text stringlengths 31 243k | type stringclasses 1
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|---|---|---|---|---|---|---|---|
class RoCBertOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
... | class_definition | 23,377 | 23,988 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py | null | 9,300 |
class RoCBertLayer(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 = RoCBertAttention(config)
self.is_decoder = config.is_decoder
self.add_cross_attention = co... | class_definition | 24,073 | 27,995 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py | null | 9,301 |
class RoCBertEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([RoCBertLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
hidden_states: ... | class_definition | 28,082 | 31,878 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py | null | 9,302 |
class RoCBertPooler(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
# We "pool" the model by simply taking the hi... | class_definition | 31,964 | 32,526 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py | null | 9,303 |
class RoCBertPredictionHeadTransform(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 | 32,629 | 33,332 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py | null | 9,304 |
class RoCBertLMPredictionHead(nn.Module):
def __init__(self, config):
super().__init__()
self.transform = RoCBertPredictionHeadTransform(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 | 33,428 | 34,266 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py | null | 9,305 |
class RoCBertOnlyMLMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = RoCBertLMPredictionHead(config)
def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
prediction_scores = self.predictions(sequence_output)
return prediction_scores | class_definition | 34,357 | 34,677 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py | null | 9,306 |
class RoCBertPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = RoCBertConfig
load_tf_weights = load_tf_weights_in_roc_bert
base_model_prefix = "roc_bert"
suppo... | class_definition | 34,680 | 35,844 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py | null | 9,307 |
class RoCBertModel(RoCBertPreTrainedModel):
"""
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 | 40,016 | 50,167 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py | null | 9,308 |
class RoCBertForPreTraining(RoCBertPreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder.weight", "cls.predictions.decoder.bias"]
def __init__(self, config):
super().__init__(config)
self.roc_bert = RoCBertModel(config)
self.cls = RoCBertOnlyMLMHead(config)
# Initia... | class_definition | 50,325 | 59,969 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py | null | 9,309 |
class RoCBertForMaskedLM(RoCBertPreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder.weight", "cls.predictions.decoder.bias"]
# Copied from transformers.models.bert.modeling_bert.BertForMaskedLM.__init__ with Bert->RoCBert,bert->roc_bert
def __init__(self, config):
super().__init__(conf... | class_definition | 60,081 | 66,055 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py | null | 9,310 |
class RoCBertForCausalLM(RoCBertPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["cls.predictions.decoder.weight", "cls.predictions.decoder.bias"]
# Copied from transformers.models.bert.modeling_bert.BertLMHeadModel.__init__ with BertLMHeadModel->RoCBertForCausalLM,Bert->RoCBert,bert->roc_bert
def ... | class_definition | 66,193 | 75,457 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py | null | 9,311 |
class RoCBertForSequenceClassification(RoCBertPreTrainedModel):
# Copied from transformers.models.bert.modeling_bert.BertForSequenceClassification.__init__ with Bert->RoCBert,bert->roc_bert
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.conf... | class_definition | 75,676 | 80,165 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py | null | 9,312 |
class RoCBertForMultipleChoice(RoCBertPreTrainedModel):
# Copied from transformers.models.bert.modeling_bert.BertForMultipleChoice.__init__ with Bert->RoCBert,bert->roc_bert
def __init__(self, config):
super().__init__(config)
self.roc_bert = RoCBertModel(config)
classifier_dropout = (
... | class_definition | 80,393 | 84,724 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py | null | 9,313 |
class RoCBertForTokenClassification(RoCBertPreTrainedModel):
# Copied from transformers.models.bert.modeling_bert.BertForTokenClassification.__init__ with Bert->RoCBert,bert->roc_bert
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.roc_bert ... | class_definition | 84,950 | 88,273 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py | null | 9,314 |
class RoCBertForQuestionAnswering(RoCBertPreTrainedModel):
# Copied from transformers.models.bert.modeling_bert.BertForQuestionAnswering.__init__ with Bert->RoCBert,bert->roc_bert
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.roc_bert = Ro... | class_definition | 88,557 | 93,294 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/modeling_roc_bert.py | null | 9,315 |
class RoCBertTokenizer(PreTrainedTokenizer):
r"""
Args:
Construct a RoCBert tokenizer. Based on WordPiece. This tokenizer inherits from [`PreTrainedTokenizer`] which
contains most of the main methods. Users should refer to this superclass for more information regarding those
methods.
vocab_f... | class_definition | 2,177 | 41,798 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py | null | 9,316 |
class RoCBertBasicTokenizer:
"""
Constructs a RoCBertBasicTokenizer 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 (`It... | class_definition | 41,917 | 48,679 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py | null | 9,317 |
class RoCBertWordpieceTokenizer:
"""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):
"""
... | class_definition | 48,810 | 50,705 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/tokenization_roc_bert.py | null | 9,318 |
class RoCBertConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`RoCBertModel`]. It is used to instantiate a
RoCBert model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a simi... | class_definition | 797 | 8,497 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roc_bert/configuration_roc_bert.py | null | 9,319 |
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 | 1,323 | 8,071 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/tokenization_prophetnet.py | null | 9,320 |
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 | 8,150 | 10,038 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/tokenization_prophetnet.py | null | 9,321 |
class ProphetNetTokenizer(PreTrainedTokenizer):
r"""
Construct a ProphetNetTokenizer. Based on WordPiece.
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
Args:
... | class_definition | 10,382 | 21,199 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/tokenization_prophetnet.py | null | 9,322 |
class ProphetNetConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`ProphetNetModel`]. It is used to instantiate a
ProphetNet model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yie... | class_definition | 838 | 8,869 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/configuration_prophetnet.py | null | 9,323 |
class ProphetNetSeq2SeqLMOutput(ModelOutput):
"""
Base class for sequence-to-sequence language models outputs.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss.
logits (`torch.FloatTensor` of shape `(batch_s... | class_definition | 12,152 | 18,104 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py | null | 9,324 |
class ProphetNetSeq2SeqModelOutput(ModelOutput):
"""
Base class for model encoder's outputs that also contains : pre-computed hidden states that can speed up sequential
decoding.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, decoder_sequence_length, hidden_size)`):
... | class_definition | 18,118 | 24,095 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py | null | 9,325 |
class ProphetNetDecoderModelOutput(ModelOutput):
"""
Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding).
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, decoder_sequence_length, hidden_size)`):
Sequence of main st... | class_definition | 24,109 | 28,325 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py | null | 9,326 |
class ProphetNetDecoderLMOutput(ModelOutput):
"""
Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding).
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss.
l... | class_definition | 28,339 | 32,601 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py | null | 9,327 |
class ProphetNetPreTrainedModel(PreTrainedModel):
config_class = ProphetNetConfig
base_model_prefix = "prophetnet"
supports_gradient_checkpointing = True
def _init_weights(self, module):
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=self.config.init_std)... | class_definition | 32,604 | 34,258 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py | null | 9,328 |
class ProphetNetPositionalEmbeddings(nn.Embedding):
"""
This module learns positional embeddings up to a fixed maximum size. Padding ids are ignored by either offsetting
based on padding_idx or by setting padding_idx to None and ensuring that the appropriate position ids are passed to
the forward functi... | class_definition | 34,261 | 36,329 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py | null | 9,329 |
class ProphetNetAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
config: ProphetNetConfig,
num_attn_heads: int,
):
super().__init__()
hidden_size = config.hidden_size
self.attention_dropout = confi... | class_definition | 36,332 | 42,447 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py | null | 9,330 |
class ProphetNetFeedForward(nn.Module):
"""
This is the residual two feed-forward layer block based on the original Transformer implementation.
"""
def __init__(self, config: ProphetNetConfig, ffn_dim: int):
super().__init__()
self.activation_fn = ACT2FN[config.activation_function]
... | class_definition | 42,450 | 43,439 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py | null | 9,331 |
class ProphetNetNgramSelfAttention(nn.Module):
def __init__(self, config: ProphetNetConfig):
super().__init__()
self.hidden_size = config.hidden_size
self.num_buckets = config.num_buckets
self.relative_max_distance = config.relative_max_distance
self.num_attn_heads = config.... | class_definition | 43,442 | 59,796 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py | null | 9,332 |
class ProphetNetEncoderLayer(nn.Module):
"""
Encoder block for Prophetnet
"""
def __init__(self, config: ProphetNetConfig):
super().__init__()
# 1st residual block
self.self_attn = ProphetNetAttention(config, config.num_encoder_attention_heads)
self.self_attn_layer_norm ... | class_definition | 59,799 | 61,157 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py | null | 9,333 |
class ProphetNetDecoderLayer(nn.Module):
"""
Decoder block for Prophetnet
"""
def __init__(self, config: ProphetNetConfig):
super().__init__()
# 1st residual block
self.self_attn = ProphetNetNgramSelfAttention(config)
self.self_attn_layer_norm = LayerNorm(config.hidden_s... | class_definition | 61,160 | 64,688 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py | null | 9,334 |
class ProphetNetEncoder(ProphetNetPreTrainedModel):
r"""
word_embeddings (`torch.nn.Embeddings` of shape `(config.vocab_size, config.hidden_size)`, *optional*):
The word embedding parameters. This can be used to initialize [`ProphetNetEncoder`] with pre-defined word
embeddings instead of random... | class_definition | 64,807 | 70,606 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py | null | 9,335 |
class ProphetNetDecoder(ProphetNetPreTrainedModel):
r"""
word_embeddings (`torch.nn.Embeddings` of shape `(config.vocab_size, config.hidden_size)`, *optional*):
The word embedding parameters. This can be used to initialize [`ProphetNetEncoder`] with pre-defined word
embeddings instead of random... | class_definition | 70,725 | 88,290 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py | null | 9,336 |
class ProphetNetModel(ProphetNetPreTrainedModel):
_tied_weights_keys = ["encoder.word_embeddings.weight", "decoder.word_embeddings.weight"]
def __init__(self, config: ProphetNetConfig):
super().__init__(config)
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_i... | class_definition | 88,446 | 94,348 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py | null | 9,337 |
class ProphetNetForConditionalGeneration(ProphetNetPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["encoder.word_embeddings.weight", "decoder.word_embeddings.weight", "lm_head.weight"]
def __init__(self, config: ProphetNetConfig):
super().__init__(config)
self.prophetnet = ProphetNetMo... | class_definition | 94,510 | 102,568 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py | null | 9,338 |
class ProphetNetForCausalLM(ProphetNetPreTrainedModel, GenerationMixin):
_tied_weights_keys = [
"prophetnet.word_embeddings.weight",
"prophetnet.decoder.word_embeddings.weight",
"lm_head.weight",
]
def __init__(self, config: ProphetNetConfig):
# set config for CLM
co... | class_definition | 102,768 | 113,713 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py | null | 9,339 |
class ProphetNetDecoderWrapper(ProphetNetPreTrainedModel):
"""
This is a wrapper class, so that [`ProphetNetForCausalLM`] can correctly be loaded from pretrained prophetnet
classes.
"""
def __init__(self, config: ProphetNetConfig):
super().__init__(config)
self.word_embeddings = nn... | class_definition | 113,716 | 114,496 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py | null | 9,340 |
class TextNetImageProcessor(BaseImageProcessor):
r"""
Constructs a TextNet image processor.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by
`do_resize` in the `pr... | class_definition | 1,446 | 17,574 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/image_processing_textnet.py | null | 9,341 |
class TextNetConvLayer(nn.Module):
def __init__(self, config: TextNetConfig):
super().__init__()
self.kernel_size = config.stem_kernel_size
self.stride = config.stem_stride
self.activation_function = config.stem_act_func
padding = (
(config.kernel_size[0] // 2, ... | class_definition | 1,629 | 2,814 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/modeling_textnet.py | null | 9,342 |
class TextNetRepConvLayer(nn.Module):
r"""
This layer supports re-parameterization by combining multiple convolutional branches
(e.g., main convolution, vertical, horizontal, and identity branches) during training.
At inference time, these branches can be collapsed into a single convolution for
effi... | class_definition | 2,817 | 6,386 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/modeling_textnet.py | null | 9,343 |
class TextNetStage(nn.Module):
def __init__(self, config: TextNetConfig, depth: int):
super().__init__()
kernel_size = config.conv_layer_kernel_sizes[depth]
stride = config.conv_layer_strides[depth]
num_layers = len(kernel_size)
stage_in_channel_size = config.hidden_sizes[de... | class_definition | 6,389 | 7,289 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/modeling_textnet.py | null | 9,344 |
class TextNetEncoder(nn.Module):
def __init__(self, config: TextNetConfig):
super().__init__()
stages = []
num_stages = len(config.conv_layer_kernel_sizes)
for stage_ix in range(num_stages):
stages.append(TextNetStage(config, stage_ix))
self.stages = nn.ModuleLi... | class_definition | 7,292 | 8,250 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/modeling_textnet.py | null | 9,345 |
class TextNetPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = TextNetConfig
base_model_prefix = "textnet"
main_input_name = "pixel_values"
def _init_weights(... | class_definition | 9,491 | 10,217 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/modeling_textnet.py | null | 9,346 |
class TextNetModel(TextNetPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.stem = TextNetConvLayer(config)
self.encoder = TextNetEncoder(config)
self.pooler = nn.AdaptiveAvgPool2d((2, 2))
self.post_init()
@add_start_docstrings_to_model_forward(... | class_definition | 10,362 | 12,135 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/modeling_textnet.py | null | 9,347 |
class TextNetForImageClassification(TextNetPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.textnet = TextNetModel(config)
self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))
self.flatten = nn.Flatten()
self.fc =... | class_definition | 12,339 | 16,353 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/modeling_textnet.py | null | 9,348 |
class TextNetBackbone(TextNetPreTrainedModel, BackboneMixin):
def __init__(self, config):
super().__init__(config)
super()._init_backbone(config)
self.textnet = TextNetModel(config)
self.num_features = config.hidden_sizes
# initialize weights and apply final processing
... | class_definition | 16,502 | 18,902 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/modeling_textnet.py | null | 9,349 |
class TextNetConfig(BackboneConfigMixin, PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`TextNextModel`]. It is used to instantiate a
TextNext model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the def... | class_definition | 912 | 6,181 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/configuration_textnet.py | null | 9,350 |
class FalconLinear(nn.Linear):
def forward(self, input: torch.Tensor) -> torch.Tensor:
hidden_states = input @ self.weight.T
if self.bias is None:
return hidden_states
return hidden_states + self.bias | class_definition | 2,303 | 2,543 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py | null | 9,351 |
class FalconRotaryEmbedding(nn.Module):
def __init__(self, config: FalconConfig, device=None):
super().__init__()
# BC: "rope_type" was originally "type"
if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
self.rope_type = config.rope_scaling.get("rope_type", ... | class_definition | 4,509 | 7,706 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py | null | 9,352 |
class FalconAttention(nn.Module):
def __init__(self, config: FalconConfig, layer_idx=None):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.hidden_size // self.num_heads
se... | class_definition | 10,051 | 22,570 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py | null | 9,353 |
class FalconFlashAttention2(FalconAttention):
"""
Falcon flash attention module. This module inherits from `FalconAttention` as the weights of the module stays
untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
flash attention and deal wit... | class_definition | 22,573 | 27,822 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py | null | 9,354 |
class FalconMLP(nn.Module):
def __init__(self, config: FalconConfig):
super().__init__()
hidden_size = config.hidden_size
self.dense_h_to_4h = FalconLinear(hidden_size, config.ffn_hidden_size, bias=config.bias)
self.act = get_activation(config.activation)
self.dense_4h_to_h ... | class_definition | 27,825 | 28,418 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py | null | 9,355 |
class FalconDecoderLayer(nn.Module):
def __init__(self, config: FalconConfig, layer_idx=None):
super().__init__()
hidden_size = config.hidden_size
self.num_heads = config.num_attention_heads
self.self_attention = FALCON_ATTENTION_CLASSES[config._attn_implementation](config, layer_id... | class_definition | 28,638 | 32,509 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py | null | 9,356 |
class FalconPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = FalconConfig
base_model_prefix = "transformer"
supports_gradient_checkpointing = True
_no_split_m... | class_definition | 38,058 | 39,960 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py | null | 9,357 |
class FalconModel(FalconPreTrainedModel):
def __init__(self, config: FalconConfig):
super().__init__(config)
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.use_alibi = config.alibi
# Embedding + LN Embedding
self.word_embeddings... | class_definition | 40,120 | 54,981 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py | null | 9,358 |
class FalconForCausalLM(FalconPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config: FalconConfig):
super().__init__(config)
self.transformer = FalconModel(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
... | class_definition | 55,168 | 60,400 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py | null | 9,359 |
class FalconForSequenceClassification(FalconPreTrainedModel):
def __init__(self, config: FalconConfig):
super().__init__(config)
self.num_labels = config.num_labels
self.transformer = FalconModel(config)
self.score = nn.Linear(config.hidden_size, config.num_labels, bias=False)
... | class_definition | 61,196 | 66,279 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py | null | 9,360 |
class FalconForTokenClassification(FalconPreTrainedModel):
def __init__(self, config: FalconConfig):
super().__init__(config)
self.num_labels = config.num_labels
self.transformer = FalconModel(config)
if getattr(config, "classifier_dropout", None) is not None:
classifier... | class_definition | 66,512 | 69,849 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py | null | 9,361 |
class FalconForQuestionAnswering(FalconPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.transformer = FalconModel(config)
self.qa_outputs = nn.Linear(config.hidden_size, 2)
# Initialize weights and apply final processing
self.post_init()
@add_... | class_definition | 70,156 | 73,912 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py | null | 9,362 |
class FalconConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`FalconModel`]. It is used to instantiate a Falcon
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar... | class_definition | 797 | 10,887 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/configuration_falcon.py | null | 9,363 |
class BlenderbotSmallLearnedPositionalEmbedding(nn.Embedding):
"""
This module learns positional embeddings up to a fixed maximum size.
"""
def __init__(self, num_embeddings: int, embedding_dim: int):
super().__init__(num_embeddings, embedding_dim)
def forward(self, input_ids_shape: torch.... | class_definition | 2,402 | 3,070 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py | null | 9,364 |
class BlenderbotSmallAttention(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 | 3,167 | 10,579 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py | null | 9,365 |
class BlenderbotSmallEncoderLayer(nn.Module):
def __init__(self, config: BlenderbotSmallConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = BLENDERBOT_SMALL_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=self.embed_dim,
num_heads=c... | class_definition | 10,703 | 13,935 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py | null | 9,366 |
class BlenderbotSmallDecoderLayer(nn.Module):
def __init__(self, config: BlenderbotSmallConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = BLENDERBOT_SMALL_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=self.embed_dim,
num_heads=c... | class_definition | 14,206 | 20,191 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py | null | 9,367 |
class BlenderbotSmallPreTrainedModel(PreTrainedModel):
config_class = BlenderbotSmallConfig
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.nor... | class_definition | 20,194 | 21,218 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py | null | 9,368 |
class BlenderbotSmallEncoder(BlenderbotSmallPreTrainedModel):
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`BlenderbotSmallEncoderLayer`].
Args:
config: BlenderbotSmallConfig
embed_tokens (nn.Embedding): output embedding
"""
... | class_definition | 29,582 | 37,330 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py | null | 9,369 |
class BlenderbotSmallDecoder(BlenderbotSmallPreTrainedModel):
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`BlenderbotSmallDecoderLayer`]
Args:
config: BlenderbotSmallConfig
embed_tokens (nn.Embedding): output embedding
"""
def __init__(sel... | class_definition | 37,333 | 49,821 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py | null | 9,370 |
class BlenderbotSmallModel(BlenderbotSmallPreTrainedModel):
_tied_weights_keys = ["decoder.embed_tokens.weight", "encoder.embed_tokens.weight"]
def __init__(self, config: BlenderbotSmallConfig):
super().__init__(config)
padding_idx, vocab_size = config.pad_token_id, config.vocab_size
s... | class_definition | 49,988 | 55,498 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py | null | 9,371 |
class BlenderbotSmallForConditionalGeneration(BlenderbotSmallPreTrainedModel, GenerationMixin):
base_model_prefix = "model"
_keys_to_ignore_on_load_missing = ["final_logits_bias"]
_tied_weights_keys = ["decoder.embed_tokens.weight", "encoder.embed_tokens.weight", "lm_head.weight"]
def __init__(self, co... | class_definition | 55,659 | 61,857 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py | null | 9,372 |
class BlenderbotSmallDecoderWrapper(BlenderbotSmallPreTrainedModel):
"""
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().__... | class_definition | 61,959 | 62,433 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py | null | 9,373 |
class BlenderbotSmallForCausalLM(BlenderbotSmallPreTrainedModel, 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.... | class_definition | 62,583 | 71,991 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_blenderbot_small.py | null | 9,374 |
class FlaxBlenderbotSmallAttention(nn.Module):
config: BlenderbotSmallConfig
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.emb... | class_definition | 12,472 | 19,887 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py | null | 9,375 |
class FlaxBlenderbotSmallEncoderLayer(nn.Module):
config: BlenderbotSmallConfig
dtype: jnp.dtype = jnp.float32
def setup(self) -> None:
self.embed_dim = self.config.d_model
self.self_attn = FlaxBlenderbotSmallAttention(
config=self.config,
embed_dim=self.embed_dim,
... | class_definition | 19,996 | 22,311 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py | null | 9,376 |
class FlaxBlenderbotSmallEncoderLayerCollection(nn.Module):
config: BlenderbotSmallConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.layers = [
FlaxBlenderbotSmallEncoderLayer(self.config, name=str(i), dtype=self.dtype)
for i in rang... | class_definition | 22,430 | 24,414 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py | null | 9,377 |
class FlaxBlenderbotSmallDecoderLayer(nn.Module):
config: BlenderbotSmallConfig
dtype: jnp.dtype = jnp.float32
def setup(self) -> None:
self.embed_dim = self.config.d_model
self.self_attn = FlaxBlenderbotSmallAttention(
config=self.config,
embed_dim=self.embed_dim,
... | class_definition | 24,523 | 28,113 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py | null | 9,378 |
class FlaxBlenderbotSmallDecoderLayerCollection(nn.Module):
config: BlenderbotSmallConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.layers = [
FlaxBlenderbotSmallDecoderLayer(self.config, name=str(i), dtype=self.dtype)
for i in rang... | class_definition | 28,232 | 30,982 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py | null | 9,379 |
class FlaxBlenderbotSmallEncoder(nn.Module):
config: BlenderbotSmallConfig
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.paddi... | class_definition | 30,985 | 33,088 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py | null | 9,380 |
class FlaxBlenderbotSmallDecoder(nn.Module):
config: BlenderbotSmallConfig
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.paddi... | class_definition | 33,091 | 35,649 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py | null | 9,381 |
class FlaxBlenderbotSmallModule(nn.Module):
config: BlenderbotSmallConfig
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.norma... | class_definition | 35,752 | 38,233 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py | null | 9,382 |
class FlaxBlenderbotSmallPreTrainedModel(FlaxPreTrainedModel):
config_class = BlenderbotSmallConfig
base_model_prefix: str = "model"
module_class: nn.Module = None
def __init__(
self,
config: BlenderbotSmallConfig,
input_shape: Tuple[int] = (1, 1),
seed: int = 0,
... | class_definition | 38,236 | 52,841 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py | null | 9,383 |
class FlaxBlenderbotSmallModel(FlaxBlenderbotSmallPreTrainedModel):
config: BlenderbotSmallConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
module_class = FlaxBlenderbotSmallModule | class_definition | 53,020 | 53,233 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py | null | 9,384 |
class FlaxBlenderbotSmallForConditionalGenerationModule(nn.Module):
config: BlenderbotSmallConfig
dtype: jnp.dtype = jnp.float32
bias_init: Callable[..., jnp.ndarray] = jax.nn.initializers.zeros
def setup(self):
self.model = FlaxBlenderbotSmallModule(config=self.config, dtype=self.dtype)
... | class_definition | 53,479 | 56,099 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py | null | 9,385 |
class FlaxBlenderbotSmallForConditionalGeneration(FlaxBlenderbotSmallPreTrainedModel):
module_class = FlaxBlenderbotSmallForConditionalGenerationModule
dtype: jnp.dtype = jnp.float32
@add_start_docstrings(BLENDERBOT_SMALL_DECODE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=FlaxCausalLMOutpu... | class_definition | 56,261 | 64,073 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py | null | 9,386 |
class BlenderbotSmallConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`BlenderbotSmallModel`]. It is used to instantiate
an BlenderbotSmall model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the d... | class_definition | 1,124 | 7,817 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/configuration_blenderbot_small.py | null | 9,387 |
class BlenderbotSmallOnnxConfig(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 | 7,893 | 18,212 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/configuration_blenderbot_small.py | null | 9,388 |
class TFBlenderbotSmallLearnedPositionalEmbedding(keras.layers.Embedding):
"""
This module learns positional embeddings up to a fixed maximum size.
"""
def __init__(self, num_embeddings: int, embedding_dim: int, **kwargs):
super().__init__(num_embeddings, embedding_dim, **kwargs)
def call(... | class_definition | 4,252 | 5,012 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py | null | 9,389 |
class TFBlenderbotSmallAttention(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,
):
... | class_definition | 5,114 | 12,699 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py | null | 9,390 |
class TFBlenderbotSmallEncoderLayer(keras.layers.Layer):
def __init__(self, config: BlenderbotSmallConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.d_model
self.self_attn = TFBlenderbotSmallAttention(
self.embed_dim, config.encoder_attention_heads, dropout=co... | class_definition | 12,804 | 16,544 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py | null | 9,391 |
class TFBlenderbotSmallDecoderLayer(keras.layers.Layer):
def __init__(self, config: BlenderbotSmallConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.d_model
self.self_attn = TFBlenderbotSmallAttention(
embed_dim=self.embed_dim,
num_heads=config.dec... | class_definition | 16,649 | 23,545 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py | null | 9,392 |
class TFBlenderbotSmallPreTrainedModel(TFPreTrainedModel):
config_class = BlenderbotSmallConfig
base_model_prefix = "model" | class_definition | 23,548 | 23,679 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py | null | 9,393 |
class TFBlenderbotSmallEncoder(keras.layers.Layer):
config_class = BlenderbotSmallConfig
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`TFBlenderbotSmallEncoderLayer`].
Args:
config: BlenderbotSmallConfig
"""
def __init__(self... | class_definition | 32,431 | 40,376 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py | null | 9,394 |
class TFBlenderbotSmallDecoder(keras.layers.Layer):
config_class = BlenderbotSmallConfig
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`TFBlenderbotSmallDecoderLayer`]
Args:
config: BlenderbotSmallConfig
embed_tokens: output embedding
"""
... | class_definition | 40,399 | 52,489 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py | null | 9,395 |
class TFBlenderbotSmallMainLayer(keras.layers.Layer):
config_class = BlenderbotSmallConfig
def __init__(self, config: BlenderbotSmallConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.shared = keras.layers.Embedding(
input_dim=config.vocab_size,
... | class_definition | 52,512 | 57,613 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py | null | 9,396 |
class TFBlenderbotSmallModel(TFBlenderbotSmallPreTrainedModel):
def __init__(self, config: BlenderbotSmallConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.model = TFBlenderbotSmallMainLayer(config, name="model")
def get_encoder(self):
return self.model.encod... | class_definition | 57,781 | 61,709 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py | null | 9,397 |
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 | 61,778 | 62,584 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py | null | 9,398 |
class TFBlenderbotSmallForConditionalGeneration(TFBlenderbotSmallPreTrainedModel, 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(... | class_definition | 62,746 | 71,605 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py | null | 9,399 |
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