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 Blip2ProcessorKwargs(ProcessingKwargs, total=False):
_defaults = {
"text_kwargs": {
"add_special_tokens": True,
"padding": False,
"stride": 0,
"return_overflowing_tokens": False,
"return_special_tokens_mask": False,
"return_offset... | class_definition | 1,033 | 1,526 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/processing_blip_2.py | null | 3,100 |
class Blip2Processor(ProcessorMixin):
r"""
Constructs a BLIP-2 processor which wraps a BLIP image processor and an OPT/T5 tokenizer into a single processor.
[`BlipProcessor`] offers all the functionalities of [`BlipImageProcessor`] and [`AutoTokenizer`]. See the docstring
of [`~BlipProcessor.__call__`]... | class_definition | 1,529 | 8,869 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/processing_blip_2.py | null | 3,101 |
class FlavaModelOutput(ModelOutput):
"""
Output from FlavaModel containing embeddings and outputs from individual encoders.
Note that `image_embeddings` and `text_embeddigns` returned are similar to pooled output returned from a
transformer. If you want embeddings for contrastive loss or retrieval use ... | class_definition | 2,021 | 4,376 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py | null | 3,102 |
class FlavaLosses(ModelOutput):
"""Class representing pretraining losses from FLAVA model
Args:
mim (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `mim_labels` and `pixel_values` are present, `input_ids_masked` is absent and `mim_weight` > 0.:
Masked Image Modeling loss as ... | class_definition | 4,390 | 6,747 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py | null | 3,103 |
class FlavaForPreTrainingOutput(ModelOutput):
"""
Output from FlavaForPreTraining containing embeddings, and outputs from individual encoders.
Note that `image_embeddings` and `text_embeddings` returned are similar to pooled output returned from a
transformer. If you want embeddings for contrastive los... | class_definition | 6,761 | 14,615 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py | null | 3,104 |
class FlavaImageEmbeddings(nn.Module):
"""
Construct the CLS token, position and patch embeddings. Optionally, also the mask token.
"""
def __init__(self, config: FlavaImageConfig, use_mask_token: bool = False) -> None:
super().__init__()
use_mask_token = use_mask_token or config.mask_... | class_definition | 14,772 | 19,116 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py | null | 3,105 |
class PatchEmbeddings(nn.Module):
"""
Image to Patch Embedding.
"""
def __init__(
self,
image_size: int = 224,
patch_size: Union[int, Tuple[int, int]] = 16,
num_channels: int = 3,
embed_dim: int = 768,
):
super().__init__()
if not isinstance(i... | class_definition | 19,273 | 20,683 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py | null | 3,106 |
class FlavaTextEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.position_em... | class_definition | 20,686 | 23,544 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py | null | 3,107 |
class FlavaSelfAttention(nn.Module):
def __init__(self, config: FlavaPossibleConfigs) -> 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 | 23,547 | 26,703 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py | null | 3,108 |
class FlavaSelfOutput(nn.Module):
"""
The residual connection is defined in FlavaLayer (same as ViTLayer) instead of here (as is the case with other
models), due to the layernorm applied before each block.
"""
def __init__(self, config: FlavaPossibleConfigs) -> None:
super().__init__()
... | class_definition | 26,706 | 27,383 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py | null | 3,109 |
class FlavaAttention(nn.Module):
def __init__(self, config: FlavaPossibleConfigs) -> None:
super().__init__()
self.attention = FlavaSelfAttention(config)
self.output = FlavaSelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads: Set[int]) -> None:
if le... | class_definition | 27,386 | 29,216 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py | null | 3,110 |
class FlavaIntermediate(nn.Module):
def __init__(self, config: FlavaPossibleConfigs) -> None:
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]
... | class_definition | 29,219 | 29,895 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py | null | 3,111 |
class FlavaOutput(nn.Module):
def __init__(self, config: FlavaPossibleConfigs) -> None:
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
# Copied from transformers.models.vit.modeling_vit.ViTOut... | class_definition | 29,898 | 30,511 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py | null | 3,112 |
class FlavaLayer(nn.Module):
"""This corresponds to the Block class in the timm implementation."""
def __init__(self, config: FlavaPossibleConfigs) -> None:
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = Fla... | class_definition | 30,514 | 32,367 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py | null | 3,113 |
class FlavaEncoder(nn.Module):
def __init__(self, config: FlavaConfig) -> None:
super().__init__()
self.config = config
self.layer = nn.ModuleList([FlavaLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
... | class_definition | 32,370 | 34,379 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py | null | 3,114 |
class FlavaPooler(nn.Module):
def __init__(self, config: FlavaPossibleConfigs):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hidden_states: torch.Tensor):
# We "pool" the model by simply taking th... | class_definition | 34,382 | 34,948 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py | null | 3,115 |
class FlavaPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = FlavaConfig
base_model_prefix = "flava"
supports_gradient_checkpointing = True
def _init_weights(... | class_definition | 42,224 | 43,395 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py | null | 3,116 |
class FlavaImageModel(FlavaPreTrainedModel):
config_class = FlavaImageConfig
# This override allows us to load FlavaImageModel from FlavaModel/FlavaForPreTraining checkpoints.
base_model_prefix = "flava.image_model"
main_input_name = "pixel_values"
def __init__(self, config: FlavaImageConfig, add_p... | class_definition | 43,593 | 47,629 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py | null | 3,117 |
class FlavaTextModel(FlavaPreTrainedModel):
config_class = FlavaTextConfig
# This override allows us to load FlavaTextModel from FlavaModel/FlavaForPreTraining checkpoints.
base_model_prefix = "flava.text_model"
def __init__(self, config: FlavaTextConfig, add_pooling_layer: bool = True):
super(... | class_definition | 47,825 | 52,037 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py | null | 3,118 |
class FlavaMultimodalModel(FlavaPreTrainedModel):
config_class = FlavaMultimodalConfig
# This override allows us to load FlavaMultimodalModel from FlavaModel/FlavaForPreTraining checkpoints.
base_model_prefix = "flava.multimodal_model"
main_input_name = "hidden_states"
def __init__(self, config: Fl... | class_definition | 52,245 | 56,333 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py | null | 3,119 |
class FlavaModel(FlavaPreTrainedModel):
config_class = FlavaConfig
def __init__(self, config: FlavaConfig):
super().__init__(config)
if not isinstance(config.text_config, FlavaTextConfig):
raise TypeError(
"config.text_config is expected to be of type FlavaTextConfi... | class_definition | 56,520 | 67,567 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py | null | 3,120 |
class FlavaImageCodebookResPath(nn.Module):
def __init__(self, in_size: int, out_size: int, **kwargs):
super().__init__()
hid_size = out_size // 4
path = OrderedDict()
path["relu_1"] = nn.ReLU()
path["conv_1"] = nn.Conv2d(in_size, hid_size, kernel_size=3, padding=1)
... | class_definition | 67,570 | 68,355 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py | null | 3,121 |
class FlavaImageCodebookBlock(nn.Module):
def __init__(self, in_size: int, out_size: int, num_layers: int, **kwargs):
super().__init__()
self.post_gain = 1 / (num_layers**2)
if in_size != out_size:
self.id_path = nn.Conv2d(in_size, out_size, kernel_size=1, padding=0)
el... | class_definition | 68,358 | 68,916 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py | null | 3,122 |
class FlavaImageCodebookLayerGroup(nn.Module):
def __init__(self, num_blocks: int, num_layers: int, in_size: int, out_size: int, use_pool: bool = True):
super().__init__()
blocks = OrderedDict()
for i in range(num_blocks):
if i == 0:
blocks[f"block_{i+1}"] = Flava... | class_definition | 68,919 | 69,612 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py | null | 3,123 |
class FlavaImageCodebook(FlavaPreTrainedModel):
base_model_prefix = ""
config_class = FlavaImageCodebookConfig
main_input_name = "pixel_values"
supports_gradient_checkpointing = False
def __init__(
self,
config: FlavaImageCodebookConfig,
**kwargs: Any,
):
super()... | class_definition | 70,143 | 74,981 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py | null | 3,124 |
class FlavaPredictionHeadTransform(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
if isinstance(config.hidden_act, str):
self.transform_act_fn = ACT2FN[config.hidden_act]
else:
self.tra... | class_definition | 74,984 | 75,655 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py | null | 3,125 |
class FlavaMaskedPredictionHead(nn.Module):
def __init__(self, config, weight=None):
super().__init__()
self.config = config
self.transform = FlavaPredictionHeadTransform(config)
self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.bias = nn.Parame... | class_definition | 75,658 | 76,410 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py | null | 3,126 |
class FlavaITMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.pooler = FlavaPooler(config)
self.seq_relationship = nn.Linear(config.hidden_size, 2)
def forward(self, x):
x = self.pooler(x)
x = self.seq_relationship(x)
... | class_definition | 76,413 | 76,746 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py | null | 3,127 |
class FlavaGlobalContrastiveHead(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.global_backprop_contrastive = config.global_backprop_contrastive
def forward(self, image_embeddings, text_embeddings, logit_scale):
temperature = torch.exp(logit... | class_definition | 76,749 | 78,903 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py | null | 3,128 |
class FlavaForPreTraining(FlavaPreTrainedModel):
# Those are linked to xxx.bias
_tied_weights_keys = [
"mmm_text_head.decoder.bias",
"mmm_image_head.decoder.bias",
"mlm_head.decoder.bias",
"mim_head.decoder.bias",
]
def __init__(self, config: FlavaConfig, image_codebook:... | class_definition | 79,148 | 96,511 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/modeling_flava.py | null | 3,129 |
class FlavaFeatureExtractor(FlavaImageProcessor):
def __init__(self, *args, **kwargs) -> None:
warnings.warn(
"The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
" use FlavaImageProcessor instead.",
FutureWarning,
... | class_definition | 834 | 1,200 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/feature_extraction_flava.py | null | 3,130 |
class FlavaImageConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`FlavaImageModel`]. It is used to instantiate an
FLAVA model according to the specified arguments, defining the model architecture.
Instantiating a configuration with the defaults will yield ... | class_definition | 835 | 5,587 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/configuration_flava.py | null | 3,131 |
class FlavaTextConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`FlavaTextModel`]. It is used to instantiate an
FLAVA model according to the specified arguments, defining the model architecture.
Instantiating a configuration with the defaults will yield a ... | class_definition | 5,590 | 11,547 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/configuration_flava.py | null | 3,132 |
class FlavaMultimodalConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`FlavaMultimodalModel`]. It is used to instantiate
an FLAVA model according to the specified arguments, defining the model architecture.
Instantiating a configuration with the defaults w... | class_definition | 11,550 | 15,558 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/configuration_flava.py | null | 3,133 |
class FlavaImageCodebookConfig(PretrainedConfig):
model_type = "flava_image_codebook"
base_config_key = "image_codebook_config"
r"""
[`FlavaImageCodebookConfig`] is the configuration class to store the configuration of a [`FlavaImageCodebook`]. It
is used to instantiate an FLAVA model according to ... | class_definition | 15,561 | 18,469 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/configuration_flava.py | null | 3,134 |
class FlavaConfig(PretrainedConfig):
r"""
[`FlavaConfig`] is the configuration class to store the configuration of a [`FlavaModel`]. It is used to
instantiate FLAVA model according to the specified arguments, defining the text model, image model, image codebook
and multimodal model configs. Instantiatin... | class_definition | 18,472 | 33,944 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/configuration_flava.py | null | 3,135 |
class FlavaMaskingGenerator:
def __init__(
self,
input_size: Union[int, Tuple[int, int]] = 14,
total_mask_patches: int = 75,
mask_group_max_patches: Optional[int] = None,
mask_group_min_patches: int = 16,
mask_group_min_aspect_ratio: Optional[float] = 0.3,
mas... | class_definition | 1,741 | 4,782 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/image_processing_flava.py | null | 3,136 |
class FlavaImageProcessor(BaseImageProcessor):
r"""
Constructs a Flava 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
`do_resize` parameter ... | class_definition | 4,785 | 37,400 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/image_processing_flava.py | null | 3,137 |
class FlavaProcessor(ProcessorMixin):
r"""
Constructs a FLAVA processor which wraps a FLAVA image processor and a FLAVA tokenizer into a single processor.
[`FlavaProcessor`] offers all the functionalities of [`FlavaImageProcessor`] and [`BertTokenizerFast`]. See the
[`~FlavaProcessor.__call__`] and [`~... | class_definition | 991 | 6,831 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/processing_flava.py | null | 3,138 |
class AlbertConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`AlbertModel`] or a [`TFAlbertModel`]. It is used
to instantiate an ALBERT model according to the specified arguments, defining the model architecture. Instantiating
a configuration with the defau... | class_definition | 892 | 7,493 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/configuration_albert.py | null | 3,139 |
class AlbertOnnxConfig(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 | 7,590 | 8,087 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/configuration_albert.py | null | 3,140 |
class AlbertEmbeddings(nn.Module):
"""
Construct the embeddings from word, position and token_type embeddings.
"""
def __init__(self, config: AlbertConfig):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.embedding_size, padding_idx=config.pad_token_id)
... | class_definition | 6,728 | 10,020 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py | null | 3,141 |
class AlbertAttention(nn.Module):
def __init__(self, config: AlbertConfig):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple o... | class_definition | 10,023 | 16,209 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py | null | 3,142 |
class AlbertSdpaAttention(AlbertAttention):
def __init__(self, config):
super().__init__(config)
self.dropout_prob = config.attention_probs_dropout_prob
self.require_contiguous_qkv = not is_torch_greater_or_equal_than_2_2
def forward(
self,
hidden_states: torch.Tensor,
... | class_definition | 16,212 | 19,102 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py | null | 3,143 |
class AlbertLayer(nn.Module):
def __init__(self, config: AlbertConfig):
super().__init__()
self.config = config
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.full_layer_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_n... | class_definition | 19,201 | 20,916 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py | null | 3,144 |
class AlbertLayerGroup(nn.Module):
def __init__(self, config: AlbertConfig):
super().__init__()
self.albert_layers = nn.ModuleList([AlbertLayer(config) for _ in range(config.inner_group_num)])
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torc... | class_definition | 20,919 | 22,280 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py | null | 3,145 |
class AlbertTransformer(nn.Module):
def __init__(self, config: AlbertConfig):
super().__init__()
self.config = config
self.embedding_hidden_mapping_in = nn.Linear(config.embedding_size, config.hidden_size)
self.albert_layer_groups = nn.ModuleList([AlbertLayerGroup(config) for _ in r... | class_definition | 22,283 | 24,490 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py | null | 3,146 |
class AlbertPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = AlbertConfig
load_tf_weights = load_tf_weights_in_albert
base_model_prefix = "albert"
_supports_s... | class_definition | 24,493 | 25,635 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py | null | 3,147 |
class AlbertForPreTrainingOutput(ModelOutput):
"""
Output type of [`AlbertForPreTraining`].
Args:
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 next sequence prediction
... | class_definition | 25,649 | 27,577 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py | null | 3,148 |
class AlbertModel(AlbertPreTrainedModel):
config_class = AlbertConfig
base_model_prefix = "albert"
def __init__(self, config: AlbertConfig, add_pooling_layer: bool = True):
super().__init__(config)
self.config = config
self.embeddings = AlbertEmbeddings(config)
self.encoder... | class_definition | 31,246 | 37,599 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py | null | 3,149 |
class AlbertForPreTraining(AlbertPreTrainedModel):
_tied_weights_keys = ["predictions.decoder.bias", "predictions.decoder.weight"]
def __init__(self, config: AlbertConfig):
super().__init__(config)
self.albert = AlbertModel(config)
self.predictions = AlbertMLMHead(config)
self.... | class_definition | 37,838 | 42,457 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py | null | 3,150 |
class AlbertMLMHead(nn.Module):
def __init__(self, config: AlbertConfig):
super().__init__()
self.LayerNorm = nn.LayerNorm(config.embedding_size, eps=config.layer_norm_eps)
self.bias = nn.Parameter(torch.zeros(config.vocab_size))
self.dense = nn.Linear(config.hidden_size, config.emb... | class_definition | 42,460 | 43,681 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py | null | 3,151 |
class AlbertSOPHead(nn.Module):
def __init__(self, config: AlbertConfig):
super().__init__()
self.dropout = nn.Dropout(config.classifier_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
def forward(self, pooled_output: torch.Tensor) -> torch.Tensor:
... | class_definition | 43,684 | 44,137 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py | null | 3,152 |
class AlbertForMaskedLM(AlbertPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["predictions.decoder.bias", "predictions.decoder.weight"]
def __init__(self, config):
super().__init__(config)
self.albert = AlbertModel(config, add_pooling_layer=False)
self.predictions = AlbertMLMH... | class_definition | 44,253 | 48,623 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py | null | 3,153 |
class AlbertForSequenceClassification(AlbertPreTrainedModel):
def __init__(self, config: AlbertConfig):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.albert = AlbertModel(config)
self.dropout = nn.Dropout(config.classifier_dropout_pro... | class_definition | 48,849 | 52,838 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py | null | 3,154 |
class AlbertForTokenClassification(AlbertPreTrainedModel):
def __init__(self, config: AlbertConfig):
super().__init__(config)
self.num_labels = config.num_labels
self.albert = AlbertModel(config, add_pooling_layer=False)
classifier_dropout_prob = (
config.classifier_drop... | class_definition | 53,071 | 56,005 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py | null | 3,155 |
class AlbertForQuestionAnswering(AlbertPreTrainedModel):
def __init__(self, config: AlbertConfig):
super().__init__(config)
self.num_labels = config.num_labels
self.albert = AlbertModel(config, add_pooling_layer=False)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_label... | class_definition | 56,296 | 60,715 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py | null | 3,156 |
class AlbertForMultipleChoice(AlbertPreTrainedModel):
def __init__(self, config: AlbertConfig):
super().__init__(config)
self.albert = AlbertModel(config)
self.dropout = nn.Dropout(config.classifier_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, 1)
# Initiali... | class_definition | 60,950 | 64,499 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py | null | 3,157 |
class AlbertTokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "fast" ALBERT tokenizer (backed by HuggingFace's *tokenizers* library). Based on
[Unigram](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=unigram#models). This
tokenizer inherits from [`PreTrainedTokenize... | class_definition | 1,200 | 8,829 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/tokenization_albert_fast.py | null | 3,158 |
class FlaxAlbertForPreTrainingOutput(ModelOutput):
"""
Output type of [`FlaxAlbertForPreTraining`].
Args:
prediction_logits (`jnp.ndarray` of shape `(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token befo... | class_definition | 1,714 | 3,306 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py | null | 3,159 |
class FlaxAlbertEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
config: AlbertConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.word_embeddings = nn.Embed(
self.config.vocab_size,
... | class_definition | 6,866 | 8,594 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py | null | 3,160 |
class FlaxAlbertSelfAttention(nn.Module):
config: AlbertConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
if self.config.hidden_size % self.config.num_attention_heads != 0:
raise ValueError(
"`config.hidden_size`: {self.config.hidden_... | class_definition | 8,597 | 12,255 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py | null | 3,161 |
class FlaxAlbertLayer(nn.Module):
config: AlbertConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.attention = FlaxAlbertSelfAttention(self.config, dtype=self.dtype)
self.ffn = nn.Dense(
self.config.intermediate_size,
kernel_i... | class_definition | 12,258 | 13,923 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py | null | 3,162 |
class FlaxAlbertLayerCollection(nn.Module):
config: AlbertConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.layers = [
FlaxAlbertLayer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.inner_group_num)
]
def __c... | class_definition | 13,926 | 15,352 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py | null | 3,163 |
class FlaxAlbertLayerCollections(nn.Module):
config: AlbertConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
layer_index: Optional[str] = None
def setup(self):
self.albert_layers = FlaxAlbertLayerCollection(self.config, dtype=self.dtype)
def __call__(
self,
... | class_definition | 15,355 | 16,116 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py | null | 3,164 |
class FlaxAlbertLayerGroups(nn.Module):
config: AlbertConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.layers = [
FlaxAlbertLayerCollections(self.config, name=str(i), layer_index=str(i), dtype=self.dtype)
for i in range(self.config.... | class_definition | 16,119 | 17,884 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py | null | 3,165 |
class FlaxAlbertEncoder(nn.Module):
config: AlbertConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.embedding_hidden_mapping_in = nn.Dense(
self.config.hidden_size,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range... | class_definition | 17,887 | 18,902 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py | null | 3,166 |
class FlaxAlbertOnlyMLMHead(nn.Module):
config: AlbertConfig
dtype: jnp.dtype = jnp.float32
bias_init: Callable[..., np.ndarray] = jax.nn.initializers.zeros
def setup(self):
self.dense = nn.Dense(self.config.embedding_size, dtype=self.dtype)
self.activation = ACT2FN[self.config.hidden_a... | class_definition | 18,905 | 19,997 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py | null | 3,167 |
class FlaxAlbertSOPHead(nn.Module):
config: AlbertConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.dropout = nn.Dropout(self.config.classifier_dropout_prob)
self.classifier = nn.Dense(2, dtype=self.dtype)
def __call__(self, pooled_output, deterministic=True):
pooled_... | class_definition | 20,000 | 20,455 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py | null | 3,168 |
class FlaxAlbertPreTrainedModel(FlaxPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = AlbertConfig
base_model_prefix = "albert"
module_class: nn.Module = None
def __init__(
... | class_definition | 20,458 | 24,020 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py | null | 3,169 |
class FlaxAlbertModule(nn.Module):
config: AlbertConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
add_pooling_layer: bool = True
def setup(self):
self.embeddings = FlaxAlbertEmbeddings(self.config, dtype=self.dtype)
self.encoder = FlaxAlbertEncoder(self.config, dtyp... | class_definition | 24,023 | 26,552 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py | null | 3,170 |
class FlaxAlbertModel(FlaxAlbertPreTrainedModel):
module_class = FlaxAlbertModule | class_definition | 26,712 | 26,797 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py | null | 3,171 |
class FlaxAlbertForPreTrainingModule(nn.Module):
config: AlbertConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.albert = FlaxAlbertModule(config=self.config, dtype=self.dtype)
self.predictions = FlaxAlbertOnlyMLMHead(config=self.config, dtype=self.dtype)
self.sop_classifi... | class_definition | 26,918 | 28,710 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py | null | 3,172 |
class FlaxAlbertForPreTraining(FlaxAlbertPreTrainedModel):
module_class = FlaxAlbertForPreTrainingModule | class_definition | 28,949 | 29,057 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py | null | 3,173 |
class FlaxAlbertForMaskedLMModule(nn.Module):
config: AlbertConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.albert = FlaxAlbertModule(config=self.config, add_pooling_layer=False, dtype=self.dtype)
self.predictions = FlaxAlbertOnlyMLMHead(config=self.config, dtype=self.dtype)
... | class_definition | 29,912 | 31,457 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py | null | 3,174 |
class FlaxAlbertForMaskedLM(FlaxAlbertPreTrainedModel):
module_class = FlaxAlbertForMaskedLMModule | class_definition | 31,566 | 31,668 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py | null | 3,175 |
class FlaxAlbertForSequenceClassificationModule(nn.Module):
config: AlbertConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.albert = FlaxAlbertModule(config=self.config, dtype=self.dtype)
classifier_dropout = (
self.config.classifier_dropout_prob
if self.co... | class_definition | 31,812 | 33,442 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py | null | 3,176 |
class FlaxAlbertForSequenceClassification(FlaxAlbertPreTrainedModel):
module_class = FlaxAlbertForSequenceClassificationModule | class_definition | 33,668 | 33,798 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py | null | 3,177 |
class FlaxAlbertForMultipleChoiceModule(nn.Module):
config: AlbertConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.albert = FlaxAlbertModule(config=self.config, dtype=self.dtype)
self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)
self.classifier = nn.Dense(1,... | class_definition | 33,956 | 35,898 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py | null | 3,178 |
class FlaxAlbertForMultipleChoice(FlaxAlbertPreTrainedModel):
module_class = FlaxAlbertForMultipleChoiceModule | class_definition | 36,133 | 36,247 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py | null | 3,179 |
class FlaxAlbertForTokenClassificationModule(nn.Module):
config: AlbertConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.albert = FlaxAlbertModule(config=self.config, dtype=self.dtype, add_pooling_layer=False)
classifier_dropout = (
self.config.classifier_dropout_prob
... | class_definition | 36,534 | 38,148 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py | null | 3,180 |
class FlaxAlbertForTokenClassification(FlaxAlbertPreTrainedModel):
module_class = FlaxAlbertForTokenClassificationModule | class_definition | 38,381 | 38,505 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py | null | 3,181 |
class FlaxAlbertForQuestionAnsweringModule(nn.Module):
config: AlbertConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.albert = FlaxAlbertModule(config=self.config, dtype=self.dtype, add_pooling_layer=False)
self.qa_outputs = nn.Dense(self.config.num_labels, dtype=self.dtype)
... | class_definition | 38,657 | 40,173 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py | null | 3,182 |
class FlaxAlbertForQuestionAnswering(FlaxAlbertPreTrainedModel):
module_class = FlaxAlbertForQuestionAnsweringModule | class_definition | 40,464 | 40,584 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py | null | 3,183 |
class AlbertTokenizer(PreTrainedTokenizer):
"""
Construct an ALBERT tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information rega... | class_definition | 1,111 | 14,498 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/tokenization_albert.py | null | 3,184 |
class TFAlbertPreTrainingLoss:
"""
Loss function suitable for ALBERT pretraining, that is, the task of pretraining a language model by combining SOP +
MLM. .. note:: Any label of -100 will be ignored (along with the corresponding logits) in the loss computation.
"""
def hf_compute_loss(self, labels... | class_definition | 1,942 | 5,200 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py | null | 3,185 |
class TFAlbertEmbeddings(keras.layers.Layer):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config: AlbertConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.embedding_size = config.embedding_size
self.max_p... | class_definition | 5,203 | 8,648 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py | null | 3,186 |
class TFAlbertAttention(keras.layers.Layer):
"""Contains the complete attention sublayer, including both dropouts and layer norm."""
def __init__(self, config: AlbertConfig, **kwargs):
super().__init__(**kwargs)
if config.hidden_size % config.num_attention_heads != 0:
raise ValueEr... | class_definition | 8,651 | 14,709 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py | null | 3,187 |
class TFAlbertLayer(keras.layers.Layer):
def __init__(self, config: AlbertConfig, **kwargs):
super().__init__(**kwargs)
self.attention = TFAlbertAttention(config, name="attention")
self.ffn = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(... | class_definition | 14,712 | 17,461 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py | null | 3,188 |
class TFAlbertLayerGroup(keras.layers.Layer):
def __init__(self, config: AlbertConfig, **kwargs):
super().__init__(**kwargs)
self.albert_layers = [
TFAlbertLayer(config, name=f"albert_layers_._{i}") for i in range(config.inner_group_num)
]
def call(
self,
hi... | class_definition | 17,464 | 19,317 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py | null | 3,189 |
class TFAlbertTransformer(keras.layers.Layer):
def __init__(self, config: AlbertConfig, **kwargs):
super().__init__(**kwargs)
self.num_hidden_layers = config.num_hidden_layers
self.num_hidden_groups = config.num_hidden_groups
# Number of layers in a hidden group
self.layers_... | class_definition | 19,320 | 22,374 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py | null | 3,190 |
class TFAlbertPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = AlbertConfig
base_model_prefix = "albert" | class_definition | 22,377 | 22,637 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py | null | 3,191 |
class TFAlbertMLMHead(keras.layers.Layer):
def __init__(self, config: AlbertConfig, input_embeddings: keras.layers.Layer, **kwargs):
super().__init__(**kwargs)
self.config = config
self.embedding_size = config.embedding_size
self.dense = keras.layers.Dense(
config.embedd... | class_definition | 22,640 | 25,492 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py | null | 3,192 |
class TFAlbertMainLayer(keras.layers.Layer):
config_class = AlbertConfig
def __init__(self, config: AlbertConfig, add_pooling_layer: bool = True, **kwargs):
super().__init__(**kwargs)
self.config = config
self.embeddings = TFAlbertEmbeddings(config, name="embeddings")
self.enc... | class_definition | 25,515 | 31,430 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py | null | 3,193 |
class TFAlbertForPreTrainingOutput(ModelOutput):
"""
Output type of [`TFAlbertForPreTraining`].
Args:
prediction_logits (`tf.Tensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before Sof... | class_definition | 31,444 | 33,033 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py | null | 3,194 |
class TFAlbertModel(TFAlbertPreTrainedModel):
def __init__(self, config: AlbertConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.albert = TFAlbertMainLayer(config, name="albert")
@unpack_inputs
@add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format... | class_definition | 38,906 | 40,780 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py | null | 3,195 |
class TFAlbertForPreTraining(TFAlbertPreTrainedModel, TFAlbertPreTrainingLoss):
# 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"predictions.decoder.weight"]
def __init__(self, config: AlbertConfig, *inp... | class_definition | 41,004 | 45,385 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py | null | 3,196 |
class TFAlbertSOPHead(keras.layers.Layer):
def __init__(self, config: AlbertConfig, **kwargs):
super().__init__(**kwargs)
self.dropout = keras.layers.Dropout(rate=config.classifier_dropout_prob)
self.classifier = keras.layers.Dense(
units=config.num_labels,
kernel_in... | class_definition | 45,388 | 46,375 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py | null | 3,197 |
class TFAlbertForMaskedLM(TFAlbertPreTrainedModel, 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"predictions.decoder.weight"]
def __init__(self, config: Alber... | class_definition | 46,484 | 51,042 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py | null | 3,198 |
class TFAlbertForSequenceClassification(TFAlbertPreTrainedModel, 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"predictions"]
_keys_to_ignore_on_load_missing = [r"dropout"... | class_definition | 51,268 | 55,048 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py | null | 3,199 |
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