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
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|---|---|---|---|---|---|---|---|
class TFLayoutLMv3SelfAttention(keras.layers.Layer):
def __init__(self, config: LayoutLMv3Config, **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... | class_definition | 12,048 | 17,908 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlmv3/modeling_tf_layoutlmv3.py | null | 5,800 |
class TFLayoutLMv3SelfOutput(keras.layers.Layer):
def __init__(self, config: LayoutLMv3Config, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
se... | class_definition | 17,980 | 19,319 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlmv3/modeling_tf_layoutlmv3.py | null | 5,801 |
class TFLayoutLMv3Attention(keras.layers.Layer):
def __init__(self, config: LayoutLMv3Config, **kwargs):
super().__init__(**kwargs)
self.self_attention = TFLayoutLMv3SelfAttention(config, name="self")
self.self_output = TFLayoutLMv3SelfOutput(config, name="output")
def call(
sel... | class_definition | 19,322 | 20,857 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlmv3/modeling_tf_layoutlmv3.py | null | 5,802 |
class TFLayoutLMv3Intermediate(keras.layers.Layer):
def __init__(self, config: LayoutLMv3Config, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
... | class_definition | 20,928 | 21,962 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlmv3/modeling_tf_layoutlmv3.py | null | 5,803 |
class TFLayoutLMv3Output(keras.layers.Layer):
def __init__(self, config: LayoutLMv3Config, **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 | 22,027 | 23,368 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlmv3/modeling_tf_layoutlmv3.py | null | 5,804 |
class TFLayoutLMv3Layer(keras.layers.Layer):
def __init__(self, config: LayoutLMv3Config, **kwargs):
super().__init__(**kwargs)
self.attention = TFLayoutLMv3Attention(config, name="attention")
self.intermediate = TFLayoutLMv3Intermediate(config, name="intermediate")
self.bert_output ... | class_definition | 23,371 | 25,337 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlmv3/modeling_tf_layoutlmv3.py | null | 5,805 |
class TFLayoutLMv3Encoder(keras.layers.Layer):
def __init__(self, config: LayoutLMv3Config, **kwargs):
super().__init__(**kwargs)
self.config = config
self.layer = [TFLayoutLMv3Layer(config, name=f"layer.{i}") for i in range(config.num_hidden_layers)]
self.has_relative_attention_bia... | class_definition | 25,340 | 32,772 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlmv3/modeling_tf_layoutlmv3.py | null | 5,806 |
class TFLayoutLMv3MainLayer(keras.layers.Layer):
config_class = LayoutLMv3Config
def __init__(self, config: LayoutLMv3Config, **kwargs):
super().__init__(**kwargs)
self.config = config
if config.text_embed:
self.embeddings = TFLayoutLMv3TextEmbeddings(config, name="embeddi... | class_definition | 32,795 | 48,227 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlmv3/modeling_tf_layoutlmv3.py | null | 5,807 |
class TFLayoutLMv3PreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = LayoutLMv3Config
base_model_prefix = "layoutlmv3"
@property
def input_signature(self):
... | class_definition | 48,230 | 48,681 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlmv3/modeling_tf_layoutlmv3.py | null | 5,808 |
class TFLayoutLMv3Model(TFLayoutLMv3PreTrainedModel):
# 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"position_ids"]
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inpu... | class_definition | 55,891 | 58,819 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlmv3/modeling_tf_layoutlmv3.py | null | 5,809 |
class TFLayoutLMv3ClassificationHead(keras.layers.Layer):
"""
Head for sentence-level classification tasks. Reference: RobertaClassificationHead
"""
def __init__(self, config: LayoutLMv3Config, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
config.hid... | class_definition | 58,822 | 60,684 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlmv3/modeling_tf_layoutlmv3.py | null | 5,810 |
class TFLayoutLMv3ForSequenceClassification(TFLayoutLMv3PreTrainedModel, 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"position_ids"]
def __init__(self, config: LayoutLM... | class_definition | 61,022 | 65,166 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlmv3/modeling_tf_layoutlmv3.py | null | 5,811 |
class TFLayoutLMv3ForTokenClassification(TFLayoutLMv3PreTrainedModel, 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"position_ids"]
def __init__(self, config: LayoutLMv3Conf... | class_definition | 65,626 | 70,816 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlmv3/modeling_tf_layoutlmv3.py | null | 5,812 |
class TFLayoutLMv3ForQuestionAnswering(TFLayoutLMv3PreTrainedModel, 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"position_ids"]
def __init__(self, config: LayoutLMv3Config, ... | class_definition | 71,172 | 76,774 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlmv3/modeling_tf_layoutlmv3.py | null | 5,813 |
class LayoutLMv3PatchEmbeddings(nn.Module):
"""LayoutLMv3 image (patch) embeddings. This class also automatically interpolates the position embeddings for varying
image sizes."""
def __init__(self, config):
super().__init__()
image_size = (
config.input_size
if isin... | class_definition | 10,036 | 11,615 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlmv3/modeling_layoutlmv3.py | null | 5,814 |
class LayoutLMv3TextEmbeddings(nn.Module):
"""
LayoutLMv3 text embeddings. Same as `RobertaEmbeddings` but with added spatial (layout) embeddings.
"""
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=co... | class_definition | 11,618 | 16,965 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlmv3/modeling_layoutlmv3.py | null | 5,815 |
class LayoutLMv3PreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = LayoutLMv3Config
base_model_prefix = "layoutlmv3"
def _init_weights(self, module):
"""In... | class_definition | 16,968 | 18,060 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlmv3/modeling_layoutlmv3.py | null | 5,816 |
class LayoutLMv3SelfAttention(nn.Module):
def __init__(self, config):
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 of the ... | class_definition | 18,063 | 22,510 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlmv3/modeling_layoutlmv3.py | null | 5,817 |
class LayoutLMv3SelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
... | class_definition | 22,590 | 23,202 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlmv3/modeling_layoutlmv3.py | null | 5,818 |
class LayoutLMv3Attention(nn.Module):
def __init__(self, config):
super().__init__()
self.self = LayoutLMv3SelfAttention(config)
self.output = LayoutLMv3SelfOutput(config)
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
outp... | class_definition | 23,318 | 24,116 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlmv3/modeling_layoutlmv3.py | null | 5,819 |
class LayoutLMv3Layer(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 = LayoutLMv3Attention(config)
self.intermediate = LayoutLMv3Intermediate(config)
self.out... | class_definition | 24,228 | 25,628 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlmv3/modeling_layoutlmv3.py | null | 5,820 |
class LayoutLMv3Encoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([LayoutLMv3Layer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
self.has_relative_attention_bias = confi... | class_definition | 25,631 | 31,956 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlmv3/modeling_layoutlmv3.py | null | 5,821 |
class LayoutLMv3Intermediate(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.... | class_definition | 32,038 | 32,609 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlmv3/modeling_layoutlmv3.py | null | 5,822 |
class LayoutLMv3Output(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
... | class_definition | 32,685 | 33,299 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlmv3/modeling_layoutlmv3.py | null | 5,823 |
class LayoutLMv3Model(LayoutLMv3PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
if config.text_embed:
self.embeddings = LayoutLMv3TextEmbeddings(config)
if config.visual_embed:
# use the default pre-training parame... | class_definition | 33,467 | 44,349 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlmv3/modeling_layoutlmv3.py | null | 5,824 |
class LayoutLMv3ClassificationHead(nn.Module):
"""
Head for sentence-level classification tasks. Reference: RobertaClassificationHead
"""
def __init__(self, config, pool_feature=False):
super().__init__()
self.pool_feature = pool_feature
if pool_feature:
self.dense =... | class_definition | 44,352 | 45,275 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlmv3/modeling_layoutlmv3.py | null | 5,825 |
class LayoutLMv3ForTokenClassification(LayoutLMv3PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.layoutlmv3 = LayoutLMv3Model(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
if config.num_labels < ... | class_definition | 45,735 | 49,846 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlmv3/modeling_layoutlmv3.py | null | 5,826 |
class LayoutLMv3ForQuestionAnswering(LayoutLMv3PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.layoutlmv3 = LayoutLMv3Model(config)
self.qa_outputs = LayoutLMv3ClassificationHead(config, pool_feature=False)
sel... | class_definition | 50,202 | 55,632 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlmv3/modeling_layoutlmv3.py | null | 5,827 |
class LayoutLMv3ForSequenceClassification(LayoutLMv3PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.layoutlmv3 = LayoutLMv3Model(config)
self.classifier = LayoutLMv3ClassificationHead(config,... | class_definition | 55,970 | 60,390 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlmv3/modeling_layoutlmv3.py | null | 5,828 |
class LayoutLMv3ImageProcessor(BaseImageProcessor):
r"""
Constructs a LayoutLMv3 image processor.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height, width) dimensions to `(size["height"], size["width"])`. Can be
overridden by `do... | class_definition | 3,512 | 18,323 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlmv3/image_processing_layoutlmv3.py | null | 5,829 |
class LayoutLMv3Processor(ProcessorMixin):
r"""
Constructs a LayoutLMv3 processor which combines a LayoutLMv3 image processor and a LayoutLMv3 tokenizer into a
single processor.
[`LayoutLMv3Processor`] offers all the functionalities you need to prepare data for the model.
It first uses [`LayoutLMv... | class_definition | 905 | 9,142 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlmv3/processing_layoutlmv3.py | null | 5,830 |
class LayoutLMv3Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`LayoutLMv3Model`]. It is used to instantiate an
LayoutLMv3 model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yi... | class_definition | 1,132 | 8,756 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlmv3/configuration_layoutlmv3.py | null | 5,831 |
class LayoutLMv3OnnxConfig(OnnxConfig):
torch_onnx_minimum_version = version.parse("1.12")
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
# The order of inputs is different for question answering and sequence classification
if self.task in ["question-answering", "sequence-cl... | class_definition | 8,759 | 13,203 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlmv3/configuration_layoutlmv3.py | null | 5,832 |
class BloomTokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "fast" Bloom tokenizer (backed by HuggingFace's *tokenizers* library). Based on byte-level
Byte-Pair-Encoding.
This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
be enc... | class_definition | 939 | 6,248 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bloom/tokenization_bloom_fast.py | null | 5,833 |
class FlaxBloomAttention(nn.Module):
config: BloomConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.hidden_size = self.config.hidden_size
self.num_heads = self.config.n_head
self.head_dim = self.hidden_size // self.num_heads
self.attention_softmax_in_fp32 = self.dt... | class_definition | 7,908 | 14,831 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bloom/modeling_flax_bloom.py | null | 5,834 |
class BloomGELU(nn.Module):
def setup(self):
self.dtype = jnp.float32
def __call__(self, x):
return x * 0.5 * (1.0 + tanh(0.79788456 * x * (1 + 0.044715 * x * x))) | class_definition | 14,834 | 15,022 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bloom/modeling_flax_bloom.py | null | 5,835 |
class FlaxBloomMLP(nn.Module):
config: BloomConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
hidden_size = self.config.hidden_size
kernel_init = jax.nn.initializers.normal(self.config.initializer_range)
self.dense_h_to_4h = nn.Dense(4 * hidden_size, dtype=self.dtype, kernel_... | class_definition | 15,025 | 15,991 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bloom/modeling_flax_bloom.py | null | 5,836 |
class FlaxBloomBlock(nn.Module):
config: BloomConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.input_layernorm = nn.LayerNorm(epsilon=self.config.layer_norm_epsilon, dtype=self.dtype)
self.self_attention = FlaxBloomAttention(self.config, dtype=self.dtype)
self.post_atten... | class_definition | 15,994 | 17,953 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bloom/modeling_flax_bloom.py | null | 5,837 |
class FlaxBloomPreTrainedModel(FlaxPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = BloomConfig
base_model_prefix = "transformer"
module_class: nn.Module = None
def __init__... | class_definition | 17,956 | 22,703 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bloom/modeling_flax_bloom.py | null | 5,838 |
class FlaxBloomBlockCollection(nn.Module):
config: BloomConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.layers = [
FlaxBloomBlock(self.config, name=str(layer_number), dtype=self.dtype)
for layer_number in range(self.config.num_hidden_layers)
]
def __... | class_definition | 22,706 | 24,188 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bloom/modeling_flax_bloom.py | null | 5,839 |
class FlaxBloomModule(nn.Module):
config: BloomConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.embed_dim = self.config.hidden_size
# word embeddings (no positional embedding layer)
self.word_embeddings = nn.Embed(
self.config.vocab_size,
self.emb... | class_definition | 24,191 | 26,541 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bloom/modeling_flax_bloom.py | null | 5,840 |
class FlaxBloomModel(FlaxBloomPreTrainedModel):
module_class = FlaxBloomModule | class_definition | 26,798 | 26,880 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bloom/modeling_flax_bloom.py | null | 5,841 |
class FlaxBloomForCausalLMModule(nn.Module):
config: BloomConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.transformer = FlaxBloomModule(self.config, dtype=self.dtype)
self.lm_head = nn.Dense(
self.config.vocab_size,
use_bias=False,
dtype=self.... | class_definition | 26,989 | 28,555 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bloom/modeling_flax_bloom.py | null | 5,842 |
class FlaxBloomForCausalLM(FlaxBloomPreTrainedModel):
module_class = FlaxBloomForCausalLMModule
def prepare_inputs_for_generation(self, input_ids, max_length, attention_mask: Optional[jax.Array] = None):
# initializing the cache
batch_size, seq_length = input_ids.shape
past_key_values ... | class_definition | 28,758 | 29,980 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bloom/modeling_flax_bloom.py | null | 5,843 |
class BloomConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`BloomModel`]. It is used to instantiate a Bloom
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar con... | class_definition | 1,078 | 6,595 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bloom/configuration_bloom.py | null | 5,844 |
class BloomOnnxConfig(OnnxConfigWithPast):
torch_onnx_minimum_version = version.parse("1.12")
def __init__(
self,
config: PretrainedConfig,
task: str = "default",
patching_specs: List[PatchingSpec] = None,
use_past: bool = False,
):
super().__init__(config, t... | class_definition | 6,598 | 10,137 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bloom/configuration_bloom.py | null | 5,845 |
class GeLUFunction(torch.autograd.Function):
@staticmethod
def forward(ctx, input: torch.Tensor) -> torch.Tensor:
ctx.save_for_backward(input)
return bloom_gelu_forward(input)
@staticmethod
def backward(ctx, grad_output: torch.Tensor) -> torch.Tensor:
input = ctx.saved_tensors
... | class_definition | 5,877 | 6,264 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bloom/modeling_bloom.py | null | 5,846 |
class BloomGelu(nn.Module):
"""
BloomBiasGelu wrapper function that make use of the simple function on inference mode to make the model
torchscriptable and use the autograd function in training mode to get the accurate results of the gradients Partly
copied from Megatron-DeepSpeed code and adapted for o... | class_definition | 6,267 | 6,944 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bloom/modeling_bloom.py | null | 5,847 |
class BloomAttention(nn.Module):
def __init__(self, config: BloomConfig, layer_idx: Optional[int] = None):
super().__init__()
self.pretraining_tp = config.pretraining_tp
self.slow_but_exact = config.slow_but_exact
self.hidden_size = config.hidden_size
self.num_heads = confi... | class_definition | 6,947 | 14,159 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bloom/modeling_bloom.py | null | 5,848 |
class BloomMLP(nn.Module):
def __init__(self, config: BloomConfig):
super().__init__()
hidden_size = config.hidden_size
self.pretraining_tp = config.pretraining_tp
self.slow_but_exact = config.slow_but_exact
self.dense_h_to_4h = nn.Linear(hidden_size, 4 * hidden_size)
... | class_definition | 14,162 | 15,513 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bloom/modeling_bloom.py | null | 5,849 |
class BloomBlock(nn.Module):
def __init__(self, config: BloomConfig, layer_idx: Optional[int] = None):
super().__init__()
hidden_size = config.hidden_size
self.input_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
self.num_heads = config.n_head
self.self_at... | class_definition | 15,516 | 17,907 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bloom/modeling_bloom.py | null | 5,850 |
class BloomPreTrainedModel(PreTrainedModel):
config_class = BloomConfig
base_model_prefix = "transformer"
supports_gradient_checkpointing = True
_no_split_modules = ["BloomBlock"]
_skip_keys_device_placement = "past_key_values"
_supports_cache_class = True
_supports_static_cache = True
_... | class_definition | 17,910 | 19,169 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bloom/modeling_bloom.py | null | 5,851 |
class BloomModel(BloomPreTrainedModel):
def __init__(self, config: BloomConfig):
super().__init__(config)
self.embed_dim = config.hidden_size
self.num_heads = config.n_head
# Embedding + LN Embedding
self.word_embeddings = nn.Embedding(config.vocab_size, self.embed_dim)
... | class_definition | 24,554 | 38,409 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bloom/modeling_bloom.py | null | 5,852 |
class BloomForCausalLM(BloomPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config: BloomConfig):
super().__init__(config)
self.transformer = BloomModel(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
... | class_definition | 38,612 | 46,754 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bloom/modeling_bloom.py | null | 5,853 |
class BloomForSequenceClassification(BloomPreTrainedModel):
def __init__(self, config: BloomConfig):
super().__init__(config)
self.num_labels = config.num_labels
self.transformer = BloomModel(config)
self.score = nn.Linear(config.hidden_size, config.num_labels, bias=False)
#... | class_definition | 47,547 | 53,257 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bloom/modeling_bloom.py | null | 5,854 |
class BloomForTokenClassification(BloomPreTrainedModel):
def __init__(self, config: BloomConfig):
super().__init__(config)
self.num_labels = config.num_labels
self.transformer = BloomModel(config)
if hasattr(config, "classifier_dropout") and config.classifier_dropout is not None:
... | class_definition | 53,488 | 57,614 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bloom/modeling_bloom.py | null | 5,855 |
class BloomForQuestionAnswering(BloomPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.transformer = BloomModel(config)
self.qa_outputs = nn.Linear(config.hidden_size, 2)
# Initialize weights and apply final processing
self.post_init()
@add_sta... | class_definition | 57,919 | 61,805 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bloom/modeling_bloom.py | null | 5,856 |
class PhimoeRotaryEmbedding(nn.Module):
def __init__(
self,
config: Optional[PhimoeConfig] = None,
):
super().__init__()
self.config = config
if config.rope_scaling is not None:
self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("ty... | class_definition | 5,620 | 6,912 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phimoe/modeling_phimoe.py | null | 5,857 |
class PhimoeAttention(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: PhimoeConfig, layer_idx: Optional[int] = None):
... | class_definition | 9,551 | 14,639 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phimoe/modeling_phimoe.py | null | 5,858 |
class PhimoeFlashAttention2(PhimoeAttention):
"""
Phimoe flash attention module. This module inherits from `PhimoeAttention` 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 wit... | class_definition | 14,642 | 18,885 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phimoe/modeling_phimoe.py | null | 5,859 |
class PhimoeSdpaAttention(PhimoeAttention):
"""
Phimoe attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
`PhimoeAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
SDPA API.
"""
# Adapted ... | class_definition | 18,888 | 23,518 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phimoe/modeling_phimoe.py | null | 5,860 |
class PhimoeBlockSparseTop2MLP(nn.Module):
def __init__(self, config: PhimoeConfig):
super().__init__()
self.ffn_dim = config.intermediate_size
self.hidden_dim = config.hidden_size
self.w1 = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False)
self.w2 = nn.Linear(self.ffn_di... | class_definition | 23,771 | 24,474 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phimoe/modeling_phimoe.py | null | 5,861 |
class MultiplierProcessor(torch.autograd.Function):
@staticmethod
def forward(
ctx,
scores: torch.Tensor,
multiplier: torch.Tensor,
selected_experts: torch.Tensor,
masked_gates: torch.Tensor,
mask_for_one: torch.Tensor,
):
"""
Forward pass for ... | class_definition | 24,477 | 26,340 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phimoe/modeling_phimoe.py | null | 5,862 |
class PhimoeSparseMoeBlock(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 sta... | class_definition | 31,124 | 34,476 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phimoe/modeling_phimoe.py | null | 5,863 |
class PhimoeDecoderLayer(nn.Module):
def __init__(self, config: PhimoeConfig, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = PHIMOE_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx)
self.block_sparse_moe = PhimoeSparseMoeB... | class_definition | 34,479 | 38,276 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phimoe/modeling_phimoe.py | null | 5,864 |
class PhimoePreTrainedModel(PreTrainedModel):
config_class = PhimoeConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["PhimoeDecoderLayer"]
_skip_keys_device_placement = ["past_key_values"]
_supports_flash_attn_2 = True
_supports_sdpa = True
_s... | class_definition | 39,403 | 40,329 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phimoe/modeling_phimoe.py | null | 5,865 |
class PhimoeModel(PhimoePreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`PhimoeDecoderLayer`]
Args:
config: PhimoeConfig
"""
def __init__(self, config: PhimoeConfig):
super().__init__(config)
self.padding_idx = conf... | class_definition | 45,029 | 60,083 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phimoe/modeling_phimoe.py | null | 5,866 |
class PhimoeForCausalLM(PhimoePreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
super().__init__(config)
self.model = PhimoeModel(config)
self.vocab_size = config.vocab_size
self.lm_head = nn.Linear(config.hidden_size, config.... | class_definition | 60,086 | 68,773 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phimoe/modeling_phimoe.py | null | 5,867 |
class PhimoeForSequenceClassification(PhimoePreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = PhimoeModel(config)
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
# Initialize weight... | class_definition | 69,688 | 73,504 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phimoe/modeling_phimoe.py | null | 5,868 |
class PhimoeConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`PhimoeModel`]. It is used to instantiate a Phi-moe
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a simila... | class_definition | 852 | 10,243 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phimoe/configuration_phimoe.py | null | 5,869 |
class NllbTokenizer(PreTrainedTokenizer):
"""
Construct an NLLB tokenizer.
Adapted from [`RobertaTokenizer`] and [`XLNetTokenizer`]. Based on
[SentencePiece](https://github.com/google/sentencepiece).
The tokenization method is `<tokens> <eos> <language code>` for source language documents, and `<l... | class_definition | 3,474 | 19,064 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nllb/tokenization_nllb.py | null | 5,870 |
class NllbTokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "fast" NLLB tokenizer (backed by HuggingFace's *tokenizers* library). Based on
[BPE](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=BPE#models).
This tokenizer inherits from [`PreTrainedTokenizerFast`] whi... | class_definition | 3,665 | 15,939 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nllb/tokenization_nllb_fast.py | null | 5,871 |
class XLMRobertaEmbeddings(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... | class_definition | 2,117 | 6,300 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm_roberta/modeling_xlm_roberta.py | null | 5,872 |
class XLMRobertaSelfAttention(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_si... | class_definition | 6,408 | 13,762 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm_roberta/modeling_xlm_roberta.py | null | 5,873 |
class XLMRobertaSdpaSelfAttention(XLMRobertaSelfAttention):
def __init__(self, config, position_embedding_type=None):
super().__init__(config, position_embedding_type=position_embedding_type)
self.dropout_prob = config.attention_probs_dropout_prob
self.require_contiguous_qkv = version.parse(... | class_definition | 13,874 | 19,511 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm_roberta/modeling_xlm_roberta.py | null | 5,874 |
class XLMRobertaSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
... | class_definition | 19,616 | 20,228 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm_roberta/modeling_xlm_roberta.py | null | 5,875 |
class XLMRobertaAttention(nn.Module):
def __init__(self, config, position_embedding_type=None):
super().__init__()
self.self = XLM_ROBERTA_SELF_ATTENTION_CLASSES[config._attn_implementation](
config, position_embedding_type=position_embedding_type
)
self.output = XLMRober... | class_definition | 20,475 | 22,616 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm_roberta/modeling_xlm_roberta.py | null | 5,876 |
class XLMRobertaIntermediate(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.... | class_definition | 22,723 | 23,294 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm_roberta/modeling_xlm_roberta.py | null | 5,877 |
class XLMRobertaOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
... | class_definition | 23,395 | 24,009 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm_roberta/modeling_xlm_roberta.py | null | 5,878 |
class XLMRobertaLayer(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 = XLMRobertaAttention(config)
self.is_decoder = config.is_decoder
self.add_cross_attentio... | class_definition | 24,109 | 28,046 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm_roberta/modeling_xlm_roberta.py | null | 5,879 |
class XLMRobertaEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([XLMRobertaLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
hidden_st... | class_definition | 28,148 | 31,950 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm_roberta/modeling_xlm_roberta.py | null | 5,880 |
class XLMRobertaPooler(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... | class_definition | 32,051 | 32,616 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm_roberta/modeling_xlm_roberta.py | null | 5,881 |
class XLMRobertaPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = XLMRobertaConfig
base_model_prefix = "roberta"
supports_gradient_checkpointing = True
_no_spl... | class_definition | 32,726 | 34,069 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm_roberta/modeling_xlm_roberta.py | null | 5,882 |
class XLMRobertaModel(XLMRobertaPreTrainedModel):
"""
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](https://arxi... | class_definition | 37,887 | 48,654 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm_roberta/modeling_xlm_roberta.py | null | 5,883 |
class XLMRobertaForCausalLM(XLMRobertaPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.decoder.weight", "lm_head.decoder.bias"]
def __init__(self, config):
super().__init__(config)
if not config.is_decoder:
logger.warning("If you want to use `XLMRobertaLMHeadModel`... | class_definition | 48,925 | 55,616 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm_roberta/modeling_xlm_roberta.py | null | 5,884 |
class XLMRobertaForMaskedLM(XLMRobertaPreTrainedModel):
_tied_weights_keys = ["lm_head.decoder.weight", "lm_head.decoder.bias"]
def __init__(self, config):
super().__init__(config)
if config.is_decoder:
logger.warning(
"If you want to use `XLMRobertaForMaskedLM` mak... | class_definition | 55,871 | 59,780 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm_roberta/modeling_xlm_roberta.py | null | 5,885 |
class XLMRobertaLMHead(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.d... | class_definition | 59,856 | 60,921 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm_roberta/modeling_xlm_roberta.py | null | 5,886 |
class XLMRobertaForSequenceClassification(XLMRobertaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.roberta = XLMRobertaModel(config, add_pooling_layer=False)
self.classifier = XLMRobertaCla... | class_definition | 61,296 | 65,314 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm_roberta/modeling_xlm_roberta.py | null | 5,887 |
class XLMRobertaForMultipleChoice(XLMRobertaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.roberta = XLMRobertaModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, 1)
# Initialize we... | class_definition | 65,690 | 69,430 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm_roberta/modeling_xlm_roberta.py | null | 5,888 |
class XLMRobertaForTokenClassification(XLMRobertaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.roberta = XLMRobertaModel(config, add_pooling_layer=False)
classifier_dropout = (
config.classifier_dropout if... | class_definition | 69,809 | 72,979 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm_roberta/modeling_xlm_roberta.py | null | 5,889 |
class XLMRobertaClassificationHead(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.classif... | class_definition | 73,092 | 73,868 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm_roberta/modeling_xlm_roberta.py | null | 5,890 |
class XLMRobertaForQuestionAnswering(XLMRobertaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.roberta = XLMRobertaModel(config, add_pooling_layer=False)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels... | class_definition | 74,303 | 78,646 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm_roberta/modeling_xlm_roberta.py | null | 5,891 |
class XLMRobertaTokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "fast" XLM-RoBERTa tokenizer (backed by HuggingFace's *tokenizers* library). Adapted from
[`RobertaTokenizer`] and [`XLNetTokenizer`]. Based on
[BPE](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=BPE... | class_definition | 1,242 | 7,919 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm_roberta/tokenization_xlm_roberta_fast.py | null | 5,892 |
class XLMRobertaTokenizer(PreTrainedTokenizer):
"""
Adapted from [`RobertaTokenizer`] and [`XLNetTokenizer`]. 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 s... | class_definition | 1,068 | 12,704 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm_roberta/tokenization_xlm_roberta.py | null | 5,893 |
class TFXLMRobertaEmbeddings(keras.layers.Layer):
"""
Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
"""
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.padding_idx = 1
self.config = config
self.hidden_size = config.hi... | class_definition | 7,860 | 12,060 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm_roberta/modeling_tf_xlm_roberta.py | null | 5,894 |
class TFXLMRobertaPooler(keras.layers.Layer):
def __init__(self, config: XLMRobertaConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.hidden_size,
kernel_initializer=get_initializer(config.initializer_range),
activation="t... | class_definition | 12,154 | 13,135 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm_roberta/modeling_tf_xlm_roberta.py | null | 5,895 |
class TFXLMRobertaSelfAttention(keras.layers.Layer):
def __init__(self, config: XLMRobertaConfig, **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 o... | class_definition | 13,236 | 20,071 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm_roberta/modeling_tf_xlm_roberta.py | null | 5,896 |
class TFXLMRobertaSelfOutput(keras.layers.Layer):
def __init__(self, config: XLMRobertaConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
se... | class_definition | 20,169 | 21,508 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm_roberta/modeling_tf_xlm_roberta.py | null | 5,897 |
class TFXLMRobertaAttention(keras.layers.Layer):
def __init__(self, config: XLMRobertaConfig, **kwargs):
super().__init__(**kwargs)
self.self_attention = TFXLMRobertaSelfAttention(config, name="self")
self.dense_output = TFXLMRobertaSelfOutput(config, name="output")
def prune_heads(sel... | class_definition | 21,605 | 23,461 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm_roberta/modeling_tf_xlm_roberta.py | null | 5,898 |
class TFXLMRobertaIntermediate(keras.layers.Layer):
def __init__(self, config: XLMRobertaConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
... | class_definition | 23,561 | 24,595 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm_roberta/modeling_tf_xlm_roberta.py | null | 5,899 |
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