.max_pos... | class_definition | 1,767 | 7,467 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py | null | 9,577 |
class TFLayoutLMSelfAttention(keras.layers.Layer):
def __init__(self, config: LayoutLMConfig, **kwargs):
super().__init__(**kwargs)
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of th... | class_definition | 7,566 | 14,395 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py | null | 9,578 |
class TFLayoutLMSelfOutput(keras.layers.Layer):
def __init__(self, config: LayoutLMConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
self.L... | class_definition | 14,491 | 15,826 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py | null | 9,579 |
class TFLayoutLMAttention(keras.layers.Layer):
def __init__(self, config: LayoutLMConfig, **kwargs):
super().__init__(**kwargs)
self.self_attention = TFLayoutLMSelfAttention(config, name="self")
self.dense_output = TFLayoutLMSelfOutput(config, name="output")
def prune_heads(self, heads... | class_definition | 15,921 | 17,769 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py | null | 9,580 |
class TFLayoutLMIntermediate(keras.layers.Layer):
def __init__(self, config: LayoutLMConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
... | class_definition | 17,867 | 18,897 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py | null | 9,581 |
class TFLayoutLMOutput(keras.layers.Layer):
def __init__(self, config: LayoutLMConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
self.Layer... | class_definition | 18,989 | 20,326 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py | null | 9,582 |
class TFLayoutLMLayer(keras.layers.Layer):
def __init__(self, config: LayoutLMConfig, **kwargs):
super().__init__(**kwargs)
self.attention = TFLayoutLMAttention(config, name="attention")
self.is_decoder = config.is_decoder
self.add_cross_attention = config.add_cross_attention
... | class_definition | 20,417 | 25,170 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py | null | 9,583 |
class TFLayoutLMEncoder(keras.layers.Layer):
def __init__(self, config: LayoutLMConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.layer = [TFLayoutLMLayer(config, name=f"layer_._{i}") for i in range(config.num_hidden_layers)]
def call(
self,
hidden_... | class_definition | 25,263 | 28,356 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py | null | 9,584 |
class TFLayoutLMPooler(keras.layers.Layer):
def __init__(self, config: LayoutLMConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.hidden_size,
kernel_initializer=get_initializer(config.initializer_range),
activation="tanh"... | class_definition | 28,448 | 29,425 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py | null | 9,585 |
class TFLayoutLMPredictionHeadTransform(keras.layers.Layer):
def __init__(self, config: LayoutLMConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.hidden_size,
kernel_initializer=get_initializer(config.initializer_range),
... | class_definition | 29,534 | 30,939 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py | null | 9,586 |
class TFLayoutLMLMPredictionHead(keras.layers.Layer):
def __init__(self, config: LayoutLMConfig, input_embeddings: keras.layers.Layer, **kwargs):
super().__init__(**kwargs)
self.config = config
self.hidden_size = config.hidden_size
self.transform = TFLayoutLMPredictionHeadTransform... | class_definition | 31,041 | 33,012 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py | null | 9,587 |
class TFLayoutLMMLMHead(keras.layers.Layer):
def __init__(self, config: LayoutLMConfig, input_embeddings: keras.layers.Layer, **kwargs):
super().__init__(**kwargs)
self.predictions = TFLayoutLMLMPredictionHead(config, input_embeddings, name="predictions")
def call(self, sequence_output: tf.Ten... | class_definition | 33,105 | 33,820 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py | null | 9,588 |
class TFLayoutLMMainLayer(keras.layers.Layer):
config_class = LayoutLMConfig
def __init__(self, config: LayoutLMConfig, add_pooling_layer: bool = True, **kwargs):
super().__init__(**kwargs)
self.config = config
self.embeddings = TFLayoutLMEmbeddings(config, name="embeddings")
... | class_definition | 33,843 | 40,139 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py | null | 9,589 |
class TFLayoutLMPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = LayoutLMConfig
base_model_prefix = "layoutlm"
@property
def input_signature(self):
... | class_definition | 40,142 | 40,617 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py | null | 9,590 |
class TFLayoutLMModel(TFLayoutLMPreTrainedModel):
def __init__(self, config: LayoutLMConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.layoutlm = TFLayoutLMMainLayer(config, name="layoutlm")
@unpack_inputs
@add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DO... | class_definition | 46,375 | 49,799 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py | null | 9,591 |
class TFLayoutLMForMaskedLM(TFLayoutLMPreTrainedModel, TFMaskedLanguageModelingLoss):
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
_keys_to_ignore_on_load_unexpected = [
r"pooler",
r"cls.seq_relationship",
r"cls.predicti... | class_definition | 49,912 | 55,440 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py | null | 9,592 |
class TFLayoutLMForSequenceClassification(TFLayoutLMPreTrainedModel, TFSequenceClassificationLoss):
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
_keys_to_ignore_on_load_unexpected = [r"mlm___cls", r"nsp___cls", r"cls.predictions", r"cls.seq_rel... | class_definition | 55,670 | 60,995 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py | null | 9,593 |
class TFLayoutLMForTokenClassification(TFLayoutLMPreTrainedModel, TFTokenClassificationLoss):
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
_keys_to_ignore_on_load_unexpected = [
r"pooler",
r"mlm___cls",
r"nsp___cls",
... | class_definition | 61,232 | 66,453 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py | null | 9,594 |
class TFLayoutLMForQuestionAnswering(TFLayoutLMPreTrainedModel, TFQuestionAnsweringLoss):
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
_keys_to_ignore_on_load_unexpected = [
r"pooler",
r"mlm___cls",
r"nsp___cls",
... | class_definition | 66,794 | 73,115 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py | null | 9,595 |
class LayoutLMTokenizer(PreTrainedTokenizer):
r"""
Construct a LayoutLM 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:
voc... | class_definition | 1,788 | 12,469 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/tokenization_layoutlm.py | null | 9,596 |
class BasicTokenizer:
"""
Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.).
Args:
do_lower_case (`bool`, *optional*, defaults to `True`):
Whether or not to lowercase the input when tokenizing.
never_split (`Iterable`, *opti... | class_definition | 12,544 | 19,292 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/tokenization_layoutlm.py | null | 9,597 |
class WordpieceTokenizer:
"""Runs WordPiece tokenization."""
def __init__(self, vocab, unk_token, max_input_chars_per_word=100):
self.vocab = vocab
self.unk_token = unk_token
self.max_input_chars_per_word = max_input_chars_per_word
def tokenize(self, text):
"""
Toke... | class_definition | 19,371 | 21,259 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/tokenization_layoutlm.py | null | 9,598 |
class LayoutLMConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`LayoutLMModel`]. It is used to instantiate a
LayoutLM model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a s... | class_definition | 952 | 6,718 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/configuration_layoutlm.py | null | 9,599 |
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