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 FlaxPegasusForConditionalGeneration(FlaxPegasusPreTrainedModel):
module_class = FlaxPegasusForConditionalGenerationModule
dtype: jnp.dtype = jnp.float32
@add_start_docstrings(PEGASUS_DECODE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=FlaxCausalLMOutputWithCrossAttentions, config_clas... | class_definition | 56,481 | 64,210 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pegasus/modeling_flax_pegasus.py | null | 6,500 |
class PegasusTokenizerFast(PreTrainedTokenizerFast):
r"""
Construct a "fast" PEGASUS 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 [`PreTrainedToke... | class_definition | 1,142 | 9,939 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pegasus/tokenization_pegasus_fast.py | null | 6,501 |
class PegasusConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`PegasusModel`]. It is used to instantiate an
PEGASUS model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a sim... | class_definition | 796 | 7,470 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pegasus/configuration_pegasus.py | null | 6,502 |
class PegasusTokenizer(PreTrainedTokenizer):
r"""
Construct a PEGASUS 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 re... | class_definition | 1,018 | 13,124 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pegasus/tokenization_pegasus.py | null | 6,503 |
class TFPegasusSinusoidalPositionalEmbedding(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 NotImplemen... | class_definition | 4,200 | 6,463 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pegasus/modeling_tf_pegasus.py | null | 6,504 |
class TFPegasusAttention(keras.layers.Layer):
"""Multi-headed attention from "Attention Is All You Need"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = True,
**kwargs,
):
supe... | class_definition | 6,557 | 14,134 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pegasus/modeling_tf_pegasus.py | null | 6,505 |
class TFPegasusEncoderLayer(keras.layers.Layer):
def __init__(self, config: PegasusConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.d_model
self.self_attn = TFPegasusAttention(
self.embed_dim, config.encoder_attention_heads, dropout=config.attention_dropout, ... | class_definition | 14,235 | 17,911 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pegasus/modeling_tf_pegasus.py | null | 6,506 |
class TFPegasusDecoderLayer(keras.layers.Layer):
def __init__(self, config: PegasusConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.d_model
self.self_attn = TFPegasusAttention(
embed_dim=self.embed_dim,
num_heads=config.decoder_attention_heads,
... | class_definition | 18,012 | 24,808 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pegasus/modeling_tf_pegasus.py | null | 6,507 |
class TFPegasusPreTrainedModel(TFPreTrainedModel):
config_class = PegasusConfig
base_model_prefix = "model" | class_definition | 24,811 | 24,926 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pegasus/modeling_tf_pegasus.py | null | 6,508 |
class TFPegasusEncoder(keras.layers.Layer):
config_class = PegasusConfig
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`TFPegasusEncoderLayer`].
Args:
config: PegasusConfig
"""
def __init__(self, config: PegasusConfig, embed_t... | class_definition | 34,064 | 42,021 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pegasus/modeling_tf_pegasus.py | null | 6,509 |
class TFPegasusDecoder(keras.layers.Layer):
config_class = PegasusConfig
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`TFPegasusDecoderLayer`]
Args:
config: PegasusConfig
embed_tokens: output embedding
"""
def __init__(self, config: Peg... | class_definition | 42,044 | 54,345 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pegasus/modeling_tf_pegasus.py | null | 6,510 |
class TFPegasusMainLayer(keras.layers.Layer):
config_class = PegasusConfig
def __init__(self, config: PegasusConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.shared = keras.layers.Embedding(
input_dim=config.vocab_size,
output_dim=config.d... | class_definition | 54,368 | 59,849 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pegasus/modeling_tf_pegasus.py | null | 6,511 |
class TFPegasusModel(TFPegasusPreTrainedModel):
def __init__(self, config: PegasusConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.model = TFPegasusMainLayer(config, name="model")
def get_encoder(self):
return self.model.encoder
def get_decoder(self):
... | class_definition | 59,999 | 64,030 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pegasus/modeling_tf_pegasus.py | null | 6,512 |
class BiasLayer(keras.layers.Layer):
"""
Bias as a layer. It is used for serialization purposes: `keras.Model.save_weights` stores on a per-layer basis,
so all weights have to be registered in a layer.
"""
def __init__(self, shape, initializer, trainable, name, **kwargs):
super().__init__(n... | class_definition | 64,099 | 64,905 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pegasus/modeling_tf_pegasus.py | null | 6,513 |
class TFPegasusForConditionalGeneration(TFPegasusPreTrainedModel, TFCausalLanguageModelingLoss):
_keys_to_ignore_on_load_unexpected = [
r"model.encoder.embed_tokens.weight",
r"model.decoder.embed_tokens.weight",
]
def __init__(self, config, *inputs, **kwargs):
super().__init__(confi... | class_definition | 65,049 | 74,199 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pegasus/modeling_tf_pegasus.py | null | 6,514 |
class PegasusSinusoidalPositionalEmbedding(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 | 2,401 | 3,969 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pegasus/modeling_pegasus.py | null | 6,515 |
class PegasusAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = True,
is_causal: bool = False,
... | class_definition | 4,058 | 11,454 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pegasus/modeling_pegasus.py | null | 6,516 |
class PegasusEncoderLayer(nn.Module):
def __init__(self, config: PegasusConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = PEGASUS_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=self.embed_dim,
num_heads=config.encoder_attention_h... | class_definition | 11,624 | 14,765 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pegasus/modeling_pegasus.py | null | 6,517 |
class PegasusDecoderLayer(nn.Module):
def __init__(self, config: PegasusConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = PEGASUS_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=self.embed_dim,
num_heads=config.decoder_attention_h... | class_definition | 14,877 | 20,761 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pegasus/modeling_pegasus.py | null | 6,518 |
class PegasusPreTrainedModel(PreTrainedModel):
config_class = PegasusConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
def _init_weights(self, module):
std = self.config.init_std
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, s... | class_definition | 20,764 | 21,475 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pegasus/modeling_pegasus.py | null | 6,519 |
class PegasusEncoder(PegasusPreTrainedModel):
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`PegasusEncoderLayer`].
Args:
config: PegasusConfig
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, config: P... | class_definition | 29,477 | 38,586 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pegasus/modeling_pegasus.py | null | 6,520 |
class PegasusDecoder(PegasusPreTrainedModel):
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`PegasusDecoderLayer`]
Args:
config: PegasusConfig
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, config: PegasusConfig, embed_... | class_definition | 38,589 | 52,366 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pegasus/modeling_pegasus.py | null | 6,521 |
class PegasusModel(PegasusPreTrainedModel):
_tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
def __init__(self, config: PegasusConfig):
super().__init__(config)
padding_idx, vocab_size = config.pad_token_id, config.vocab_size
self.shared = nn.Embeddin... | class_definition | 52,516 | 59,170 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pegasus/modeling_pegasus.py | null | 6,522 |
class PegasusForConditionalGeneration(PegasusPreTrainedModel, GenerationMixin):
base_model_prefix = "model"
_keys_to_ignore_on_load_missing = ["final_logits_bias"]
_tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"]
def __init__(self, config: PegasusCon... | class_definition | 59,309 | 66,899 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pegasus/modeling_pegasus.py | null | 6,523 |
class PegasusDecoderWrapper(PegasusPreTrainedModel):
"""
This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is
used in combination with the [`EncoderDecoderModel`] framework.
"""
def __init__(self, config):
super().__init__(config)
... | class_definition | 66,993 | 67,443 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pegasus/modeling_pegasus.py | null | 6,524 |
class PegasusForCausalLM(PegasusPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
config = copy.deepcopy(config)
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = PegasusD... | class_definition | 67,446 | 78,105 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pegasus/modeling_pegasus.py | null | 6,525 |
class BartLearnedPositionalEmbedding(nn.Embedding):
"""
This module learns positional embeddings up to a fixed maximum size.
"""
def __init__(self, num_embeddings: int, embedding_dim: int):
# Bart is set up so that if padding_idx is specified then offset the embedding ids by 2
# and adj... | class_definition | 3,026 | 3,930 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/modeling_bart.py | null | 6,526 |
class BartScaledWordEmbedding(nn.Embedding):
"""
This module overrides nn.Embeddings' forward by multiplying with embeddings scale.
"""
def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, embed_scale: Optional[float] = 1.0):
super().__init__(num_embeddings, embedding_d... | class_definition | 3,933 | 4,418 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/modeling_bart.py | null | 6,527 |
class BartAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = True,
is_causal: bool = False,
con... | class_definition | 4,421 | 11,811 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/modeling_bart.py | null | 6,528 |
class BartFlashAttention2(BartAttention):
"""
Bart flash attention module. This module inherits from `BartAttention` as the weights of the module stays
untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
flash attention and deal with paddin... | class_definition | 11,814 | 18,250 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/modeling_bart.py | null | 6,529 |
class BartSdpaAttention(BartAttention):
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torc... | class_definition | 18,253 | 24,030 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/modeling_bart.py | null | 6,530 |
class BartEncoderLayer(nn.Module):
def __init__(self, config: BartConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = BART_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=self.embed_dim,
num_heads=config.encoder_attention_heads,
... | class_definition | 24,169 | 27,367 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/modeling_bart.py | null | 6,531 |
class BartDecoderLayer(nn.Module):
def __init__(self, config: BartConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = BART_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=self.embed_dim,
num_heads=config.decoder_attention_heads,
... | class_definition | 27,370 | 33,309 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/modeling_bart.py | null | 6,532 |
class BartClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(
self,
input_dim: int,
inner_dim: int,
num_classes: int,
pooler_dropout: float,
):
super().__init__()
self.dense = nn.Linear(input_dim, inner_dim... | class_definition | 33,312 | 34,098 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/modeling_bart.py | null | 6,533 |
class BartPreTrainedModel(PreTrainedModel):
config_class = BartConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_keys_to_ignore_on_load_unexpected = ["encoder.version", "decoder.version"]
_no_split_modules = [r"BartEncoderLayer", r"BartDecoderLayer"]
_skip_keys_device_pl... | class_definition | 34,101 | 35,318 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/modeling_bart.py | null | 6,534 |
class PretrainedBartModel(BartPreTrainedModel):
def __init_subclass__(self):
warnings.warn(
"The class `PretrainedBartModel` has been depreciated, please use `BartPreTrainedModel` instead.",
FutureWarning,
) | class_definition | 35,321 | 35,572 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/modeling_bart.py | null | 6,535 |
class BartPretrainedModel(BartPreTrainedModel):
def __init_subclass__(self):
warnings.warn(
"The class `PretrainedBartModel` has been depreciated, please use `BartPreTrainedModel` instead.",
FutureWarning,
) | class_definition | 35,575 | 35,826 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/modeling_bart.py | null | 6,536 |
class BartEncoder(BartPreTrainedModel):
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`BartEncoderLayer`].
Args:
config: BartConfig
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, config: BartConfig, e... | class_definition | 45,135 | 53,882 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/modeling_bart.py | null | 6,537 |
class BartDecoder(BartPreTrainedModel):
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`BartDecoderLayer`]
Args:
config: BartConfig
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, config: BartConfig, embed_tokens: Optiona... | class_definition | 53,885 | 68,464 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/modeling_bart.py | null | 6,538 |
class BartModel(BartPreTrainedModel):
_tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
def __init__(self, config: BartConfig):
super().__init__(config)
padding_idx, vocab_size = config.pad_token_id, config.vocab_size
embed_scale = math.sqrt(config.d_m... | class_definition | 68,608 | 74,419 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/modeling_bart.py | null | 6,539 |
class BartForConditionalGeneration(BartPreTrainedModel, GenerationMixin):
base_model_prefix = "model"
_tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"]
_keys_to_ignore_on_load_missing = ["final_logits_bias"]
def __init__(self, config: BartConfig):
... | class_definition | 74,552 | 80,933 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/modeling_bart.py | null | 6,540 |
class BartForSequenceClassification(BartPreTrainedModel):
_tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
def __init__(self, config: BartConfig, **kwargs):
super().__init__(config, **kwargs)
self.model = BartModel(config)
self.classification_head = Ba... | class_definition | 81,132 | 86,728 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/modeling_bart.py | null | 6,541 |
class BartForQuestionAnswering(BartPreTrainedModel):
_tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
def __init__(self, config):
super().__init__(config)
config.num_labels = 2
self.num_labels = config.num_labels
self.model = BartModel(config... | class_definition | 87,014 | 92,435 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/modeling_bart.py | null | 6,542 |
class BartDecoderWrapper(BartPreTrainedModel):
"""
This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is
used in combination with the [`EncoderDecoderModel`] framework.
"""
def __init__(self, config):
super().__init__(config)
... | class_definition | 92,438 | 92,879 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/modeling_bart.py | null | 6,543 |
class BartForCausalLM(BartPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
config = copy.deepcopy(config)
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = BartDecoderWra... | class_definition | 93,062 | 102,393 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/modeling_bart.py | null | 6,544 |
class BartTokenizer(PreTrainedTokenizer):
"""
Constructs a BART tokenizer, which is smilar to the ROBERTa tokenizer, using 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 encoded differently whether it ... | class_definition | 2,332 | 16,249 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/tokenization_bart.py | null | 6,545 |
class BartTokenizerFast(PreTrainedTokenizerFast):
r"""
Construct a "fast" BART tokenizer (backed by HuggingFace's *tokenizers* library), derived from the GPT-2 tokenizer,
using byte-level Byte-Pair-Encoding.
This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepi... | class_definition | 1,165 | 11,253 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/tokenization_bart_fast.py | null | 6,546 |
class BartConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`BartModel`]. It is used to instantiate a BART
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar confi... | class_definition | 1,105 | 8,471 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/configuration_bart.py | null | 6,547 |
class BartOnnxConfig(OnnxSeq2SeqConfigWithPast):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
if self.task in ["default", "seq2seq-lm"]:
common_inputs = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
... | class_definition | 8,474 | 18,782 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/configuration_bart.py | null | 6,548 |
class TFBartLearnedPositionalEmbedding(keras.layers.Embedding):
"""
This module learns positional embeddings up to a fixed maximum size.
"""
def __init__(self, num_embeddings: int, embedding_dim: int, **kwargs):
# Bart is set up so that if padding_idx is specified then offset the embedding ids ... | class_definition | 3,930 | 5,057 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/modeling_tf_bart.py | null | 6,549 |
class TFBartAttention(keras.layers.Layer):
"""Multi-headed attention from "Attention Is All You Need"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = True,
**kwargs,
):
super()... | class_definition | 5,060 | 12,634 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/modeling_tf_bart.py | null | 6,550 |
class TFBartEncoderLayer(keras.layers.Layer):
def __init__(self, config: BartConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.d_model
self.self_attn = TFBartAttention(
self.embed_dim, config.encoder_attention_heads, dropout=config.attention_dropout, name="sel... | class_definition | 12,637 | 16,344 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/modeling_tf_bart.py | null | 6,551 |
class TFBartDecoderLayer(keras.layers.Layer):
def __init__(self, config: BartConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.d_model
self.self_attn = TFBartAttention(
embed_dim=self.embed_dim,
num_heads=config.decoder_attention_heads,
... | class_definition | 16,347 | 23,199 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/modeling_tf_bart.py | null | 6,552 |
class TFBartClassificationHead(keras.layers.Layer):
"""Head for sentence-level classification tasks."""
def __init__(self, inner_dim: int, num_classes: int, pooler_dropout: float, name: str, **kwargs):
super().__init__(name=name, **kwargs)
self.dense = keras.layers.Dense(inner_dim, name="dense"... | class_definition | 23,202 | 24,493 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/modeling_tf_bart.py | null | 6,553 |
class TFBartPretrainedModel(TFPreTrainedModel):
config_class = BartConfig
base_model_prefix = "model"
@property
def dummy_inputs(self):
dummy_inputs = super().dummy_inputs
# Dummy inputs should not contain the default val of 1
# as this is the padding token and some assertions c... | class_definition | 24,496 | 25,260 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/modeling_tf_bart.py | null | 6,554 |
class TFBartEncoder(keras.layers.Layer):
config_class = BartConfig
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`TFBartEncoderLayer`].
Args:
config: BartConfig
"""
def __init__(self, config: BartConfig, embed_tokens: Optional... | class_definition | 34,653 | 42,019 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/modeling_tf_bart.py | null | 6,555 |
class TFBartDecoder(keras.layers.Layer):
config_class = BartConfig
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`TFBartDecoderLayer`]
Args:
config: BartConfig
embed_tokens: output embedding
"""
def __init__(self, config: BartConfig, emb... | class_definition | 42,042 | 53,827 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/modeling_tf_bart.py | null | 6,556 |
class TFBartMainLayer(keras.layers.Layer):
config_class = BartConfig
def __init__(self, config: BartConfig, load_weight_prefix=None, **kwargs):
super().__init__(**kwargs)
self.config = config
self.shared = keras.layers.Embedding(
input_dim=config.vocab_size,
outp... | class_definition | 53,850 | 60,045 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/modeling_tf_bart.py | null | 6,557 |
class TFBartModel(TFBartPretrainedModel):
_requires_load_weight_prefix = True
def __init__(self, config: BartConfig, load_weight_prefix=None, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.model = TFBartMainLayer(config, load_weight_prefix=load_weight_prefix, name="model"... | class_definition | 60,189 | 64,230 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/modeling_tf_bart.py | null | 6,558 |
class BiasLayer(keras.layers.Layer):
"""
Bias as a layer. It is used for serialization purposes: `keras.Model.save_weights` stores on a per-layer basis,
so all weights have to be registered in a layer.
"""
def __init__(self, shape, initializer, trainable, name, **kwargs):
super().__init__(n... | class_definition | 64,233 | 65,039 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/modeling_tf_bart.py | null | 6,559 |
class TFBartForConditionalGeneration(TFBartPretrainedModel, TFCausalLanguageModelingLoss):
_keys_to_ignore_on_load_missing = [r"final_logits_bias"]
_requires_load_weight_prefix = True
def __init__(self, config, load_weight_prefix=None, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)... | class_definition | 65,177 | 74,107 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/modeling_tf_bart.py | null | 6,560 |
class TFBartForSequenceClassification(TFBartPretrainedModel, TFSequenceClassificationLoss):
def __init__(self, config: BartConfig, load_weight_prefix=None, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.model = TFBartMainLayer(config, load_weight_prefix=load_weight_prefix, name... | class_definition | 74,306 | 80,770 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/modeling_tf_bart.py | null | 6,561 |
class FlaxBartAttention(nn.Module):
config: BartConfig
embed_dim: int
num_heads: int
dropout: float = 0.0
causal: bool = False
bias: bool = True
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self) -> None:
self.head_dim = self.embed_dim // self.num_hea... | class_definition | 12,337 | 19,730 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/modeling_flax_bart.py | null | 6,562 |
class FlaxBartEncoderLayer(nn.Module):
config: BartConfig
dtype: jnp.dtype = jnp.float32
def setup(self) -> None:
self.embed_dim = self.config.d_model
self.self_attn = FlaxBartAttention(
config=self.config,
embed_dim=self.embed_dim,
num_heads=self.config.... | class_definition | 19,733 | 22,015 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/modeling_flax_bart.py | null | 6,563 |
class FlaxBartEncoderLayerCollection(nn.Module):
config: BartConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.layers = [
FlaxBartEncoderLayer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.encoder_layers)
]
... | class_definition | 22,018 | 23,957 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/modeling_flax_bart.py | null | 6,564 |
class FlaxBartDecoderLayer(nn.Module):
config: BartConfig
dtype: jnp.dtype = jnp.float32
def setup(self) -> None:
self.embed_dim = self.config.d_model
self.self_attn = FlaxBartAttention(
config=self.config,
embed_dim=self.embed_dim,
num_heads=self.config.... | class_definition | 23,960 | 27,506 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/modeling_flax_bart.py | null | 6,565 |
class FlaxBartDecoderLayerCollection(nn.Module):
config: BartConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.layers = [
FlaxBartDecoderLayer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.decoder_layers)
]
... | class_definition | 27,509 | 30,214 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/modeling_flax_bart.py | null | 6,566 |
class FlaxBartClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
config: BartConfig
inner_dim: int
num_classes: int
pooler_dropout: float
dtype: jnp.dtype = jnp.float32
def setup(self):
self.dense = nn.Dense(
self.inner_dim, dtype=self.dt... | class_definition | 30,217 | 31,266 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/modeling_flax_bart.py | null | 6,567 |
class FlaxBartEncoder(nn.Module):
config: BartConfig
embed_tokens: nn.Embed
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.dropout_layer = nn.Dropout(rate=self.config.dropout)
embed_dim = self.config.d_model
self.padding_idx = self.config.p... | class_definition | 31,269 | 33,602 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/modeling_flax_bart.py | null | 6,568 |
class FlaxBartDecoder(nn.Module):
config: BartConfig
embed_tokens: nn.Embed
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.dropout_layer = nn.Dropout(rate=self.config.dropout)
embed_dim = self.config.d_model
self.padding_idx = self.config.p... | class_definition | 33,605 | 36,320 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/modeling_flax_bart.py | null | 6,569 |
class FlaxBartModule(nn.Module):
config: BartConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.shared = nn.Embed(
self.config.vocab_size,
self.config.d_model,
embedding_init=jax.nn.initializers.normal(self.config.init_std... | class_definition | 36,323 | 38,760 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/modeling_flax_bart.py | null | 6,570 |
class FlaxBartPreTrainedModel(FlaxPreTrainedModel):
config_class = BartConfig
base_model_prefix: str = "model"
module_class: nn.Module = None
def __init__(
self,
config: BartConfig,
input_shape: Tuple[int] = (1, 1),
seed: int = 0,
dtype: jnp.dtype = jnp.float32,
... | class_definition | 38,763 | 53,253 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/modeling_flax_bart.py | null | 6,571 |
class FlaxBartModel(FlaxBartPreTrainedModel):
config: BartConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
module_class = FlaxBartModule | class_definition | 53,409 | 53,578 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/modeling_flax_bart.py | null | 6,572 |
class FlaxBartForConditionalGenerationModule(nn.Module):
config: BartConfig
dtype: jnp.dtype = jnp.float32
bias_init: Callable[..., jnp.ndarray] = jax.nn.initializers.zeros
def setup(self):
self.model = FlaxBartModule(config=self.config, dtype=self.dtype)
self.lm_head = nn.Dense(
... | class_definition | 53,689 | 56,276 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/modeling_flax_bart.py | null | 6,573 |
class FlaxBartForConditionalGeneration(FlaxBartPreTrainedModel):
module_class = FlaxBartForConditionalGenerationModule
dtype: jnp.dtype = jnp.float32
@add_start_docstrings(BART_DECODE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=FlaxCausalLMOutputWithCrossAttentions, config_class=BartConfig... | class_definition | 56,409 | 64,110 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/modeling_flax_bart.py | null | 6,574 |
class FlaxBartForSequenceClassificationModule(nn.Module):
config: BartConfig
dtype: jnp.dtype = jnp.float32
num_labels: Optional[int] = None
def setup(self):
self.model = FlaxBartModule(config=self.config, dtype=self.dtype)
self.classification_head = FlaxBartClassificationHead(
... | class_definition | 65,880 | 69,042 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/modeling_flax_bart.py | null | 6,575 |
class FlaxBartForSequenceClassification(FlaxBartPreTrainedModel):
module_class = FlaxBartForSequenceClassificationModule
dtype = jnp.float32 | class_definition | 69,241 | 69,389 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/modeling_flax_bart.py | null | 6,576 |
class FlaxBartForQuestionAnsweringModule(nn.Module):
config: BartConfig
dtype: jnp.dtype = jnp.float32
num_labels = 2
def setup(self):
self.model = FlaxBartModule(config=self.config, dtype=self.dtype)
self.qa_outputs = nn.Dense(
self.num_labels, dtype=self.dtype, kernel_init... | class_definition | 69,552 | 71,815 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/modeling_flax_bart.py | null | 6,577 |
class FlaxBartForQuestionAnswering(FlaxBartPreTrainedModel):
module_class = FlaxBartForQuestionAnsweringModule
dtype = jnp.float32 | class_definition | 72,101 | 72,239 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/modeling_flax_bart.py | null | 6,578 |
class FlaxBartDecoderPreTrainedModel(FlaxPreTrainedModel):
config_class = BartConfig
base_model_prefix: str = "model"
module_class: nn.Module = None
def __init__(
self,
config: BartConfig,
input_shape: Tuple[int] = (1, 1),
seed: int = 0,
dtype: jnp.dtype = jnp.fl... | class_definition | 72,401 | 78,135 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/modeling_flax_bart.py | null | 6,579 |
class FlaxBartDecoderWrapper(nn.Module):
"""
This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is
used in combination with the [`EncoderDecoderModel`] framework.
"""
config: BartConfig
dtype: jnp.dtype = jnp.float32
def setup(self)... | class_definition | 78,138 | 78,899 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/modeling_flax_bart.py | null | 6,580 |
class FlaxBartForCausalLMModule(nn.Module):
config: BartConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.model = FlaxBartDecoderWrapper(config=self.config, dtype=self.dtype)
self.lm_head = nn.Dense(
self.config.vocab_size,
use_bias=False,
dtype... | class_definition | 78,902 | 80,808 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/modeling_flax_bart.py | null | 6,581 |
class FlaxBartForCausalLM(FlaxBartDecoderPreTrainedModel):
module_class = FlaxBartForCausalLMModule
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_val... | class_definition | 81,030 | 82,565 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bart/modeling_flax_bart.py | null | 6,582 |
class YosoCumulation(torch.autograd.Function):
@staticmethod
def forward(ctx, query_mask, key_mask, query, key, value, config):
hash_code_len = config["hash_code_len"]
expectation = (1 - torch.acos(torch.matmul(query, key.transpose(-1, -2))) / math.pi) ** hash_code_len
expectation = exp... | class_definition | 3,723 | 4,935 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/yoso/modeling_yoso.py | null | 6,583 |
class YosoLSHCumulation(torch.autograd.Function):
@staticmethod
def forward(ctx, query_mask, key_mask, query, key, value, config):
if query_mask.size(0) != key_mask.size(0):
raise ValueError("Query mask and Key mask differ in sizes in dimension 0")
if query_mask.size(0) != query.size... | class_definition | 4,938 | 8,690 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/yoso/modeling_yoso.py | null | 6,584 |
class YosoEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.position_embeddi... | class_definition | 8,788 | 11,754 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/yoso/modeling_yoso.py | null | 6,585 |
class YosoSelfAttention(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_size}) i... | class_definition | 11,757 | 17,567 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/yoso/modeling_yoso.py | null | 6,586 |
class YosoSelfOutput(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)
def f... | class_definition | 17,638 | 18,244 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/yoso/modeling_yoso.py | null | 6,587 |
class YosoAttention(nn.Module):
def __init__(self, config, position_embedding_type=None):
super().__init__()
self.self = YosoSelfAttention(config, position_embedding_type=position_embedding_type)
self.output = YosoSelfOutput(config)
self.pruned_heads = set()
def prune_heads(self... | class_definition | 18,247 | 19,750 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/yoso/modeling_yoso.py | null | 6,588 |
class YosoIntermediate(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.interm... | class_definition | 19,823 | 20,388 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/yoso/modeling_yoso.py | null | 6,589 |
class YosoOutput(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)
def... | class_definition | 20,455 | 21,063 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/yoso/modeling_yoso.py | null | 6,590 |
class YosoLayer(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 = YosoAttention(config)
self.add_cross_attention = config.add_cross_attention
self.intermediate... | class_definition | 21,066 | 22,260 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/yoso/modeling_yoso.py | null | 6,591 |
class YosoEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([YosoLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
hidden_states,
... | class_definition | 22,263 | 24,036 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/yoso/modeling_yoso.py | null | 6,592 |
class YosoPredictionHeadTransform(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.tran... | class_definition | 24,120 | 24,820 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/yoso/modeling_yoso.py | null | 6,593 |
class YosoLMPredictionHead(nn.Module):
def __init__(self, config):
super().__init__()
self.transform = YosoPredictionHeadTransform(config)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.decoder = nn.Linear(c... | class_definition | 24,913 | 25,745 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/yoso/modeling_yoso.py | null | 6,594 |
class YosoOnlyMLMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = YosoLMPredictionHead(config)
def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
prediction_scores = self.predictions(sequence_output)
return prediction_scores | class_definition | 25,833 | 26,147 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/yoso/modeling_yoso.py | null | 6,595 |
class YosoPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = YosoConfig
base_model_prefix = "yoso"
supports_gradient_checkpointing = True
def _init_weights(sel... | class_definition | 26,150 | 27,254 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/yoso/modeling_yoso.py | null | 6,596 |
class YosoModel(YosoPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.embeddings = YosoEmbeddings(config)
self.encoder = YosoEncoder(config)
# Initialize weights and apply final processing
self.post_init()
def get_... | class_definition | 30,672 | 35,262 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/yoso/modeling_yoso.py | null | 6,597 |
class YosoForMaskedLM(YosoPreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder.weight", "cls.predictions.decoder.bias"]
def __init__(self, config):
super().__init__(config)
self.yoso = YosoModel(config)
self.cls = YosoOnlyMLMHead(config)
# Initialize weights and ap... | class_definition | 35,367 | 38,462 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/yoso/modeling_yoso.py | null | 6,598 |
class YosoClassificationHead(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.Lin... | class_definition | 38,465 | 39,141 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/yoso/modeling_yoso.py | null | 6,599 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.