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 UnivNetKernelPredictorResidualBlock(nn.Module):
"""
Implementation of the residual block for the kernel predictor network inside each location variable convolution
block (LVCBlock).
Parameters:
config: (`UnivNetConfig`):
Config for the `UnivNetModel` model.
"""
def __... | class_definition | 1,832 | 3,712 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/univnet/modeling_univnet.py | null | 4,500 |
class UnivNetKernelPredictor(nn.Module):
"""
Implementation of the kernel predictor network which supplies the kernel and bias for the location variable
convolutional layers (LVCs) in each UnivNet LVCBlock.
Based on the KernelPredictor implementation in
[maum-ai/univnet](https://github.com/maum-ai/... | class_definition | 3,715 | 8,679 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/univnet/modeling_univnet.py | null | 4,501 |
class UnivNetLvcResidualBlock(nn.Module):
"""
Implementation of the location variable convolution (LVC) residual block for the UnivNet residual network.
Parameters:
config: (`UnivNetConfig`):
Config for the `UnivNetModel` model.
kernel_size (`int`):
The kernel size f... | class_definition | 8,682 | 14,544 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/univnet/modeling_univnet.py | null | 4,502 |
class UnivNetLvcBlock(nn.Module):
"""
Implementation of the location variable convolution (LVC) residual block of the UnivNet residual block. Includes a
`UnivNetKernelPredictor` inside to predict the kernels and biases of the LVC layers.
Based on LVCBlock in
[maum-ai/univnet](https://github.com/mau... | class_definition | 14,547 | 17,767 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/univnet/modeling_univnet.py | null | 4,503 |
class UnivNetModel(PreTrainedModel):
config_class = UnivNetConfig
main_input_name = "input_features"
def __init__(self, config: UnivNetConfig):
super().__init__(config)
self.num_kernels = len(config.resblock_kernel_sizes)
self.leaky_relu_slope = config.leaky_relu_slope
sel... | class_definition | 20,391 | 27,542 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/univnet/modeling_univnet.py | null | 4,504 |
class BigBirdTokenizer(PreTrainedTokenizer):
"""
Construct a BigBird tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information reg... | class_definition | 1,006 | 14,216 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/tokenization_big_bird.py | null | 4,505 |
class BigBirdConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`BigBirdModel`]. It is used to instantiate an
BigBird model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a sim... | class_definition | 899 | 7,380 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/configuration_big_bird.py | null | 4,506 |
class BigBirdOnnxConfig(OnnxConfig):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
else:
dynamic_axis = {0: "batch", 1: "sequence"}
return OrderedDict(
... | class_definition | 7,383 | 7,831 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/configuration_big_bird.py | null | 4,507 |
class BigBirdTokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "fast" BigBird tokenizer (backed by HuggingFace's *tokenizers* library). Based on
[Unigram](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=unigram#models). This
tokenizer inherits from [`PreTrainedTokeni... | class_definition | 1,206 | 10,165 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/tokenization_big_bird_fast.py | null | 4,508 |
class FlaxBigBirdForPreTrainingOutput(ModelOutput):
"""
Output type of [`BigBirdForPreTraining`].
Args:
prediction_logits (`jnp.ndarray` of shape `(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before... | class_definition | 1,936 | 3,552 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_flax_big_bird.py | null | 4,509 |
class FlaxBigBirdForQuestionAnsweringModelOutput(ModelOutput):
"""
Base class for outputs of question answering models.
Args:
start_logits (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
Span-start scores (before SoftMax).
end_logits (`jnp.ndarray` of shape `(batch_siz... | class_definition | 3,578 | 5,180 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_flax_big_bird.py | null | 4,510 |
class FlaxBigBirdEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
config: BigBirdConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertEmbeddings.setup
def setup(se... | class_definition | 9,019 | 11,035 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_flax_big_bird.py | null | 4,511 |
class FlaxBigBirdSelfAttention(nn.Module):
config: BigBirdConfig
causal: bool = False
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.head_dim = self.config.hidden_size // self.config.num_attention_heads
if self.config.hidden_size % self.config.num_a... | class_definition | 11,137 | 19,034 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_flax_big_bird.py | null | 4,512 |
class FlaxBigBirdBlockSparseAttention(nn.Module):
config: BigBirdConfig
block_sparse_seed: int = None
dtype: jnp.dtype = jnp.float32
def setup(self):
self.query = nn.Dense(
self.config.hidden_size,
dtype=self.dtype,
use_bias=self.config.use_bias,
... | class_definition | 19,037 | 58,387 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_flax_big_bird.py | null | 4,513 |
class FlaxBigBirdSelfOutput(nn.Module):
config: BigBirdConfig
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),
dt... | class_definition | 58,486 | 59,306 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_flax_big_bird.py | null | 4,514 |
class FlaxBigBirdAttention(nn.Module):
config: BigBirdConfig
layer_id: int = None
causal: bool = False
dtype: jnp.dtype = jnp.float32
def setup(self):
if self.config.attention_type == "original_full":
self.self = FlaxBigBirdSelfAttention(self.config, causal=self.causal, dtype=se... | class_definition | 59,309 | 61,525 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_flax_big_bird.py | null | 4,515 |
class FlaxBigBirdIntermediate(nn.Module):
config: BigBirdConfig
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 | 61,626 | 62,210 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_flax_big_bird.py | null | 4,516 |
class FlaxBigBirdOutput(nn.Module):
config: BigBirdConfig
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),
dtype=... | class_definition | 62,305 | 63,129 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_flax_big_bird.py | null | 4,517 |
class FlaxBigBirdLayer(nn.Module):
config: BigBirdConfig
layer_id: int = None
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.attention = FlaxBigBirdAttention(
self.config, layer_id=self.layer_id, causal=self.config.is_decoder, dtype=self.dtype
... | class_definition | 63,132 | 65,465 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_flax_big_bird.py | null | 4,518 |
class FlaxBigBirdLayerCollection(nn.Module):
config: BigBirdConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
gradient_checkpointing: bool = False
def setup(self):
if self.gradient_checkpointing:
FlaxBigBirdCheckpointLayer = remat(FlaxBigBirdLayer, static_argnums... | class_definition | 65,468 | 68,632 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_flax_big_bird.py | null | 4,519 |
class FlaxBigBirdEncoder(nn.Module):
config: BigBirdConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
gradient_checkpointing: bool = False
def setup(self):
self.layer = FlaxBigBirdLayerCollection(
self.config,
dtype=self.dtype,
gradient_ch... | class_definition | 68,728 | 69,974 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_flax_big_bird.py | null | 4,520 |
class FlaxBigBirdPredictionHeadTransform(nn.Module):
config: BigBirdConfig
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.con... | class_definition | 70,086 | 70,633 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_flax_big_bird.py | null | 4,521 |
class FlaxBigBirdLMPredictionHead(nn.Module):
config: BigBirdConfig
dtype: jnp.dtype = jnp.float32
bias_init: Callable[..., jnp.ndarray] = jax.nn.initializers.zeros
def setup(self):
self.transform = FlaxBigBirdPredictionHeadTransform(self.config, dtype=self.dtype)
self.decoder = nn.Dens... | class_definition | 70,763 | 71,669 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_flax_big_bird.py | null | 4,522 |
class FlaxBigBirdOnlyMLMHead(nn.Module):
config: BigBirdConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.predictions = FlaxBigBirdLMPredictionHead(self.config, dtype=self.dtype)
def __call__(self, hidden_states, shared_embedding=None):
hidden_states = self.predictions(hidden... | class_definition | 71,769 | 72,161 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_flax_big_bird.py | null | 4,523 |
class FlaxBigBirdPreTrainingHeads(nn.Module):
config: BigBirdConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.predictions = FlaxBigBirdLMPredictionHead(self.config, dtype=self.dtype)
self.seq_relationship = nn.Dense(2, dtype=self.dtype)
def __call__(self, hidden_states, pool... | class_definition | 72,164 | 72,740 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_flax_big_bird.py | null | 4,524 |
class FlaxBigBirdPreTrainedModel(FlaxPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = BigBirdConfig
base_model_prefix = "bert"
module_class: nn.Module = None
def __init__(
... | class_definition | 72,743 | 81,216 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_flax_big_bird.py | null | 4,525 |
class FlaxBigBirdModule(nn.Module):
config: BigBirdConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
add_pooling_layer: bool = True
gradient_checkpointing: bool = False
def setup(self):
self.embeddings = FlaxBigBirdEmbeddings(self.config, dtype=self.dtype)
self.e... | class_definition | 81,219 | 83,590 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_flax_big_bird.py | null | 4,526 |
class FlaxBigBirdModel(FlaxBigBirdPreTrainedModel):
module_class = FlaxBigBirdModule | class_definition | 83,844 | 83,932 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_flax_big_bird.py | null | 4,527 |
class FlaxBigBirdForPreTrainingModule(nn.Module):
config: BigBirdConfig
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self):
self.bert = FlaxBigBirdModule(
config=self.config,
dtype=self.dtype,
gradient_checkpointing=self.gradi... | class_definition | 84,160 | 86,049 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_flax_big_bird.py | null | 4,528 |
class FlaxBigBirdForPreTraining(FlaxBigBirdPreTrainedModel):
module_class = FlaxBigBirdForPreTrainingModule | class_definition | 86,390 | 86,501 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_flax_big_bird.py | null | 4,529 |
class FlaxBigBirdForMaskedLMModule(nn.Module):
config: BigBirdConfig
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self):
self.bert = FlaxBigBirdModule(
config=self.config,
add_pooling_layer=False,
dtype=self.dtype,
... | class_definition | 87,495 | 89,216 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_flax_big_bird.py | null | 4,530 |
class FlaxBigBirdForMaskedLM(FlaxBigBirdPreTrainedModel):
module_class = FlaxBigBirdForMaskedLMModule | class_definition | 89,425 | 89,530 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_flax_big_bird.py | null | 4,531 |
class FlaxBigBirdClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
config: BigBirdConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.dense = nn.Dense(self.config.hidden_size, dtype=self.dtype)
classifier_dropout = (
self.config.clas... | class_definition | 89,646 | 90,579 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_flax_big_bird.py | null | 4,532 |
class FlaxBigBirdForSequenceClassificationModule(nn.Module):
config: BigBirdConfig
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self):
self.bert = FlaxBigBirdModule(
config=self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpoin... | class_definition | 90,582 | 92,035 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_flax_big_bird.py | null | 4,533 |
class FlaxBigBirdForSequenceClassification(FlaxBigBirdPreTrainedModel):
module_class = FlaxBigBirdForSequenceClassificationModule | class_definition | 92,375 | 92,508 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_flax_big_bird.py | null | 4,534 |
class FlaxBigBirdForMultipleChoiceModule(nn.Module):
config: BigBirdConfig
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self):
self.bert = FlaxBigBirdModule(
config=self.config,
dtype=self.dtype,
gradient_checkpointing=self.gr... | class_definition | 92,776 | 94,899 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_flax_big_bird.py | null | 4,535 |
class FlaxBigBirdForMultipleChoice(FlaxBigBirdPreTrainedModel):
module_class = FlaxBigBirdForMultipleChoiceModule
def __init__(
self,
config: BigBirdConfig,
input_shape: Optional[tuple] = None,
seed: int = 0,
dtype: jnp.dtype = jnp.float32,
_do_init: bool = True,... | class_definition | 95,137 | 95,783 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_flax_big_bird.py | null | 4,536 |
class FlaxBigBirdForTokenClassificationModule(nn.Module):
config: BigBirdConfig
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self):
self.bert = FlaxBigBirdModule(
config=self.config,
dtype=self.dtype,
add_pooling_layer=False,
... | class_definition | 96,188 | 97,985 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_flax_big_bird.py | null | 4,537 |
class FlaxBigBirdForTokenClassification(FlaxBigBirdPreTrainedModel):
module_class = FlaxBigBirdForTokenClassificationModule | class_definition | 98,329 | 98,456 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_flax_big_bird.py | null | 4,538 |
class FlaxBigBirdForQuestionAnsweringHead(nn.Module):
config: BigBirdConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)
self.intermediate = FlaxBigBirdIntermediate(self.config, dtype=self.dtype)
self.output = FlaxBi... | class_definition | 98,609 | 99,399 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_flax_big_bird.py | null | 4,539 |
class FlaxBigBirdForQuestionAnsweringModule(nn.Module):
config: BigBirdConfig
dtype: jnp.dtype = jnp.float32
add_pooling_layer: bool = False
gradient_checkpointing: bool = False
def setup(self):
self.config.num_labels = 2
self.bert = FlaxBigBirdModule(
self.config,
... | class_definition | 99,402 | 101,536 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_flax_big_bird.py | null | 4,540 |
class FlaxBigBirdForQuestionAnswering(FlaxBigBirdPreTrainedModel):
module_class = FlaxBigBirdForQuestionAnsweringModule
@add_start_docstrings_to_model_forward(BIG_BIRD_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
def __call__(
self,
input_ids,
attention_mask=None,
... | class_definition | 101,830 | 105,114 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_flax_big_bird.py | null | 4,541 |
class FlaxBigBirdForCausalLMModule(nn.Module):
config: BigBirdConfig
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self):
self.bert = FlaxBigBirdModule(
config=self.config,
add_pooling_layer=False,
dtype=self.dtype,
... | class_definition | 105,282 | 107,508 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_flax_big_bird.py | null | 4,542 |
class FlaxBigBirdForCausalLM(FlaxBigBirdPreTrainedModel):
module_class = FlaxBigBirdForCausalLMModule
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_v... | class_definition | 107,821 | 109,358 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_flax_big_bird.py | null | 4,543 |
class BigBirdEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
# Copied from transformers.models.bert.modeling_bert.BertEmbeddings.__init__
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size... | class_definition | 9,017 | 12,260 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_big_bird.py | null | 4,544 |
class BigBirdSelfAttention(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 num... | class_definition | 12,263 | 17,336 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_big_bird.py | null | 4,545 |
class BigBirdBlockSparseAttention(nn.Module):
def __init__(self, config, seed=None):
super().__init__()
self.max_seqlen = config.max_position_embeddings
self.seed = seed
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
f"The hid... | class_definition | 17,339 | 60,445 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_big_bird.py | null | 4,546 |
class BigBirdSelfOutput(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)
de... | class_definition | 60,535 | 61,144 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_big_bird.py | null | 4,547 |
class BigBirdAttention(nn.Module):
def __init__(self, config, seed=None):
super().__init__()
self.attention_type = config.attention_type
self.config = config
self.seed = seed
if self.config.attention_type == "original_full":
self.self = BigBirdSelfAttention(confi... | class_definition | 61,147 | 64,382 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_big_bird.py | null | 4,548 |
class BigBirdIntermediate(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.int... | class_definition | 64,474 | 65,042 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_big_bird.py | null | 4,549 |
class BigBirdOutput(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 | 65,128 | 65,739 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_big_bird.py | null | 4,550 |
class BigBirdLayer(nn.Module):
def __init__(self, config, seed=None):
super().__init__()
self.config = config
self.attention_type = config.attention_type
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = BigBirdAttenti... | class_definition | 65,742 | 70,496 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_big_bird.py | null | 4,551 |
class BigBirdEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.attention_type = config.attention_type
self.layer = nn.ModuleList(
[BigBirdLayer(config, seed=layer_idx) for layer_idx in range(config.num_hidden_layers)]
)
... | class_definition | 70,499 | 74,980 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_big_bird.py | null | 4,552 |
class BigBirdPredictionHeadTransform(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
if isinstance(config.hidden_act, str):
self.transform_act_fn = ACT2FN[config.hidden_act]
else:
self.t... | class_definition | 75,083 | 75,786 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_big_bird.py | null | 4,553 |
class BigBirdLMPredictionHead(nn.Module):
def __init__(self, config):
super().__init__()
self.transform = BigBirdPredictionHeadTransform(config)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.decoder = nn.Li... | class_definition | 75,882 | 76,720 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_big_bird.py | null | 4,554 |
class BigBirdOnlyMLMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = BigBirdLMPredictionHead(config)
def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
prediction_scores = self.predictions(sequence_output)
return prediction_scores | class_definition | 76,811 | 77,131 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_big_bird.py | null | 4,555 |
class BigBirdOnlyNSPHead(nn.Module):
def __init__(self, config):
super().__init__()
self.seq_relationship = nn.Linear(config.hidden_size, 2)
def forward(self, pooled_output):
seq_relationship_score = self.seq_relationship(pooled_output)
return seq_relationship_score | class_definition | 77,222 | 77,529 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_big_bird.py | null | 4,556 |
class BigBirdPreTrainingHeads(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = BigBirdLMPredictionHead(config)
self.seq_relationship = nn.Linear(config.hidden_size, 2)
def forward(self, sequence_output, pooled_output):
prediction_scores = self.predic... | class_definition | 77,625 | 78,094 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_big_bird.py | null | 4,557 |
class BigBirdPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = BigBirdConfig
load_tf_weights = load_tf_weights_in_big_bird
base_model_prefix = "bert"
supports_... | class_definition | 78,097 | 79,257 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_big_bird.py | null | 4,558 |
class BigBirdForPreTrainingOutput(ModelOutput):
"""
Output type of [`BigBirdForPreTraining`].
Args:
loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
Total loss as the sum of the masked language modeling loss and the next sequence prediction
... | class_definition | 82,544 | 84,500 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_big_bird.py | null | 4,559 |
class BigBirdForQuestionAnsweringModelOutput(ModelOutput):
"""
Base class for outputs of question answering models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Total span extraction loss is the sum of a Cross-Entropy for the start an... | class_definition | 84,514 | 86,408 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_big_bird.py | null | 4,560 |
class BigBirdModel(BigBirdPreTrainedModel):
"""
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.org/... | class_definition | 86,571 | 103,282 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_big_bird.py | null | 4,561 |
class BigBirdForPreTraining(BigBirdPreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder.weight", "cls.predictions.decoder.bias"]
def __init__(self, config):
super().__init__(config)
self.bert = BigBirdModel(config, add_pooling_layer=True)
self.cls = BigBirdPreTrainingHeads(... | class_definition | 103,285 | 108,027 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_big_bird.py | null | 4,562 |
class BigBirdForMaskedLM(BigBirdPreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder.weight", "cls.predictions.decoder.bias"]
def __init__(self, config):
super().__init__(config)
if config.is_decoder:
logger.warning(
"If you want to use `BigBirdForMasked... | class_definition | 108,139 | 114,146 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_big_bird.py | null | 4,563 |
class BigBirdForCausalLM(BigBirdPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["cls.predictions.decoder.weight", "cls.predictions.decoder.bias"]
def __init__(self, config):
super().__init__(config)
if not config.is_decoder:
logger.warning("If you want to use `BigBirdForCa... | class_definition | 114,284 | 120,298 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_big_bird.py | null | 4,564 |
class BigBirdClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
classifier_dropout = (
config.classifier_dropout if config.classifier... | class_definition | 120,301 | 121,124 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_big_bird.py | null | 4,565 |
class BigBirdForSequenceClassification(BigBirdPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.bert = BigBirdModel(config)
self.classifier = BigBirdClassificationHead(config)
# Initia... | class_definition | 121,353 | 126,378 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_big_bird.py | null | 4,566 |
class BigBirdForMultipleChoice(BigBirdPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.bert = BigBirdModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, 1)
# Initialize weights and ap... | class_definition | 126,616 | 130,157 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_big_bird.py | null | 4,567 |
class BigBirdForTokenClassification(BigBirdPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.bert = BigBirdModel(config)
classifier_dropout = (
config.classifier_dropout if config.classifier_dropout is not Non... | class_definition | 130,393 | 133,249 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_big_bird.py | null | 4,568 |
class BigBirdForQuestionAnsweringHead(nn.Module):
"""Head for question answering tasks."""
def __init__(self, config):
super().__init__()
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.intermediate = BigBirdIntermediate(config)
self.output = BigBirdOutput(config)
... | class_definition | 133,252 | 133,944 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_big_bird.py | null | 4,569 |
class BigBirdForQuestionAnswering(BigBirdPreTrainedModel):
def __init__(self, config, add_pooling_layer=False):
super().__init__(config)
config.num_labels = 2
self.num_labels = config.num_labels
self.sep_token_id = config.sep_token_id
self.bert = BigBirdModel(config, add_po... | class_definition | 134,238 | 141,512 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/big_bird/modeling_big_bird.py | null | 4,570 |
class _BaseAutoModelClass:
# Base class for auto models.
_model_mapping = None
def __init__(self, *args, **kwargs):
raise EnvironmentError(
f"{self.__class__.__name__} is designed to be instantiated "
f"using the `{self.__class__.__name__}.from_pretrained(pretrained_model_na... | class_definition | 25,155 | 33,769 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/auto/auto_factory.py | null | 4,571 |
class _BaseAutoBackboneClass(_BaseAutoModelClass):
# Base class for auto backbone models.
_model_mapping = None
@classmethod
def _load_timm_backbone_from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
requires_backends(cls, ["vision", "timm"])
from ...models.timm... | class_definition | 33,772 | 35,532 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/auto/auto_factory.py | null | 4,572 |
class _LazyAutoMapping(OrderedDict):
"""
" A mapping config to object (model or tokenizer for instance) that will load keys and values when it is accessed.
Args:
- config_mapping: The map model type to config class
- model_mapping: The map model type to model (or tokenizer) class
"""
... | class_definition | 40,580 | 44,541 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/auto/auto_factory.py | null | 4,573 |
class FlaxAutoModel(_BaseAutoModelClass):
_model_mapping = FLAX_MODEL_MAPPING | class_definition | 11,405 | 11,486 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/auto/modeling_flax_auto.py | null | 4,574 |
class FlaxAutoModelForPreTraining(_BaseAutoModelClass):
_model_mapping = FLAX_MODEL_FOR_PRETRAINING_MAPPING | class_definition | 11,540 | 11,651 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/auto/modeling_flax_auto.py | null | 4,575 |
class FlaxAutoModelForCausalLM(_BaseAutoModelClass):
_model_mapping = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING | class_definition | 11,757 | 11,863 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/auto/modeling_flax_auto.py | null | 4,576 |
class FlaxAutoModelForMaskedLM(_BaseAutoModelClass):
_model_mapping = FLAX_MODEL_FOR_MASKED_LM_MAPPING | class_definition | 11,976 | 12,082 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/auto/modeling_flax_auto.py | null | 4,577 |
class FlaxAutoModelForSeq2SeqLM(_BaseAutoModelClass):
_model_mapping = FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING | class_definition | 12,195 | 12,313 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/auto/modeling_flax_auto.py | null | 4,578 |
class FlaxAutoModelForSequenceClassification(_BaseAutoModelClass):
_model_mapping = FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING | class_definition | 12,501 | 12,635 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/auto/modeling_flax_auto.py | null | 4,579 |
class FlaxAutoModelForQuestionAnswering(_BaseAutoModelClass):
_model_mapping = FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING | class_definition | 12,781 | 12,905 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/auto/modeling_flax_auto.py | null | 4,580 |
class FlaxAutoModelForTokenClassification(_BaseAutoModelClass):
_model_mapping = FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING | class_definition | 13,030 | 13,158 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/auto/modeling_flax_auto.py | null | 4,581 |
class FlaxAutoModelForMultipleChoice(_BaseAutoModelClass):
_model_mapping = FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING | class_definition | 13,295 | 13,413 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/auto/modeling_flax_auto.py | null | 4,582 |
class FlaxAutoModelForNextSentencePrediction(_BaseAutoModelClass):
_model_mapping = FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING | class_definition | 13,529 | 13,664 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/auto/modeling_flax_auto.py | null | 4,583 |
class FlaxAutoModelForImageClassification(_BaseAutoModelClass):
_model_mapping = FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING | class_definition | 13,811 | 13,939 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/auto/modeling_flax_auto.py | null | 4,584 |
class FlaxAutoModelForVision2Seq(_BaseAutoModelClass):
_model_mapping = FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING | class_definition | 14,076 | 14,187 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/auto/modeling_flax_auto.py | null | 4,585 |
class FlaxAutoModelForSpeechSeq2Seq(_BaseAutoModelClass):
_model_mapping = FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING | class_definition | 14,303 | 14,421 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/auto/modeling_flax_auto.py | null | 4,586 |
class TFAutoModelForMaskGeneration(_BaseAutoModelClass):
_model_mapping = TF_MODEL_FOR_MASK_GENERATION_MAPPING | class_definition | 21,982 | 22,096 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/auto/modeling_tf_auto.py | null | 4,587 |
class TFAutoModelForTextEncoding(_BaseAutoModelClass):
_model_mapping = TF_MODEL_FOR_TEXT_ENCODING_MAPPING | class_definition | 22,099 | 22,209 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/auto/modeling_tf_auto.py | null | 4,588 |
class TFAutoModel(_BaseAutoModelClass):
_model_mapping = TF_MODEL_MAPPING | class_definition | 22,212 | 22,289 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/auto/modeling_tf_auto.py | null | 4,589 |
class TFAutoModelForAudioClassification(_BaseAutoModelClass):
_model_mapping = TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING | class_definition | 22,339 | 22,463 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/auto/modeling_tf_auto.py | null | 4,590 |
class TFAutoModelForPreTraining(_BaseAutoModelClass):
_model_mapping = TF_MODEL_FOR_PRETRAINING_MAPPING | class_definition | 22,596 | 22,703 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/auto/modeling_tf_auto.py | null | 4,591 |
class _TFAutoModelWithLMHead(_BaseAutoModelClass):
_model_mapping = TF_MODEL_WITH_LM_HEAD_MAPPING | class_definition | 22,879 | 22,980 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/auto/modeling_tf_auto.py | null | 4,592 |
class TFAutoModelForCausalLM(_BaseAutoModelClass):
_model_mapping = TF_MODEL_FOR_CAUSAL_LM_MAPPING | class_definition | 23,082 | 23,184 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/auto/modeling_tf_auto.py | null | 4,593 |
class TFAutoModelForMaskedImageModeling(_BaseAutoModelClass):
_model_mapping = TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING | class_definition | 23,293 | 23,418 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/auto/modeling_tf_auto.py | null | 4,594 |
class TFAutoModelForImageClassification(_BaseAutoModelClass):
_model_mapping = TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING | class_definition | 23,552 | 23,676 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/auto/modeling_tf_auto.py | null | 4,595 |
class TFAutoModelForZeroShotImageClassification(_BaseAutoModelClass):
_model_mapping = TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING | class_definition | 23,809 | 23,951 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/auto/modeling_tf_auto.py | null | 4,596 |
class TFAutoModelForSemanticSegmentation(_BaseAutoModelClass):
_model_mapping = TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING | class_definition | 24,110 | 24,236 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/auto/modeling_tf_auto.py | null | 4,597 |
class TFAutoModelForVision2Seq(_BaseAutoModelClass):
_model_mapping = TF_MODEL_FOR_VISION_2_SEQ_MAPPING | class_definition | 24,372 | 24,479 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/auto/modeling_tf_auto.py | null | 4,598 |
class TFAutoModelForMaskedLM(_BaseAutoModelClass):
_model_mapping = TF_MODEL_FOR_MASKED_LM_MAPPING | class_definition | 24,591 | 24,693 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/auto/modeling_tf_auto.py | null | 4,599 |
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