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 T5ForConditionalGeneration(T5PreTrainedModel, GenerationMixin):
_keys_to_ignore_on_load_unexpected = [
"decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight",
]
_tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"]
def __in... | class_definition | 79,406 | 92,402 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/t5/modeling_t5.py | null | 5,700 |
class T5EncoderModel(T5PreTrainedModel):
_tied_weights_keys = ["encoder.embed_tokens.weight"]
_keys_to_ignore_on_load_unexpected = [r"decoder"]
def __init__(self, config: T5Config):
super().__init__(config)
self.shared = nn.Embedding(config.vocab_size, config.d_model)
encoder_confi... | class_definition | 92,564 | 96,988 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/t5/modeling_t5.py | null | 5,701 |
class T5ForSequenceClassification(T5PreTrainedModel):
_keys_to_ignore_on_load_unexpected = ["decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight"]
_tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
def __init__(self, config: T5Config):
super().__i... | class_definition | 97,183 | 103,279 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/t5/modeling_t5.py | null | 5,702 |
class T5ForTokenClassification(T5PreTrainedModel):
_tied_weights_keys = ["transformer.encoder.embed_tokens.weight"]
def __init__(self, config: T5Config):
super().__init__(config)
self.num_labels = config.num_labels
self.transformer = T5EncoderModel(config)
self.dropout = nn.Dro... | class_definition | 103,512 | 105,993 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/t5/modeling_t5.py | null | 5,703 |
class T5ForQuestionAnswering(T5PreTrainedModel):
_keys_to_ignore_on_load_unexpected = ["decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight"]
_tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
def __init__(self, config: T5Config):
super().__init__... | class_definition | 106,274 | 115,096 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/t5/modeling_t5.py | null | 5,704 |
class T5Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`T5Model`] or a [`TFT5Model`]. It is used to
instantiate a T5 model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a ... | class_definition | 842 | 6,368 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/t5/configuration_t5.py | null | 5,705 |
class T5OnnxConfig(OnnxSeq2SeqConfigWithPast):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
common_inputs = {
"input_ids": {0: "batch", 1: "encoder_sequence"},
"attention_mask": {0: "batch", 1: "encoder_sequence"},
}
if self.use_past:
... | class_definition | 6,371 | 7,330 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/t5/configuration_t5.py | null | 5,706 |
class TFT5LayerNorm(keras.layers.Layer):
def __init__(self, hidden_size, epsilon=1e-6, **kwargs):
"""
Construct a layernorm module in the T5 style No bias and no subtraction of mean.
"""
super().__init__(**kwargs)
self.variance_epsilon = epsilon
self.hidden_size = hid... | class_definition | 1,982 | 2,784 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/t5/modeling_tf_t5.py | null | 5,707 |
class TFT5DenseActDense(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
wi_initializer = keras.initializers.RandomNormal(
mean=0, stddev=config.initializer_factor * (config.d_model**-0.5)
)
wo_initializer = keras.initializers.RandomNo... | class_definition | 2,787 | 4,409 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/t5/modeling_tf_t5.py | null | 5,708 |
class TFT5DenseGatedActDense(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
wi_initializer = keras.initializers.RandomNormal(
mean=0, stddev=config.initializer_factor * (config.d_model**-0.5)
)
wo_initializer = keras.initializers.Ran... | class_definition | 4,412 | 6,451 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/t5/modeling_tf_t5.py | null | 5,709 |
class TFT5LayerFF(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
if config.is_gated_act:
self.DenseReluDense = TFT5DenseGatedActDense(config, name="DenseReluDense")
else:
self.DenseReluDense = TFT5DenseActDense(config, name="Dens... | class_definition | 6,454 | 7,707 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/t5/modeling_tf_t5.py | null | 5,710 |
class TFT5Attention(keras.layers.Layer):
NEW_ID = itertools.count()
def __init__(self, config, has_relative_attention_bias=False, **kwargs):
super().__init__(**kwargs)
self.layer_id = next(TFT5Attention.NEW_ID)
self.is_decoder = config.is_decoder
self.use_cache = config.use_cach... | class_definition | 7,710 | 20,506 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/t5/modeling_tf_t5.py | null | 5,711 |
class TFT5LayerSelfAttention(keras.layers.Layer):
def __init__(self, config, has_relative_attention_bias=False, **kwargs):
super().__init__(**kwargs)
self.SelfAttention = TFT5Attention(
config,
has_relative_attention_bias=has_relative_attention_bias,
name="SelfAtt... | class_definition | 20,509 | 22,346 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/t5/modeling_tf_t5.py | null | 5,712 |
class TFT5LayerCrossAttention(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.EncDecAttention = TFT5Attention(
config,
has_relative_attention_bias=False,
name="EncDecAttention",
)
self.layer_norm = TFT5Lay... | class_definition | 22,349 | 24,281 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/t5/modeling_tf_t5.py | null | 5,713 |
class TFT5Block(keras.layers.Layer):
def __init__(self, config, has_relative_attention_bias=False, **kwargs):
super().__init__(**kwargs)
self.is_decoder = config.is_decoder
self.layer = []
self.layer.append(
TFT5LayerSelfAttention(
config,
... | class_definition | 24,284 | 28,881 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/t5/modeling_tf_t5.py | null | 5,714 |
class TFT5MainLayer(keras.layers.Layer):
config_class = T5Config
def __init__(self, config, embed_tokens=None, **kwargs):
super().__init__(**kwargs)
self.config = config
self.output_hidden_states = config.output_hidden_states
self.output_attentions = config.output_attentions
... | class_definition | 29,145 | 40,902 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/t5/modeling_tf_t5.py | null | 5,715 |
class TFT5PreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = T5Config
base_model_prefix = "transformer"
# names with a '.' represents the authorized unexpected/mi... | class_definition | 41,238 | 43,500 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/t5/modeling_tf_t5.py | null | 5,716 |
class TFT5Model(TFT5PreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.shared = keras.layers.Embedding(
input_dim=config.vocab_size,
output_dim=config.d_model,
embeddings_initializer=keras.initializer... | class_definition | 55,185 | 61,571 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/t5/modeling_tf_t5.py | null | 5,717 |
class TFT5ForConditionalGeneration(TFT5PreTrainedModel, TFCausalLanguageModelingLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.model_dim = config.d_model
self.shared = keras.layers.Embedding(
config.vocab_size,
c... | class_definition | 61,672 | 73,585 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/t5/modeling_tf_t5.py | null | 5,718 |
class TFT5EncoderModel(TFT5PreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.shared = keras.layers.Embedding(
config.vocab_size,
config.d_model,
name="shared",
embeddings_initializer=get_i... | class_definition | 73,746 | 77,078 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/t5/modeling_tf_t5.py | null | 5,719 |
class DPRContextEncoderOutput(ModelOutput):
"""
Class for outputs of [`DPRQuestionEncoder`].
Args:
pooler_output (`torch.FloatTensor` of shape `(batch_size, embeddings_size)`):
The DPR encoder outputs the *pooler_output* that corresponds to the context representation. Last layer
... | class_definition | 1,321 | 2,921 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpr/modeling_dpr.py | null | 5,720 |
class DPRQuestionEncoderOutput(ModelOutput):
"""
Class for outputs of [`DPRQuestionEncoder`].
Args:
pooler_output (`torch.FloatTensor` of shape `(batch_size, embeddings_size)`):
The DPR encoder outputs the *pooler_output* that corresponds to the question representation. Last layer
... | class_definition | 2,935 | 4,536 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpr/modeling_dpr.py | null | 5,721 |
class DPRReaderOutput(ModelOutput):
"""
Class for outputs of [`DPRQuestionEncoder`].
Args:
start_logits (`torch.FloatTensor` of shape `(n_passages, sequence_length)`):
Logits of the start index of the span for each passage.
end_logits (`torch.FloatTensor` of shape `(n_passages, ... | class_definition | 4,550 | 6,348 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpr/modeling_dpr.py | null | 5,722 |
class DPRPreTrainedModel(PreTrainedModel):
_supports_sdpa = True
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, nn.Linear):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.co... | class_definition | 6,351 | 7,230 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpr/modeling_dpr.py | null | 5,723 |
class DPREncoder(DPRPreTrainedModel):
base_model_prefix = "bert_model"
def __init__(self, config: DPRConfig):
super().__init__(config)
self.bert_model = BertModel(config, add_pooling_layer=False)
if self.bert_model.config.hidden_size <= 0:
raise ValueError("Encoder hidden_si... | class_definition | 7,233 | 9,275 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpr/modeling_dpr.py | null | 5,724 |
class DPRSpanPredictor(DPRPreTrainedModel):
base_model_prefix = "encoder"
def __init__(self, config: DPRConfig):
super().__init__(config)
self.encoder = DPREncoder(config)
self.qa_outputs = nn.Linear(self.encoder.embeddings_size, 2)
self.qa_classifier = nn.Linear(self.encoder.em... | class_definition | 9,278 | 11,486 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpr/modeling_dpr.py | null | 5,725 |
class DPRPretrainedContextEncoder(DPRPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = DPRConfig
load_tf_weights = None
base_model_prefix = "ctx_encoder" | class_definition | 11,547 | 11,841 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpr/modeling_dpr.py | null | 5,726 |
class DPRPretrainedQuestionEncoder(DPRPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = DPRConfig
load_tf_weights = None
base_model_prefix = "question_encoder" | class_definition | 11,844 | 12,144 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpr/modeling_dpr.py | null | 5,727 |
class DPRPretrainedReader(DPRPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = DPRConfig
load_tf_weights = None
base_model_prefix = "span_predictor" | class_definition | 12,147 | 12,436 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpr/modeling_dpr.py | null | 5,728 |
class DPRContextEncoder(DPRPretrainedContextEncoder):
def __init__(self, config: DPRConfig):
super().__init__(config)
self.config = config
self.ctx_encoder = DPREncoder(config)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_mode... | class_definition | 18,541 | 21,764 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpr/modeling_dpr.py | null | 5,729 |
class DPRQuestionEncoder(DPRPretrainedQuestionEncoder):
def __init__(self, config: DPRConfig):
super().__init__(config)
self.config = config
self.question_encoder = DPREncoder(config)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_... | class_definition | 21,919 | 25,261 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpr/modeling_dpr.py | null | 5,730 |
class DPRReader(DPRPretrainedReader):
def __init__(self, config: DPRConfig):
super().__init__(config)
self.config = config
self.span_predictor = DPRSpanPredictor(config)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forwa... | class_definition | 25,381 | 28,333 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpr/modeling_dpr.py | null | 5,731 |
class DPRState:
def __init__(self, src_file: Path):
self.src_file = src_file
def load_dpr_model(self):
raise NotImplementedError
@staticmethod
def from_type(comp_type: str, *args, **kwargs) -> "DPRState":
if comp_type.startswith("c"):
return DPRContextEncoderState(*... | class_definition | 1,261 | 1,906 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpr/convert_dpr_original_checkpoint_to_pytorch.py | null | 5,732 |
class DPRContextEncoderState(DPRState):
def load_dpr_model(self):
model = DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict("google-bert/bert-base-uncased")[0]))
print(f"Loading DPR biencoder from {self.src_file}")
saved_state = load_states_from_checkpoint(self.src_file)
encod... | class_definition | 1,909 | 2,850 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpr/convert_dpr_original_checkpoint_to_pytorch.py | null | 5,733 |
class DPRQuestionEncoderState(DPRState):
def load_dpr_model(self):
model = DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict("google-bert/bert-base-uncased")[0]))
print(f"Loading DPR biencoder from {self.src_file}")
saved_state = load_states_from_checkpoint(self.src_file)
enc... | class_definition | 2,853 | 3,811 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpr/convert_dpr_original_checkpoint_to_pytorch.py | null | 5,734 |
class DPRReaderState(DPRState):
def load_dpr_model(self):
model = DPRReader(DPRConfig(**BertConfig.get_config_dict("google-bert/bert-base-uncased")[0]))
print(f"Loading DPR reader from {self.src_file}")
saved_state = load_states_from_checkpoint(self.src_file)
# Fix changes from https... | class_definition | 3,814 | 4,705 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpr/convert_dpr_original_checkpoint_to_pytorch.py | null | 5,735 |
class DPRContextEncoderTokenizer(BertTokenizer):
r"""
Construct a DPRContextEncoder tokenizer.
[`DPRContextEncoderTokenizer`] is identical to [`BertTokenizer`] and runs end-to-end tokenization: punctuation
splitting and wordpiece.
Refer to superclass [`BertTokenizer`] for usage examples and docume... | class_definition | 1,040 | 1,441 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpr/tokenization_dpr.py | null | 5,736 |
class DPRQuestionEncoderTokenizer(BertTokenizer):
r"""
Constructs a DPRQuestionEncoder tokenizer.
[`DPRQuestionEncoderTokenizer`] is identical to [`BertTokenizer`] and runs end-to-end tokenization: punctuation
splitting and wordpiece.
Refer to superclass [`BertTokenizer`] for usage examples and do... | class_definition | 1,444 | 1,849 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpr/tokenization_dpr.py | null | 5,737 |
class CustomDPRReaderTokenizerMixin:
def __call__(
self,
questions,
titles: Optional[str] = None,
texts: Optional[str] = None,
padding: Union[bool, str] = False,
truncation: Union[bool, str] = False,
max_length: Optional[int] = None,
return_tensors: Op... | class_definition | 6,815 | 15,062 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpr/tokenization_dpr.py | null | 5,738 |
class DPRReaderTokenizer(CustomDPRReaderTokenizerMixin, BertTokenizer):
r"""
Construct a DPRReader tokenizer.
[`DPRReaderTokenizer`] is almost identical to [`BertTokenizer`] and runs end-to-end tokenization: punctuation
splitting and wordpiece. The difference is that is has three inputs strings: questi... | class_definition | 15,114 | 15,724 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpr/tokenization_dpr.py | null | 5,739 |
class DPRContextEncoderTokenizerFast(BertTokenizerFast):
r"""
Construct a "fast" DPRContextEncoder tokenizer (backed by HuggingFace's *tokenizers* library).
[`DPRContextEncoderTokenizerFast`] is identical to [`BertTokenizerFast`] and runs end-to-end tokenization:
punctuation splitting and wordpiece.
... | class_definition | 1,155 | 1,684 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpr/tokenization_dpr_fast.py | null | 5,740 |
class DPRQuestionEncoderTokenizerFast(BertTokenizerFast):
r"""
Constructs a "fast" DPRQuestionEncoder tokenizer (backed by HuggingFace's *tokenizers* library).
[`DPRQuestionEncoderTokenizerFast`] is identical to [`BertTokenizerFast`] and runs end-to-end tokenization:
punctuation splitting and wordpiece... | class_definition | 1,687 | 2,221 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpr/tokenization_dpr_fast.py | null | 5,741 |
class CustomDPRReaderTokenizerMixin:
def __call__(
self,
questions,
titles: Optional[str] = None,
texts: Optional[str] = None,
padding: Union[bool, str] = False,
truncation: Union[bool, str] = False,
max_length: Optional[int] = None,
return_tensors: Op... | class_definition | 7,170 | 15,326 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpr/tokenization_dpr_fast.py | null | 5,742 |
class DPRReaderTokenizerFast(CustomDPRReaderTokenizerMixin, BertTokenizerFast):
r"""
Constructs a "fast" DPRReader tokenizer (backed by HuggingFace's *tokenizers* library).
[`DPRReaderTokenizerFast`] is almost identical to [`BertTokenizerFast`] and runs end-to-end tokenization:
punctuation splitting an... | class_definition | 15,378 | 16,110 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpr/tokenization_dpr_fast.py | null | 5,743 |
class TFDPRContextEncoderOutput(ModelOutput):
r"""
Class for outputs of [`TFDPRContextEncoder`].
Args:
pooler_output (`tf.Tensor` of shape `(batch_size, embeddings_size)`):
The DPR encoder outputs the *pooler_output* that corresponds to the context representation. Last layer
... | class_definition | 1,357 | 2,898 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpr/modeling_tf_dpr.py | null | 5,744 |
class TFDPRQuestionEncoderOutput(ModelOutput):
"""
Class for outputs of [`TFDPRQuestionEncoder`].
Args:
pooler_output (`tf.Tensor` of shape `(batch_size, embeddings_size)`):
The DPR encoder outputs the *pooler_output* that corresponds to the question representation. Last layer
... | class_definition | 2,912 | 4,454 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpr/modeling_tf_dpr.py | null | 5,745 |
class TFDPRReaderOutput(ModelOutput):
"""
Class for outputs of [`TFDPRReaderEncoder`].
Args:
start_logits (`tf.Tensor` of shape `(n_passages, sequence_length)`):
Logits of the start index of the span for each passage.
end_logits (`tf.Tensor` of shape `(n_passages, sequence_lengt... | class_definition | 4,468 | 6,189 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpr/modeling_tf_dpr.py | null | 5,746 |
class TFDPREncoderLayer(keras.layers.Layer):
base_model_prefix = "bert_model"
def __init__(self, config: DPRConfig, **kwargs):
super().__init__(**kwargs)
# resolve name conflict with TFBertMainLayer instead of TFBertModel
self.bert_model = TFBertMainLayer(config, add_pooling_layer=Fals... | class_definition | 6,192 | 8,884 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpr/modeling_tf_dpr.py | null | 5,747 |
class TFDPRSpanPredictorLayer(keras.layers.Layer):
base_model_prefix = "encoder"
def __init__(self, config: DPRConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.encoder = TFDPREncoderLayer(config, name="encoder")
self.qa_outputs = keras.layers.Dense(
... | class_definition | 8,887 | 12,062 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpr/modeling_tf_dpr.py | null | 5,748 |
class TFDPRSpanPredictor(TFPreTrainedModel):
base_model_prefix = "encoder"
def __init__(self, config: DPRConfig, **kwargs):
super().__init__(config, **kwargs)
self.encoder = TFDPRSpanPredictorLayer(config)
@unpack_inputs
def call(
self,
input_ids: tf.Tensor = None,
... | class_definition | 12,065 | 13,091 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpr/modeling_tf_dpr.py | null | 5,749 |
class TFDPREncoder(TFPreTrainedModel):
base_model_prefix = "encoder"
def __init__(self, config: DPRConfig, **kwargs):
super().__init__(config, **kwargs)
self.encoder = TFDPREncoderLayer(config)
@unpack_inputs
def call(
self,
input_ids: tf.Tensor = None,
attenti... | class_definition | 13,094 | 14,108 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpr/modeling_tf_dpr.py | null | 5,750 |
class TFDPRPretrainedContextEncoder(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = DPRConfig
base_model_prefix = "ctx_encoder" | class_definition | 14,169 | 14,437 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpr/modeling_tf_dpr.py | null | 5,751 |
class TFDPRPretrainedQuestionEncoder(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = DPRConfig
base_model_prefix = "question_encoder" | class_definition | 14,440 | 14,714 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpr/modeling_tf_dpr.py | null | 5,752 |
class TFDPRPretrainedReader(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = DPRConfig
base_model_prefix = "reader" | class_definition | 14,717 | 14,972 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpr/modeling_tf_dpr.py | null | 5,753 |
class TFDPRContextEncoder(TFDPRPretrainedContextEncoder):
def __init__(self, config: DPRConfig, *args, **kwargs):
super().__init__(config, *args, **kwargs)
self.ctx_encoder = TFDPREncoderLayer(config, name="ctx_encoder")
def get_input_embeddings(self):
try:
return self.ctx_e... | class_definition | 23,515 | 26,873 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpr/modeling_tf_dpr.py | null | 5,754 |
class TFDPRQuestionEncoder(TFDPRPretrainedQuestionEncoder):
def __init__(self, config: DPRConfig, *args, **kwargs):
super().__init__(config, *args, **kwargs)
self.question_encoder = TFDPREncoderLayer(config, name="question_encoder")
def get_input_embeddings(self):
try:
retur... | class_definition | 27,031 | 30,447 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpr/modeling_tf_dpr.py | null | 5,755 |
class TFDPRReader(TFDPRPretrainedReader):
def __init__(self, config: DPRConfig, *args, **kwargs):
super().__init__(config, *args, **kwargs)
self.span_predictor = TFDPRSpanPredictorLayer(config, name="span_predictor")
def get_input_embeddings(self):
try:
return self.span_pred... | class_definition | 30,570 | 33,634 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpr/modeling_tf_dpr.py | null | 5,756 |
class DPRConfig(PretrainedConfig):
r"""
[`DPRConfig`] is the configuration class to store the configuration of a *DPRModel*.
This is the configuration class to store the configuration of a [`DPRContextEncoder`], [`DPRQuestionEncoder`], or a
[`DPRReader`]. It is used to instantiate the components of the... | class_definition | 769 | 6,389 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpr/configuration_dpr.py | null | 5,757 |
class RecurrentGemmaConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`RecurrentGemmaModel`]. It is used to instantiate a RecurrentGemma
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defau... | class_definition | 799 | 7,712 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/recurrent_gemma/configuration_recurrent_gemma.py | null | 5,758 |
class RecurrentGemmaRMSNorm(nn.Module):
def __init__(self, dim: int, eps: float = 1e-6):
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.zeros(dim))
def _norm(self, x):
return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
def forward(self, x... | class_definition | 1,618 | 2,307 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/recurrent_gemma/modeling_recurrent_gemma.py | null | 5,759 |
class RecurrentGemmaRotaryEmbedding(nn.Module):
def __init__(self, dim, base=10000, device=None):
super().__init__()
self.dim = dim
self.base = base
inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float() / self.dim))
self.register_buffer("inv_... | class_definition | 2,363 | 3,673 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/recurrent_gemma/modeling_recurrent_gemma.py | null | 5,760 |
class RecurrentGemmaSdpaAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: RecurrentGemmaConfig):
super().__init__()
self.config = config
self.attention_dropout = config.attention_dropout
self.hidden_size = config... | class_definition | 6,218 | 12,629 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/recurrent_gemma/modeling_recurrent_gemma.py | null | 5,761 |
class SqrtBoundDerivative(torch.autograd.Function):
"""Computes a square root with a gradient clipped at `_MAX_SQRT_GRADIENT`."""
@staticmethod
def forward(ctx, x: torch.Tensor) -> torch.Tensor:
"""The forward pass, which is a normal `sqrt`."""
ctx.save_for_backward(x)
return torch.... | class_definition | 12,632 | 13,283 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/recurrent_gemma/modeling_recurrent_gemma.py | null | 5,762 |
class RecurrentGemmaRglru(nn.Module):
"""A Real-Gated Linear Recurrent Unit (RG-LRU) layer."""
def __init__(self, config):
super().__init__()
self.num_attention_heads = config.num_attention_heads
self.block_width = config.lru_width // self.num_attention_heads
self.recurrent_par... | class_definition | 13,286 | 18,334 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/recurrent_gemma/modeling_recurrent_gemma.py | null | 5,763 |
class RecurrentGemmaRecurrentBlock(nn.Module):
"""Griffin and Hawk's recurrent block."""
def __init__(self, config):
super().__init__()
self.lru_width = config.lru_width
self.hidden_size = config.hidden_size
self.linear_y = nn.Linear(in_features=config.hidden_size, out_features=... | class_definition | 18,337 | 20,973 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/recurrent_gemma/modeling_recurrent_gemma.py | null | 5,764 |
class RecurrentGemmaMlp(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.intermediate_size = config.intermediate_size // 2
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=True)... | class_definition | 21,089 | 21,801 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/recurrent_gemma/modeling_recurrent_gemma.py | null | 5,765 |
class RecurrentGemmaDecoderLayer(nn.Module):
"""Griffin and Hawk's residual block."""
def __init__(self, config, layer_idx):
super().__init__()
self.temporal_pre_norm = RecurrentGemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.temporal_block = TEMPORAL_BLOCK_CLASSES[config... | class_definition | 21,804 | 23,149 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/recurrent_gemma/modeling_recurrent_gemma.py | null | 5,766 |
class RecurrentGemmaPreTrainedModel(PreTrainedModel):
config_class = RecurrentGemmaConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["RecurrentGemmaDecoderLayer"]
_skip_keys_device_placement = ["cache"]
_supports_flash_attn_2 = False
_supports_sdp... | class_definition | 24,207 | 27,316 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/recurrent_gemma/modeling_recurrent_gemma.py | null | 5,767 |
class RecurrentGemmaModel(RecurrentGemmaPreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`RecurrentGemmaDecoderLayer`]
Args:
config: RecurrentGemmaConfig
"""
def __init__(self, config: RecurrentGemmaConfig):
super().__init_... | class_definition | 30,094 | 36,325 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/recurrent_gemma/modeling_recurrent_gemma.py | null | 5,768 |
class RecurrentGemmaForCausalLM(RecurrentGemmaPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
super().__init__(config)
self.model = RecurrentGemmaModel(config)
self.vocab_size = config.vocab_size
self.lm_head = nn.Linear(con... | class_definition | 36,485 | 41,648 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/recurrent_gemma/modeling_recurrent_gemma.py | null | 5,769 |
class RwkvLinearAttention(torch.autograd.Function):
@staticmethod
def forward(ctx, time_decay, time_first, key, value, state=None, return_state=False):
batch_size, seq_len, hidden_size = key.size()
if seq_len > rwkv_cuda_kernel.max_seq_length:
raise ValueError(
f"Cann... | class_definition | 2,531 | 6,703 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rwkv/modeling_rwkv.py | null | 5,770 |
class RwkvSelfAttention(nn.Module):
def __init__(self, config, layer_id=0):
super().__init__()
self.config = config
kernel_loaded = rwkv_cuda_kernel is not None and rwkv_cuda_kernel.max_seq_length == config.context_length
if is_ninja_available() and is_torch_cuda_available() and not ... | class_definition | 9,188 | 12,458 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rwkv/modeling_rwkv.py | null | 5,771 |
class RwkvFeedForward(nn.Module):
def __init__(self, config, layer_id=0):
super().__init__()
self.config = config
self.layer_id = layer_id
hidden_size = config.hidden_size
intermediate_size = (
config.intermediate_size if config.intermediate_size is not None else ... | class_definition | 12,461 | 14,007 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rwkv/modeling_rwkv.py | null | 5,772 |
class RwkvBlock(nn.Module):
def __init__(self, config, layer_id):
super().__init__()
self.config = config
self.layer_id = layer_id
if layer_id == 0:
self.pre_ln = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_epsilon)
self.ln1 = nn.LayerNorm(config.hidd... | class_definition | 14,010 | 15,154 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rwkv/modeling_rwkv.py | null | 5,773 |
class RwkvPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = RwkvConfig
base_model_prefix = "rwkv"
_no_split_modules = ["RwkvBlock"]
_keep_in_fp32_modules = ["t... | class_definition | 15,157 | 18,186 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rwkv/modeling_rwkv.py | null | 5,774 |
class RwkvOutput(ModelOutput):
"""
Class for the RWKV model outputs.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
state (list of five `torch.FloatTenso... | class_definition | 18,200 | 19,913 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rwkv/modeling_rwkv.py | null | 5,775 |
class RwkvCausalLMOutput(ModelOutput):
"""
Base class for causal language model (or autoregressive) outputs.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss (for next-token prediction).
logits (`torch.Float... | class_definition | 19,927 | 21,900 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rwkv/modeling_rwkv.py | null | 5,776 |
class RwkvModel(RwkvPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size)
self.blocks = nn.ModuleList([RwkvBlock(config, layer_id=idx) for idx in range(config.num_hidden_layers)])
self.ln_out = nn... | class_definition | 25,536 | 32,934 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rwkv/modeling_rwkv.py | null | 5,777 |
class RwkvForCausalLM(RwkvPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["head.weight"]
def __init__(self, config):
super().__init__(config)
self.rwkv = RwkvModel(config)
self.head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and... | class_definition | 33,135 | 37,014 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rwkv/modeling_rwkv.py | null | 5,778 |
class RwkvConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`RwkvModel`]. It is used to instantiate a RWKV
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar config... | class_definition | 842 | 5,175 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rwkv/configuration_rwkv.py | null | 5,779 |
class JambaConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`JambaModel`]. It is used to instantiate a
Jamba model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar co... | class_definition | 814 | 11,710 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/jamba/configuration_jamba.py | null | 5,780 |
class JambaRMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
JambaRMSNorm is equivalent to T5LayerNorm
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
... | class_definition | 6,407 | 7,127 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/jamba/modeling_jamba.py | null | 5,781 |
class HybridMambaAttentionDynamicCache(DynamicCache):
"""
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 `value_cache` for atten... | class_definition | 7,804 | 12,647 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/jamba/modeling_jamba.py | null | 5,782 |
class JambaAttention(nn.Module):
"""
Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer
and "Generating Long Sequences with Sparse Transformers".
"""
def __init__(self, config: JambaConfig, layer_idx: Optional[int] = None):
su... | class_definition | 12,747 | 17,093 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/jamba/modeling_jamba.py | null | 5,783 |
class JambaFlashAttention2(JambaAttention):
"""
Jamba flash attention module. This module inherits from `JambaAttention` 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 deal with pa... | class_definition | 17,199 | 21,785 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/jamba/modeling_jamba.py | null | 5,784 |
class JambaSdpaAttention(JambaAttention):
"""
Jamba attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
`JambaAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
SDPA API.
"""
# Adapted from... | class_definition | 21,889 | 26,102 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/jamba/modeling_jamba.py | null | 5,785 |
class JambaMambaMixer(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... | class_definition | 26,312 | 39,934 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/jamba/modeling_jamba.py | null | 5,786 |
class JambaMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.intermediate_size = config.intermediate_size
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
self... | class_definition | 40,027 | 40,695 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/jamba/modeling_jamba.py | null | 5,787 |
class JambaSparseMoeBlock(nn.Module):
"""
This implementation is
strictly equivalent to standard MoE with full capacity (no
dropped tokens). It's faster since it formulates MoE operations
in terms of block-sparse operations to accommodate imbalanced
assignments of tokens to experts, whereas stan... | class_definition | 40,800 | 43,939 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/jamba/modeling_jamba.py | null | 5,788 |
class JambaAttentionDecoderLayer(nn.Module):
def __init__(self, config: JambaConfig, layer_idx: int):
super().__init__()
num_experts = config.layers_num_experts[layer_idx]
self.self_attn = JAMBA_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx)
ffn_layer_class = Jamb... | class_definition | 43,942 | 47,619 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/jamba/modeling_jamba.py | null | 5,789 |
class JambaMambaDecoderLayer(nn.Module):
def __init__(self, config: JambaConfig, layer_idx: int):
super().__init__()
num_experts = config.layers_num_experts[layer_idx]
self.mamba = JambaMambaMixer(config=config, layer_idx=layer_idx)
ffn_layer_class = JambaSparseMoeBlock if num_exper... | class_definition | 47,622 | 51,089 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/jamba/modeling_jamba.py | null | 5,790 |
class JambaPreTrainedModel(PreTrainedModel):
config_class = JambaConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["JambaAttentionDecoderLayer", "JambaMambaDecoderLayer"]
_skip_keys_device_placement = "past_key_values"
_supports_flash_attn_2 = True
... | class_definition | 52,111 | 53,058 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/jamba/modeling_jamba.py | null | 5,791 |
class JambaModel(JambaPreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`JambaDecoderLayer`]
Args:
config: JambaConfig
"""
def __init__(self, config: JambaConfig):
super().__init__(config)
self.padding_idx = config.p... | class_definition | 58,030 | 67,038 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/jamba/modeling_jamba.py | null | 5,792 |
class JambaForCausalLM(JambaPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config: JambaConfig):
super().__init__(config)
self.model = JambaModel(config)
self.vocab_size = config.vocab_size
self.lm_head = nn.Linear(config.hidden_siz... | class_definition | 67,156 | 75,657 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/jamba/modeling_jamba.py | null | 5,793 |
class JambaForSequenceClassification(JambaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = JambaModel(config)
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
# Initialize weights a... | class_definition | 76,566 | 80,378 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/jamba/modeling_jamba.py | null | 5,794 |
class LayoutLMv3Tokenizer(PreTrainedTokenizer):
r"""
Construct a LayoutLMv3 tokenizer. Based on [`RoBERTatokenizer`] (Byte Pair Encoding or BPE).
[`LayoutLMv3Tokenizer`] can be used to turn words, word-level bounding boxes and optional word labels to
token-level `input_ids`, `attention_mask`, `token_typ... | class_definition | 10,690 | 73,190 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlmv3/tokenization_layoutlmv3.py | null | 5,795 |
class LayoutLMv3FeatureExtractor(LayoutLMv3ImageProcessor):
def __init__(self, *args, **kwargs) -> None:
warnings.warn(
"The class LayoutLMv3FeatureExtractor is deprecated and will be removed in version 5 of Transformers."
" Please use LayoutLMv3ImageProcessor instead.",
... | class_definition | 808 | 1,194 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlmv3/feature_extraction_layoutlmv3.py | null | 5,796 |
class LayoutLMv3TokenizerFast(PreTrainedTokenizerFast):
r"""
Construct a "fast" LayoutLMv3 tokenizer (backed by HuggingFace's *tokenizers* library). Based on BPE.
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
refer to this superclass for ... | class_definition | 1,519 | 39,878 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlmv3/tokenization_layoutlmv3_fast.py | null | 5,797 |
class TFLayoutLMv3PatchEmbeddings(keras.layers.Layer):
"""LayoutLMv3 image (patch) embeddings."""
def __init__(self, config: LayoutLMv3Config, **kwargs):
super().__init__(**kwargs)
patch_sizes = (
config.patch_size
if isinstance(config.patch_size, collections.abc.Iterabl... | class_definition | 1,704 | 3,327 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlmv3/modeling_tf_layoutlmv3.py | null | 5,798 |
class TFLayoutLMv3TextEmbeddings(keras.layers.Layer):
"""
LayoutLMv3 text embeddings. Same as `RobertaEmbeddings` but with added spatial (layout) embeddings.
"""
def __init__(self, config: LayoutLMv3Config, **kwargs):
super().__init__(**kwargs)
self.word_embeddings = keras.layers.Embedd... | class_definition | 3,330 | 12,045 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlmv3/modeling_tf_layoutlmv3.py | null | 5,799 |
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