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 JiebaPreTokenizer:
def __init__(self, vocab) -> None:
self.vocab = vocab
self.normalizers = normalizers.BertNormalizer(
clean_text=False,
handle_chinese_chars=True,
strip_accents=False,
lowercase=False,
)
try:
import r... | class_definition | 766 | 2,651 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/tokenization_utils.py | null | 6,700 |
class RoFormerSinusoidalPositionalEmbedding(nn.Embedding):
"""This module produces sinusoidal positional embeddings of any length."""
def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int] = None) -> None:
super().__init__(num_positions, embedding_dim)
self.weight... | class_definition | 1,851 | 3,420 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_roformer.py | null | 6,701 |
class RoFormerEmbeddings(nn.Module):
"""Construct the embeddings from word and token_type embeddings."""
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.embedding_size, padding_idx=config.pad_token_id)
self.token_type_embeddin... | class_definition | 6,419 | 7,817 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_roformer.py | null | 6,702 |
class RoFormerSelfAttention(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 nu... | class_definition | 7,820 | 14,950 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_roformer.py | null | 6,703 |
class RoFormerSelfOutput(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)
d... | class_definition | 15,041 | 15,651 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_roformer.py | null | 6,704 |
class RoFormerAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.self = RoFormerSelfAttention(config)
self.output = RoFormerSelfOutput(config)
self.pruned_heads = set()
# Copied from transformers.models.bert.modeling_bert.BertAttention.prune_heads
def ... | class_definition | 15,654 | 17,579 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_roformer.py | null | 6,705 |
class RoFormerIntermediate(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.in... | class_definition | 17,672 | 18,241 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_roformer.py | null | 6,706 |
class RoFormerOutput(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 | 18,328 | 18,940 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_roformer.py | null | 6,707 |
class RoFormerLayer(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 = RoFormerAttention(config)
self.is_decoder = config.is_decoder
self.add_cross_attention = ... | class_definition | 18,943 | 22,699 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_roformer.py | null | 6,708 |
class RoFormerEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.embed_positions = RoFormerSinusoidalPositionalEmbedding(
config.max_position_embeddings, config.hidden_size // config.num_attention_heads
)
self.layer = nn.M... | class_definition | 22,702 | 26,748 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_roformer.py | null | 6,709 |
class RoFormerPredictionHeadTransform(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.embedding_size)
if isinstance(config.hidden_act, str):
self.transform_act_fn = ACT2FN[config.hidden_act]
else:
se... | class_definition | 26,751 | 27,431 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_roformer.py | null | 6,710 |
class RoFormerLMPredictionHead(nn.Module):
def __init__(self, config):
super().__init__()
self.transform = RoFormerPredictionHeadTransform(config)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.decoder = nn.... | class_definition | 27,434 | 28,285 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_roformer.py | null | 6,711 |
class RoFormerOnlyMLMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = RoFormerLMPredictionHead(config)
def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
prediction_scores = self.predictions(sequence_output)
return prediction_scor... | class_definition | 28,377 | 28,699 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_roformer.py | null | 6,712 |
class RoFormerPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = RoFormerConfig
load_tf_weights = load_tf_weights_in_roformer
base_model_prefix = "roformer"
sup... | class_definition | 28,702 | 29,957 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_roformer.py | null | 6,713 |
class RoFormerModel(RoFormerPreTrainedModel):
"""
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://arxiv.or... | class_definition | 33,091 | 41,780 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_roformer.py | null | 6,714 |
class RoFormerForMaskedLM(RoFormerPreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder.bias", "cls.predictions.decoder.weight"]
def __init__(self, config):
super().__init__(config)
if config.is_decoder:
logger.warning(
"If you want to use `RoFormerForMas... | class_definition | 41,893 | 46,154 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_roformer.py | null | 6,715 |
class RoFormerForCausalLM(RoFormerPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["cls.predictions.decoder.bias", "cls.predictions.decoder.weight"]
def __init__(self, config):
super().__init__(config)
if not config.is_decoder:
logger.warning("If you want to use `RoFormerFo... | class_definition | 46,293 | 52,875 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_roformer.py | null | 6,716 |
class RoFormerClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.out_proj = nn... | class_definition | 52,878 | 53,558 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_roformer.py | null | 6,717 |
class RoFormerForSequenceClassification(RoFormerPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.roformer = RoFormerModel(config)
self.classifier = RoFormerClassificationHead(config)
# Initialize weights and appl... | class_definition | 53,788 | 57,440 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_roformer.py | null | 6,718 |
class RoFormerForMultipleChoice(RoFormerPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.roformer = RoFormerModel(config)
self.sequence_summary = SequenceSummary(config)
self.classifier = nn.Linear(config.hidden_size, 1)
# Initialize weights and a... | class_definition | 57,679 | 61,042 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_roformer.py | null | 6,719 |
class RoFormerForTokenClassification(RoFormerPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.roformer = RoFormerModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(con... | class_definition | 61,279 | 63,911 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_roformer.py | null | 6,720 |
class RoFormerForQuestionAnswering(RoFormerPreTrainedModel):
def __init__(self, config):
super().__init__(config)
config.num_labels = 2
self.num_labels = config.num_labels
self.roformer = RoFormerModel(config)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_label... | class_definition | 64,206 | 68,351 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_roformer.py | null | 6,721 |
class FlaxRoFormerEmbeddings(nn.Module):
"""Construct the embeddings from word and token_type embeddings."""
config: RoFormerConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.word_embeddings = nn.Embed(
self.config.vocab_size,
s... | class_definition | 5,904 | 7,289 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_flax_roformer.py | null | 6,722 |
class FlaxRoFormerSelfAttention(nn.Module):
config: RoFormerConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self) -> None:
if self.config.hidden_size % self.config.num_attention_heads != 0:
raise ValueError(
"`config.hidden_size`: {self.co... | class_definition | 7,292 | 12,145 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_flax_roformer.py | null | 6,723 |
class FlaxRoFormerSelfOutput(nn.Module):
config: RoFormerConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.dense = nn.Dense(
self.config.hidden_size,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
... | class_definition | 12,245 | 13,067 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_flax_roformer.py | null | 6,724 |
class FlaxRoFormerAttention(nn.Module):
config: RoFormerConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.self = FlaxRoFormerSelfAttention(self.config, dtype=self.dtype)
self.output = FlaxRoFormerSelfOutput(self.config, dtype=self.dtype)
def __call__(
self,
hi... | class_definition | 13,070 | 14,358 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_flax_roformer.py | null | 6,725 |
class FlaxRoFormerIntermediate(nn.Module):
config: RoFormerConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.dense = nn.Dense(
self.config.intermediate_size,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
... | class_definition | 14,460 | 15,046 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_flax_roformer.py | null | 6,726 |
class FlaxRoFormerOutput(nn.Module):
config: RoFormerConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.dense = nn.Dense(
self.config.hidden_size,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
dtyp... | class_definition | 15,142 | 15,968 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_flax_roformer.py | null | 6,727 |
class FlaxRoFormerLayer(nn.Module):
config: RoFormerConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.attention = FlaxRoFormerAttention(self.config, dtype=self.dtype)
self.intermediate = FlaxRoFormerIntermediate(self.config, dtype=self.dtype)
... | class_definition | 15,971 | 17,185 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_flax_roformer.py | null | 6,728 |
class FlaxRoFormerLayerCollection(nn.Module):
config: RoFormerConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.layers = [
FlaxRoFormerLayer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.num_hidden_layers)
]
... | class_definition | 17,188 | 19,270 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_flax_roformer.py | null | 6,729 |
class FlaxRoFormerEncoder(nn.Module):
config: RoFormerConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.embed_positions = create_sinusoidal_positions(
self.config.max_position_embeddings, self.config.hidden_size // self.config.num_attention_head... | class_definition | 19,273 | 20,346 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_flax_roformer.py | null | 6,730 |
class FlaxRoFormerPredictionHeadTransform(nn.Module):
config: RoFormerConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.dense = nn.Dense(self.config.hidden_size, dtype=self.dtype)
self.activation = ACT2FN[self.config.hidden_act]
self.LayerNorm = nn.LayerNorm(epsilon=self.c... | class_definition | 20,459 | 21,008 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_flax_roformer.py | null | 6,731 |
class FlaxRoFormerLMPredictionHead(nn.Module):
config: RoFormerConfig
dtype: jnp.dtype = jnp.float32
bias_init: Callable[..., np.ndarray] = jax.nn.initializers.zeros
def setup(self):
self.transform = FlaxRoFormerPredictionHeadTransform(self.config, dtype=self.dtype)
self.decoder = nn.De... | class_definition | 21,114 | 22,022 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_flax_roformer.py | null | 6,732 |
class FlaxRoFormerOnlyMLMHead(nn.Module):
config: RoFormerConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.predictions = FlaxRoFormerLMPredictionHead(self.config, dtype=self.dtype)
def __call__(self, hidden_states, shared_embedding=None):
hidden_states = self.predictions(hid... | class_definition | 22,123 | 22,518 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_flax_roformer.py | null | 6,733 |
class FlaxRoFormerClassificationHead(nn.Module):
config: RoFormerConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.dense = nn.Dense(
self.config.hidden_size,
dtype=self.dtype,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
... | class_definition | 22,521 | 23,658 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_flax_roformer.py | null | 6,734 |
class FlaxRoFormerPreTrainedModel(FlaxPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = RoFormerConfig
base_model_prefix = "roformer"
module_class: nn.Module = None
def __ini... | class_definition | 23,661 | 27,203 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_flax_roformer.py | null | 6,735 |
class FlaxRoFormerModule(nn.Module):
config: RoFormerConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.embeddings = FlaxRoFormerEmbeddings(self.config, dtype=self.dtype)
self.encoder = FlaxRoFormerEncoder(self.config, dtype=self.dtype)
def __ca... | class_definition | 27,206 | 28,515 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_flax_roformer.py | null | 6,736 |
class FlaxRoFormerModel(FlaxRoFormerPreTrainedModel):
module_class = FlaxRoFormerModule | class_definition | 28,679 | 28,770 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_flax_roformer.py | null | 6,737 |
class FlaxRoFormerForMaskedLMModule(nn.Module):
config: RoFormerConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.roformer = FlaxRoFormerModule(config=self.config, dtype=self.dtype)
self.cls = FlaxRoFormerOnlyMLMHead(config=self.config, dtype=self.dtype)
def __call__(
... | class_definition | 28,882 | 30,394 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_flax_roformer.py | null | 6,738 |
class FlaxRoFormerForMaskedLM(FlaxRoFormerPreTrainedModel):
module_class = FlaxRoFormerForMaskedLMModule | class_definition | 30,507 | 30,615 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_flax_roformer.py | null | 6,739 |
class FlaxRoFormerForSequenceClassificationModule(nn.Module):
config: RoFormerConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.roformer = FlaxRoFormerModule(config=self.config, dtype=self.dtype)
self.classifier = FlaxRoFormerClassificationHead(config=self.config, dtype=self.dtype... | class_definition | 30,770 | 32,079 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_flax_roformer.py | null | 6,740 |
class FlaxRoFormerForSequenceClassification(FlaxRoFormerPreTrainedModel):
module_class = FlaxRoFormerForSequenceClassificationModule | class_definition | 32,309 | 32,445 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_flax_roformer.py | null | 6,741 |
class FlaxRoFormerForMultipleChoiceModule(nn.Module):
config: RoFormerConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.roformer = FlaxRoFormerModule(config=self.config, dtype=self.dtype)
self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)
self.classifier = nn.... | class_definition | 32,605 | 34,448 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_flax_roformer.py | null | 6,742 |
class FlaxRoFormerForMultipleChoice(FlaxRoFormerPreTrainedModel):
module_class = FlaxRoFormerForMultipleChoiceModule | class_definition | 34,687 | 34,807 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_flax_roformer.py | null | 6,743 |
class FlaxRoFormerForTokenClassificationModule(nn.Module):
config: RoFormerConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.roformer = FlaxRoFormerModule(config=self.config, dtype=self.dtype)
self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)
self.classifier ... | class_definition | 35,100 | 36,505 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_flax_roformer.py | null | 6,744 |
class FlaxRoFormerForTokenClassification(FlaxRoFormerPreTrainedModel):
module_class = FlaxRoFormerForTokenClassificationModule | class_definition | 36,742 | 36,872 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_flax_roformer.py | null | 6,745 |
class FlaxRoFormerForQuestionAnsweringModule(nn.Module):
config: RoFormerConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.roformer = FlaxRoFormerModule(config=self.config, dtype=self.dtype)
self.qa_outputs = nn.Dense(self.config.num_labels, dtype=self.dtype)
def __call__(
... | class_definition | 37,026 | 38,521 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_flax_roformer.py | null | 6,746 |
class FlaxRoFormerForQuestionAnswering(FlaxRoFormerPreTrainedModel):
module_class = FlaxRoFormerForQuestionAnsweringModule | class_definition | 38,816 | 38,942 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_flax_roformer.py | null | 6,747 |
class RoFormerConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`RoFormerModel`]. It is used to instantiate an
RoFormer model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a ... | class_definition | 880 | 6,249 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/configuration_roformer.py | null | 6,748 |
class RoFormerOnnxConfig(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"}
dynamic_axis = {0: "bat... | class_definition | 6,252 | 6,802 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/configuration_roformer.py | null | 6,749 |
class BasicTokenizer:
"""
Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.).
Args:
do_lower_case (`bool`, *optional*, defaults to `True`):
Whether or not to lowercase the input when tokenizing.
never_split (`Iterable`, *opti... | class_definition | 1,747 | 8,495 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/tokenization_roformer.py | null | 6,750 |
class WordpieceTokenizer:
"""Runs WordPiece tokenization."""
def __init__(self, vocab, unk_token, max_input_chars_per_word=100):
self.vocab = vocab
self.unk_token = unk_token
self.max_input_chars_per_word = max_input_chars_per_word
def tokenize(self, text):
"""
Toke... | class_definition | 8,574 | 10,462 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/tokenization_roformer.py | null | 6,751 |
class RoFormerTokenizer(PreTrainedTokenizer):
r"""
Construct a RoFormer tokenizer. Based on [Rust Jieba](https://pypi.org/project/rjieba/).
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding ... | class_definition | 10,465 | 21,976 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/tokenization_roformer.py | null | 6,752 |
class RoFormerTokenizerFast(PreTrainedTokenizerFast):
r"""
Construct a "fast" RoFormer tokenizer (backed by HuggingFace's *tokenizers* library).
[`RoFormerTokenizerFast`] is almost identical to [`BertTokenizerFast`] and runs end-to-end tokenization:
punctuation splitting and wordpiece. There are some d... | class_definition | 1,149 | 6,678 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/tokenization_roformer_fast.py | null | 6,753 |
class TFRoFormerSinusoidalPositionalEmbedding(keras.layers.Layer):
"""This module produces sinusoidal positional embeddings of any length."""
def __init__(self, num_positions: int, embedding_dim: int, **kwargs):
super().__init__(**kwargs)
if embedding_dim % 2 != 0:
raise NotImpleme... | class_definition | 1,887 | 4,058 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_tf_roformer.py | null | 6,754 |
class TFRoFormerEmbeddings(keras.layers.Layer):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config: RoFormerConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.embedding_size = config.embedding_size
self.i... | class_definition | 4,061 | 6,597 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_tf_roformer.py | null | 6,755 |
class TFRoFormerSelfAttention(keras.layers.Layer):
def __init__(self, config: RoFormerConfig, **kwargs):
super().__init__(**kwargs)
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of th... | class_definition | 6,600 | 13,628 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_tf_roformer.py | null | 6,756 |
class TFRoFormerSelfOutput(keras.layers.Layer):
def __init__(self, config: RoFormerConfig, **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 | 13,724 | 15,059 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_tf_roformer.py | null | 6,757 |
class TFRoFormerAttention(keras.layers.Layer):
def __init__(self, config: RoFormerConfig, **kwargs):
super().__init__(**kwargs)
self.self_attention = TFRoFormerSelfAttention(config, name="self")
self.dense_output = TFRoFormerSelfOutput(config, name="output")
def prune_heads(self, heads... | class_definition | 15,062 | 16,664 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_tf_roformer.py | null | 6,758 |
class TFRoFormerIntermediate(keras.layers.Layer):
def __init__(self, config: RoFormerConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
... | class_definition | 16,762 | 17,792 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_tf_roformer.py | null | 6,759 |
class TFRoFormerOutput(keras.layers.Layer):
def __init__(self, config: RoFormerConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
self.Layer... | class_definition | 17,884 | 19,221 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_tf_roformer.py | null | 6,760 |
class TFRoFormerLayer(keras.layers.Layer):
def __init__(self, config: RoFormerConfig, **kwargs):
super().__init__(**kwargs)
self.attention = TFRoFormerAttention(config, name="attention")
self.intermediate = TFRoFormerIntermediate(config, name="intermediate")
self.roformer_output = T... | class_definition | 19,224 | 21,121 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_tf_roformer.py | null | 6,761 |
class TFRoFormerEncoder(keras.layers.Layer):
def __init__(self, config: RoFormerConfig, **kwargs):
super().__init__(**kwargs)
self.embed_positions = TFRoFormerSinusoidalPositionalEmbedding(
config.max_position_embeddings,
config.hidden_size // config.num_attention_heads,
... | class_definition | 21,124 | 23,743 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_tf_roformer.py | null | 6,762 |
class TFRoFormerPredictionHeadTransform(keras.layers.Layer):
def __init__(self, config: RoFormerConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.embedding_size,
kernel_initializer=get_initializer(config.initializer_range),
... | class_definition | 23,746 | 25,157 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_tf_roformer.py | null | 6,763 |
class TFRoFormerLMPredictionHead(keras.layers.Layer):
def __init__(self, config: RoFormerConfig, input_embeddings: keras.layers.Layer, **kwargs):
super().__init__(**kwargs)
self.config = config
self.embedding_size = config.embedding_size
self.transform = TFRoFormerPredictionHeadTra... | class_definition | 25,160 | 27,140 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_tf_roformer.py | null | 6,764 |
class TFRoFormerMLMHead(keras.layers.Layer):
def __init__(self, config: RoFormerConfig, input_embeddings: keras.layers.Layer, **kwargs):
super().__init__(**kwargs)
self.predictions = TFRoFormerLMPredictionHead(config, input_embeddings, name="predictions")
def call(self, sequence_output: tf.Ten... | class_definition | 27,233 | 27,948 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_tf_roformer.py | null | 6,765 |
class TFRoFormerMainLayer(keras.layers.Layer):
config_class = RoFormerConfig
def __init__(self, config: RoFormerConfig, add_pooling_layer: bool = True, **kwargs):
super().__init__(**kwargs)
self.config = config
self.embeddings = TFRoFormerEmbeddings(config, name="embeddings")
... | class_definition | 27,971 | 33,585 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_tf_roformer.py | null | 6,766 |
class TFRoFormerPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = RoFormerConfig
base_model_prefix = "roformer" | class_definition | 33,588 | 33,854 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_tf_roformer.py | null | 6,767 |
class TFRoFormerModel(TFRoFormerPreTrainedModel):
def __init__(self, config: RoFormerConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.roformer = TFRoFormerMainLayer(config, name="roformer")
@unpack_inputs
@add_start_docstrings_to_model_forward(ROFORMER_INPUTS_DO... | class_definition | 39,531 | 41,328 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_tf_roformer.py | null | 6,768 |
class TFRoFormerForMaskedLM(TFRoFormerPreTrainedModel, TFMaskedLanguageModelingLoss):
def __init__(self, config: RoFormerConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
if config.is_decoder:
logger.warning(
"If you want to use `TFRoFormerForMasked... | class_definition | 41,441 | 44,857 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_tf_roformer.py | null | 6,769 |
class TFRoFormerForCausalLM(TFRoFormerPreTrainedModel, TFCausalLanguageModelingLoss):
def __init__(self, config: RoFormerConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
if not config.is_decoder:
logger.warning("If you want to use `TFRoFormerForCausalLM` as a stan... | class_definition | 44,996 | 48,184 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_tf_roformer.py | null | 6,770 |
class TFRoFormerClassificationHead(keras.layers.Layer):
"""Head for sentence-level classification tasks."""
def __init__(self, config: RoFormerConfig, *inputs, **kwargs):
super().__init__(*inputs, **kwargs)
self.dense = keras.layers.Dense(
units=config.hidden_size, kernel_initializ... | class_definition | 48,187 | 50,031 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_tf_roformer.py | null | 6,771 |
class TFRoFormerForSequenceClassification(TFRoFormerPreTrainedModel, TFSequenceClassificationLoss):
def __init__(self, config: RoFormerConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.roformer = TFRoFormerMainLayer(config, nam... | class_definition | 50,213 | 53,304 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_tf_roformer.py | null | 6,772 |
class TFRoFormerForMultipleChoice(TFRoFormerPreTrainedModel, TFMultipleChoiceLoss):
def __init__(self, config: RoFormerConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.roformer = TFRoFormerMainLayer(config, name="roformer")
self.sequence_summary = TFSequenceSumma... | class_definition | 53,543 | 57,992 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_tf_roformer.py | null | 6,773 |
class TFRoFormerForTokenClassification(TFRoFormerPreTrainedModel, TFTokenClassificationLoss):
def __init__(self, config: RoFormerConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.roformer = TFRoFormerMainLayer(config, name="rof... | class_definition | 58,229 | 61,454 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_tf_roformer.py | null | 6,774 |
class TFRoFormerForQuestionAnswering(TFRoFormerPreTrainedModel, TFQuestionAnsweringLoss):
def __init__(self, config: RoFormerConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.roformer = TFRoFormerMainLayer(config, name="roforme... | class_definition | 61,748 | 65,912 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roformer/modeling_tf_roformer.py | null | 6,775 |
class TimesformerPatchEmbeddings(nn.Module):
"""Image to Patch Embedding"""
def __init__(self, config):
super().__init__()
image_size = config.image_size
patch_size = config.patch_size
image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size... | class_definition | 1,480 | 2,673 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/timesformer/modeling_timesformer.py | null | 6,776 |
class TimesformerEmbeddings(nn.Module):
"""
Construct the patch and position embeddings.
"""
def __init__(self, config):
super().__init__()
embed_dim = config.hidden_size
num_frames = config.num_frames
drop_rate = config.hidden_dropout_prob
attention_type = conf... | class_definition | 2,676 | 6,416 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/timesformer/modeling_timesformer.py | null | 6,777 |
class TimeSformerDropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
def __init__(self, drop_prob: Optional[float] = None) -> None:
super().__init__()
self.drop_prob = drop_prob
def forward(self, hidden_states: torch.Tensor) -... | class_definition | 7,663 | 8,148 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/timesformer/modeling_timesformer.py | null | 6,778 |
class TimesformerSelfAttention(nn.Module):
def __init__(self, config: TimesformerConfig):
super().__init__()
num_heads = config.num_attention_heads
qkv_bias = config.qkv_bias
attention_dropout_prob = config.attention_probs_dropout_prob
self.num_heads = num_heads
hea... | class_definition | 8,290 | 9,640 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/timesformer/modeling_timesformer.py | null | 6,779 |
class TimesformerSelfOutput(nn.Module):
"""
The residual connection is defined in TimesformerLayer instead of here (as is the case with other models), due to
the layernorm applied before each block.
"""
def __init__(self, config: TimesformerConfig) -> None:
super().__init__()
self.d... | class_definition | 9,643 | 10,282 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/timesformer/modeling_timesformer.py | null | 6,780 |
class TimeSformerAttention(nn.Module):
def __init__(self, config: TimesformerConfig) -> None:
super().__init__()
self.attention = TimesformerSelfAttention(config)
self.output = TimesformerSelfOutput(config)
def forward(
self,
hidden_states: torch.Tensor,
output_a... | class_definition | 10,285 | 10,947 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/timesformer/modeling_timesformer.py | null | 6,781 |
class TimesformerIntermediate(nn.Module):
def __init__(self, config: TimesformerConfig) -> None:
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
if isinstance(config.hidden_act, str):
... | class_definition | 11,089 | 11,804 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/timesformer/modeling_timesformer.py | null | 6,782 |
class TimesformerOutput(nn.Module):
def __init__(self, config: TimesformerConfig) -> None:
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor) -> torc... | class_definition | 11,807 | 12,268 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/timesformer/modeling_timesformer.py | null | 6,783 |
class TimesformerLayer(nn.Module):
def __init__(self, config: TimesformerConfig, layer_index: int) -> None:
super().__init__()
attention_type = config.attention_type
drop_path_rates = [
x.item() for x in torch.linspace(0, config.drop_path_rate, config.num_hidden_layers)
... | class_definition | 12,410 | 18,502 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/timesformer/modeling_timesformer.py | null | 6,784 |
class TimesformerEncoder(nn.Module):
def __init__(self, config: TimesformerConfig) -> None:
super().__init__()
self.config = config
self.layer = nn.ModuleList([TimesformerLayer(config, ind) for ind in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forw... | class_definition | 18,505 | 20,274 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/timesformer/modeling_timesformer.py | null | 6,785 |
class TimesformerPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = TimesformerConfig
base_model_prefix = "timesformer"
main_input_name = "pixel_values"
support... | class_definition | 20,277 | 21,385 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/timesformer/modeling_timesformer.py | null | 6,786 |
class TimesformerModel(TimesformerPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.embeddings = TimesformerEmbeddings(config)
self.encoder = TimesformerEncoder(config)
self.layernorm = nn.LayerNorm(config.hidden_size, eps=conf... | class_definition | 23,022 | 28,479 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/timesformer/modeling_timesformer.py | null | 6,787 |
class TimesformerForVideoClassification(TimesformerPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.timesformer = TimesformerModel(config)
# Classifier head
self.classifier = nn.Linear(config.hidden_size, config... | class_definition | 28,709 | 35,192 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/timesformer/modeling_timesformer.py | null | 6,788 |
class TimesformerConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`TimesformerModel`]. It is used to instantiate a
TimeSformer model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will ... | class_definition | 788 | 5,533 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/timesformer/configuration_timesformer.py | null | 6,789 |
class DeformableDetrFeatureExtractor(DeformableDetrImageProcessor):
def __init__(self, *args, **kwargs) -> None:
warnings.warn(
"The class DeformableDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
" Please use DeformableDetrImageProcessor instead... | class_definition | 1,143 | 1,545 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deformable_detr/feature_extraction_deformable_detr.py | null | 6,790 |
class MultiScaleDeformableAttentionFunction(Function):
@staticmethod
def forward(
context,
value,
value_spatial_shapes,
value_level_start_index,
sampling_locations,
attention_weights,
im2col_step,
):
context.im2col_step = im2col_step
ou... | class_definition | 2,738 | 4,182 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deformable_detr/modeling_deformable_detr.py | null | 6,791 |
class DeformableDetrDecoderOutput(ModelOutput):
"""
Base class for outputs of the DeformableDetrDecoder. This class adds two attributes to
BaseModelOutputWithCrossAttentions, namely:
- a stacked tensor of intermediate decoder hidden states (i.e. the output of each decoder layer)
- a stacked tensor o... | class_definition | 4,196 | 6,929 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deformable_detr/modeling_deformable_detr.py | null | 6,792 |
class DeformableDetrModelOutput(ModelOutput):
"""
Base class for outputs of the Deformable DETR encoder-decoder model.
Args:
init_reference_points (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`):
Initial reference points sent through the Transformer decoder.
last_... | class_definition | 6,943 | 11,810 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deformable_detr/modeling_deformable_detr.py | null | 6,793 |
class DeformableDetrObjectDetectionOutput(ModelOutput):
"""
Output type of [`DeformableDetrForObjectDetection`].
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` are provided)):
Total loss as a linear combination of a negative log-likehood (cross-entro... | class_definition | 11,824 | 18,375 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deformable_detr/modeling_deformable_detr.py | null | 6,794 |
class DeformableDetrFrozenBatchNorm2d(nn.Module):
"""
BatchNorm2d where the batch statistics and the affine parameters are fixed.
Copy-paste from torchvision.misc.ops with added eps before rqsrt, without which any other models than
torchvision.models.resnet[18,34,50,101] produce nans.
"""
def ... | class_definition | 18,731 | 20,253 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deformable_detr/modeling_deformable_detr.py | null | 6,795 |
class DeformableDetrConvEncoder(nn.Module):
"""
Convolutional backbone, using either the AutoBackbone API or one from the timm library.
nn.BatchNorm2d layers are replaced by DeformableDetrFrozenBatchNorm2d as defined above.
"""
def __init__(self, config):
super().__init__()
self.... | class_definition | 21,192 | 24,514 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deformable_detr/modeling_deformable_detr.py | null | 6,796 |
class DeformableDetrConvModel(nn.Module):
"""
This module adds 2D position embeddings to all intermediate feature maps of the convolutional encoder.
"""
def __init__(self, conv_encoder, position_embedding):
super().__init__()
self.conv_encoder = conv_encoder
self.position_embedd... | class_definition | 24,610 | 25,371 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deformable_detr/modeling_deformable_detr.py | null | 6,797 |
class DeformableDetrSinePositionEmbedding(nn.Module):
"""
This is a more standard version of the position embedding, very similar to the one used by the Attention is all you
need paper, generalized to work on images.
"""
def __init__(self, embedding_dim=64, temperature=10000, normalize=False, scale... | class_definition | 25,374 | 27,134 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deformable_detr/modeling_deformable_detr.py | null | 6,798 |
class DeformableDetrLearnedPositionEmbedding(nn.Module):
"""
This module learns positional embeddings up to a fixed maximum size.
"""
def __init__(self, embedding_dim=256):
super().__init__()
self.row_embeddings = nn.Embedding(50, embedding_dim)
self.column_embeddings = nn.Embed... | class_definition | 27,219 | 28,170 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deformable_detr/modeling_deformable_detr.py | null | 6,799 |
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