text stringlengths 31 243k | type stringclasses 1
value | start int64 36 275k | end int64 286 280k | depth int64 0 1 | filepath stringlengths 85 188 | parent_class stringclasses 3
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|---|---|---|---|---|---|---|---|
class MT5ForTokenClassification(MT5PreTrainedModel):
_tied_weights_keys = ["transformer.encoder.embed_tokens.weight"]
# Copied from transformers.models.t5.modeling_t5.T5ForTokenClassification.__init__ with T5->MT5
def __init__(self, config: MT5Config):
super().__init__(config)
self.num_labe... | class_definition | 107,144 | 109,829 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mt5/modeling_mt5.py | null | 8,400 |
class MT5ForQuestionAnswering(MT5PreTrainedModel):
_keys_to_ignore_on_load_unexpected = ["decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight"]
_tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
# Copied from transformers.models.t5.modeling_t5.T5ForQuesti... | class_definition | 110,112 | 119,261 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mt5/modeling_mt5.py | null | 8,401 |
class MT5Tokenizer(T5Tokenizer):
pass | class_definition | 675 | 716 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mt5/tokenization_mt5.py | null | 8,402 |
class TFMT5Model(TFT5Model):
r"""
This class overrides [`TFT5Model`]. Please check the superclass for the appropriate documentation alongside usage
examples.
Examples:
```python
>>> from transformers import TFMT5Model, AutoTokenizer
>>> model = TFMT5Model.from_pretrained("google/mt5-small... | class_definition | 902 | 1,763 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mt5/modeling_tf_mt5.py | null | 8,403 |
class TFMT5ForConditionalGeneration(TFT5ForConditionalGeneration):
r"""
This class overrides [`TFT5ForConditionalGeneration`]. Please check the superclass for the appropriate
documentation alongside usage examples.
Examples:
```python
>>> from transformers import TFMT5ForConditionalGeneration,... | class_definition | 1,766 | 2,592 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mt5/modeling_tf_mt5.py | null | 8,404 |
class TFMT5EncoderModel(TFT5EncoderModel):
r"""
This class overrides [`TFT5EncoderModel`]. Please check the superclass for the appropriate documentation alongside
usage examples.
Examples:
```python
>>> from transformers import TFMT5EncoderModel, AutoTokenizer
>>> model = TFMT5EncoderMode... | class_definition | 2,595 | 3,324 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mt5/modeling_tf_mt5.py | null | 8,405 |
class MT5TokenizerFast(T5TokenizerFast):
pass | class_definition | 679 | 728 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mt5/tokenization_mt5_fast.py | null | 8,406 |
class FlaxMT5Model(FlaxT5Model):
r"""
This class overrides [`FlaxT5Model`]. Please check the superclass for the appropriate documentation alongside usage
examples.
Examples:
```python
>>> from transformers import FlaxMT5Model, AutoTokenizer
>>> model = FlaxMT5Model.from_pretrained("google... | class_definition | 1,507 | 2,399 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mt5/modeling_flax_mt5.py | null | 8,407 |
class FlaxMT5EncoderModel(FlaxT5EncoderModel):
r"""
This class overrides [`FlaxT5EncoderModel`]. Please check the superclass for the appropriate documentation
alongside usage examples.
Examples:
```python
>>> from transformers import FlaxT5EncoderModel, AutoTokenizer
>>> model = FlaxT5Enc... | class_definition | 2,402 | 3,290 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mt5/modeling_flax_mt5.py | null | 8,408 |
class FlaxMT5ForConditionalGeneration(FlaxT5ForConditionalGeneration):
r"""
This class overrides [`FlaxT5ForConditionalGeneration`]. Please check the superclass for the appropriate
documentation alongside usage examples.
Examples:
```python
>>> from transformers import FlaxMT5ForConditionalGen... | class_definition | 3,293 | 4,241 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mt5/modeling_flax_mt5.py | null | 8,409 |
class MT5Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`MT5Model`] or a [`TFMT5Model`]. It is used to
instantiate a mT5 model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yiel... | class_definition | 843 | 6,748 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mt5/configuration_mt5.py | null | 8,410 |
class MT5OnnxConfig(OnnxSeq2SeqConfigWithPast):
@property
# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
def inputs(self) -> Mapping[str, Mapping[int, str]]:
common_inputs = {
"input_ids": {0: "batch", 1: "encoder_sequence"},
"attention_mask": {0: "... | class_definition | 6,751 | 7,958 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mt5/configuration_mt5.py | null | 8,411 |
class DebertaV2Tokenizer(PreTrainedTokenizer):
r"""
Constructs a DeBERTa-v2 tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).
Args:
vocab_file (`str`):
[SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that
... | class_definition | 961 | 10,812 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta_v2/tokenization_deberta_v2.py | null | 8,412 |
class SPMTokenizer:
r"""
Constructs a tokenizer based on [SentencePiece](https://github.com/google/sentencepiece).
Args:
vocab_file (`str`):
[SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that
contains the vocabulary necessar... | class_definition | 10,815 | 19,057 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta_v2/tokenization_deberta_v2.py | null | 8,413 |
class DebertaV2Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`DebertaV2Model`]. It is used to instantiate a
DeBERTa-v2 model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield... | class_definition | 1,013 | 7,466 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta_v2/configuration_deberta_v2.py | null | 8,414 |
class DebertaV2OnnxConfig(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"}
if self._config.type_v... | class_definition | 7,469 | 8,881 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta_v2/configuration_deberta_v2.py | null | 8,415 |
class DebertaV2SelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def for... | class_definition | 1,619 | 2,179 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta_v2/modeling_deberta_v2.py | null | 8,416 |
class DisentangledSelfAttention(nn.Module):
"""
Disentangled self-attention module
Parameters:
config (`DebertaV2Config`):
A model config class instance with the configuration to build a new model. The schema is similar to
*BertConfig*, for more details, please refer [`Deber... | class_definition | 5,516 | 15,373 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta_v2/modeling_deberta_v2.py | null | 8,417 |
class DebertaV2Attention(nn.Module):
def __init__(self, config):
super().__init__()
self.self = DisentangledSelfAttention(config)
self.output = DebertaV2SelfOutput(config)
self.config = config
def forward(
self,
hidden_states,
attention_mask,
outp... | class_definition | 15,476 | 16,502 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta_v2/modeling_deberta_v2.py | null | 8,418 |
class DebertaV2Intermediate(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.i... | class_definition | 16,596 | 17,166 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta_v2/modeling_deberta_v2.py | null | 8,419 |
class DebertaV2Output(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
se... | class_definition | 17,275 | 17,866 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta_v2/modeling_deberta_v2.py | null | 8,420 |
class DebertaV2Layer(nn.Module):
def __init__(self, config):
super().__init__()
self.attention = DebertaV2Attention(config)
self.intermediate = DebertaV2Intermediate(config)
self.output = DebertaV2Output(config)
def forward(
self,
hidden_states,
attention... | class_definition | 17,965 | 19,030 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta_v2/modeling_deberta_v2.py | null | 8,421 |
class ConvLayer(nn.Module):
def __init__(self, config):
super().__init__()
kernel_size = getattr(config, "conv_kernel_size", 3)
groups = getattr(config, "conv_groups", 1)
self.conv_act = getattr(config, "conv_act", "tanh")
self.conv = nn.Conv1d(
config.hidden_size... | class_definition | 19,033 | 20,496 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta_v2/modeling_deberta_v2.py | null | 8,422 |
class DebertaV2Embeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config):
super().__init__()
pad_token_id = getattr(config, "pad_token_id", 0)
self.embedding_size = getattr(config, "embedding_size", config.hidden_size... | class_definition | 20,628 | 23,806 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta_v2/modeling_deberta_v2.py | null | 8,423 |
class DebertaV2Encoder(nn.Module):
"""Modified BertEncoder with relative position bias support"""
def __init__(self, config):
super().__init__()
self.layer = nn.ModuleList([DebertaV2Layer(config) for _ in range(config.num_hidden_layers)])
self.relative_attention = getattr(config, "rela... | class_definition | 23,809 | 29,088 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta_v2/modeling_deberta_v2.py | null | 8,424 |
class DebertaV2PreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = DebertaV2Config
base_model_prefix = "deberta"
_keys_to_ignore_on_load_unexpected = ["position_embe... | class_definition | 29,197 | 30,254 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta_v2/modeling_deberta_v2.py | null | 8,425 |
class DebertaV2Model(DebertaV2PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.embeddings = DebertaV2Embeddings(config)
self.encoder = DebertaV2Encoder(config)
self.z_steps = 0
self.config = config
# Initialize weights and apply final proce... | class_definition | 33,895 | 38,411 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta_v2/modeling_deberta_v2.py | null | 8,426 |
class LegacyDebertaV2PredictionHeadTransform(nn.Module):
def __init__(self, config):
super().__init__()
self.embedding_size = getattr(config, "embedding_size", config.hidden_size)
self.dense = nn.Linear(config.hidden_size, self.embedding_size)
if isinstance(config.hidden_act, str):
... | class_definition | 38,534 | 39,302 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta_v2/modeling_deberta_v2.py | null | 8,427 |
class LegacyDebertaV2LMPredictionHead(nn.Module):
def __init__(self, config):
super().__init__()
self.transform = LegacyDebertaV2PredictionHeadTransform(config)
self.embedding_size = getattr(config, "embedding_size", config.hidden_size)
# The output weights are the same as the input... | class_definition | 39,305 | 40,244 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta_v2/modeling_deberta_v2.py | null | 8,428 |
class LegacyDebertaV2OnlyMLMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = LegacyDebertaV2LMPredictionHead(config)
def forward(self, sequence_output):
prediction_scores = self.predictions(sequence_output)
return prediction_scores | class_definition | 40,247 | 40,553 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta_v2/modeling_deberta_v2.py | null | 8,429 |
class DebertaV2LMPredictionHead(nn.Module):
"""https://github.com/microsoft/DeBERTa/blob/master/DeBERTa/deberta/bert.py#L270"""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
if isinstance(config.hidden_act, str):
... | class_definition | 40,556 | 41,582 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta_v2/modeling_deberta_v2.py | null | 8,430 |
class DebertaV2OnlyMLMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.lm_head = DebertaV2LMPredictionHead(config)
# note that the input embeddings must be passed as an argument
def forward(self, sequence_output, word_embeddings):
prediction_scores = self.lm_head... | class_definition | 41,585 | 41,972 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta_v2/modeling_deberta_v2.py | null | 8,431 |
class DebertaV2ForMaskedLM(DebertaV2PreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder.weight", "cls.predictions.decoder.bias"]
_keys_to_ignore_on_load_unexpected = r"mask_predictions.*"
def __init__(self, config):
super().__init__(config)
self.legacy = config.legacy
s... | class_definition | 42,083 | 46,009 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta_v2/modeling_deberta_v2.py | null | 8,432 |
class ContextPooler(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.pooler_hidden_size, config.pooler_hidden_size)
self.dropout = nn.Dropout(config.pooler_dropout)
self.config = config
def forward(self, hidden_states):
# We "pool"... | class_definition | 46,085 | 46,826 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta_v2/modeling_deberta_v2.py | null | 8,433 |
class DebertaV2ForSequenceClassification(DebertaV2PreTrainedModel):
def __init__(self, config):
super().__init__(config)
num_labels = getattr(config, "num_labels", 2)
self.num_labels = num_labels
self.deberta = DebertaV2Model(config)
self.pooler = ContextPooler(config)
... | class_definition | 47,054 | 52,113 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta_v2/modeling_deberta_v2.py | null | 8,434 |
class DebertaV2ForTokenClassification(DebertaV2PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.deberta = DebertaV2Model(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(c... | class_definition | 52,461 | 55,024 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta_v2/modeling_deberta_v2.py | null | 8,435 |
class DebertaV2ForQuestionAnswering(DebertaV2PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.deberta = DebertaV2Model(config)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weig... | class_definition | 55,317 | 59,648 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta_v2/modeling_deberta_v2.py | null | 8,436 |
class DebertaV2ForMultipleChoice(DebertaV2PreTrainedModel):
def __init__(self, config):
super().__init__(config)
num_labels = getattr(config, "num_labels", 2)
self.num_labels = num_labels
self.deberta = DebertaV2Model(config)
self.pooler = ContextPooler(config)
outp... | class_definition | 59,885 | 63,829 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta_v2/modeling_deberta_v2.py | null | 8,437 |
class DebertaV2TokenizerFast(PreTrainedTokenizerFast):
r"""
Constructs a DeBERTa-v2 fast tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).
Args:
vocab_file (`str`):
[SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extensio... | class_definition | 1,145 | 9,757 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta_v2/tokenization_deberta_v2_fast.py | null | 8,438 |
class TFDebertaV2ContextPooler(keras.layers.Layer):
def __init__(self, config: DebertaV2Config, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(config.pooler_hidden_size, name="dense")
self.dropout = TFDebertaV2StableDropout(config.pooler_dropout, name="dropout")
... | class_definition | 1,845 | 3,160 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta_v2/modeling_tf_deberta_v2.py | null | 8,439 |
class TFDebertaV2XSoftmax(keras.layers.Layer):
"""
Masked Softmax which is optimized for saving memory
Args:
input (`tf.Tensor`): The input tensor that will apply softmax.
mask (`tf.Tensor`): The mask matrix where 0 indicate that element will be ignored in the softmax calculation.
d... | class_definition | 3,267 | 4,095 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta_v2/modeling_tf_deberta_v2.py | null | 8,440 |
class TFDebertaV2StableDropout(keras.layers.Layer):
"""
Optimized dropout module for stabilizing the training
Args:
drop_prob (float): the dropout probabilities
"""
def __init__(self, drop_prob, **kwargs):
super().__init__(**kwargs)
self.drop_prob = drop_prob
@tf.custo... | class_definition | 4,207 | 5,505 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta_v2/modeling_tf_deberta_v2.py | null | 8,441 |
class TFDebertaV2SelfOutput(keras.layers.Layer):
def __init__(self, config: DebertaV2Config, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(config.hidden_size, name="dense")
self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm... | class_definition | 5,614 | 6,965 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta_v2/modeling_tf_deberta_v2.py | null | 8,442 |
class TFDebertaV2Attention(keras.layers.Layer):
def __init__(self, config: DebertaV2Config, **kwargs):
super().__init__(**kwargs)
self.self = TFDebertaV2DisentangledSelfAttention(config, name="self")
self.dense_output = TFDebertaV2SelfOutput(config, name="output")
self.config = confi... | class_definition | 7,073 | 8,746 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta_v2/modeling_tf_deberta_v2.py | null | 8,443 |
class TFDebertaV2Intermediate(keras.layers.Layer):
def __init__(self, config: DebertaV2Config, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
... | class_definition | 8,857 | 9,889 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta_v2/modeling_tf_deberta_v2.py | null | 8,444 |
class TFDebertaV2Output(keras.layers.Layer):
def __init__(self, config: DebertaV2Config, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
self.Lay... | class_definition | 9,994 | 11,481 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta_v2/modeling_tf_deberta_v2.py | null | 8,445 |
class TFDebertaV2Layer(keras.layers.Layer):
def __init__(self, config: DebertaV2Config, **kwargs):
super().__init__(**kwargs)
self.attention = TFDebertaV2Attention(config, name="attention")
self.intermediate = TFDebertaV2Intermediate(config, name="intermediate")
self.bert_output = T... | class_definition | 11,585 | 13,577 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta_v2/modeling_tf_deberta_v2.py | null | 8,446 |
class TFDebertaV2ConvLayer(keras.layers.Layer):
def __init__(self, config: DebertaV2Config, **kwargs):
super().__init__(**kwargs)
self.kernel_size = getattr(config, "conv_kernel_size", 3)
# groups = getattr(config, "conv_groups", 1)
self.conv_act = get_tf_activation(getattr(config, ... | class_definition | 13,580 | 16,343 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta_v2/modeling_tf_deberta_v2.py | null | 8,447 |
class TFDebertaV2Encoder(keras.layers.Layer):
def __init__(self, config: DebertaV2Config, **kwargs):
super().__init__(**kwargs)
self.layer = [TFDebertaV2Layer(config, name=f"layer_._{i}") for i in range(config.num_hidden_layers)]
self.relative_attention = getattr(config, "relative_attention... | class_definition | 16,346 | 22,028 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta_v2/modeling_tf_deberta_v2.py | null | 8,448 |
class TFDebertaV2DisentangledSelfAttention(keras.layers.Layer):
"""
Disentangled self-attention module
Parameters:
config (`DebertaV2Config`):
A model config class instance with the configuration to build a new model. The schema is similar to
*BertConfig*, for more details, ... | class_definition | 25,439 | 39,302 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta_v2/modeling_tf_deberta_v2.py | null | 8,449 |
class TFDebertaV2Embeddings(keras.layers.Layer):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.config = config
self.embedding_size = getattr(config, "embedding_size", config.hidden_siz... | class_definition | 39,406 | 44,372 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta_v2/modeling_tf_deberta_v2.py | null | 8,450 |
class TFDebertaV2PredictionHeadTransform(keras.layers.Layer):
def __init__(self, config: DebertaV2Config, **kwargs):
super().__init__(**kwargs)
self.embedding_size = getattr(config, "embedding_size", config.hidden_size)
self.dense = keras.layers.Dense(
units=self.embedding_size... | class_definition | 44,494 | 45,975 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta_v2/modeling_tf_deberta_v2.py | null | 8,451 |
class TFDebertaV2LMPredictionHead(keras.layers.Layer):
def __init__(self, config: DebertaV2Config, input_embeddings: keras.layers.Layer, **kwargs):
super().__init__(**kwargs)
self.config = config
self.embedding_size = getattr(config, "embedding_size", config.hidden_size)
self.trans... | class_definition | 46,090 | 48,105 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta_v2/modeling_tf_deberta_v2.py | null | 8,452 |
class TFDebertaV2OnlyMLMHead(keras.layers.Layer):
def __init__(self, config: DebertaV2Config, input_embeddings: keras.layers.Layer, **kwargs):
super().__init__(**kwargs)
self.predictions = TFDebertaV2LMPredictionHead(config, input_embeddings, name="predictions")
def call(self, sequence_output: ... | class_definition | 48,215 | 48,936 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta_v2/modeling_tf_deberta_v2.py | null | 8,453 |
class TFDebertaV2MainLayer(keras.layers.Layer):
config_class = DebertaV2Config
def __init__(self, config: DebertaV2Config, **kwargs):
super().__init__(**kwargs)
self.config = config
self.embeddings = TFDebertaV2Embeddings(config, name="embeddings")
self.encoder = TFDebertaV2En... | class_definition | 49,044 | 52,441 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta_v2/modeling_tf_deberta_v2.py | null | 8,454 |
class TFDebertaV2PreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = DebertaV2Config
base_model_prefix = "deberta" | class_definition | 52,555 | 52,822 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta_v2/modeling_tf_deberta_v2.py | null | 8,455 |
class TFDebertaV2Model(TFDebertaV2PreTrainedModel):
def __init__(self, config: DebertaV2Config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.deberta = TFDebertaV2MainLayer(config, name="deberta")
@unpack_inputs
@add_start_docstrings_to_model_forward(DEBERTA_INPUTS_D... | class_definition | 58,201 | 59,982 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta_v2/modeling_tf_deberta_v2.py | null | 8,456 |
class TFDebertaV2ForMaskedLM(TFDebertaV2PreTrainedModel, TFMaskedLanguageModelingLoss):
def __init__(self, config: DebertaV2Config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
if config.is_decoder:
logger.warning(
"If you want to use `TFDebertaV2ForMa... | class_definition | 60,200 | 63,621 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta_v2/modeling_tf_deberta_v2.py | null | 8,457 |
class TFDebertaV2ForSequenceClassification(TFDebertaV2PreTrainedModel, TFSequenceClassificationLoss):
def __init__(self, config: DebertaV2Config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.deberta = TFDebertaV2MainLayer(config, ... | class_definition | 63,970 | 67,988 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta_v2/modeling_tf_deberta_v2.py | null | 8,458 |
class TFDebertaV2ForTokenClassification(TFDebertaV2PreTrainedModel, TFTokenClassificationLoss):
def __init__(self, config: DebertaV2Config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.deberta = TFDebertaV2MainLayer(config, name="... | class_definition | 68,341 | 71,565 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta_v2/modeling_tf_deberta_v2.py | null | 8,459 |
class TFDebertaV2ForQuestionAnswering(TFDebertaV2PreTrainedModel, TFQuestionAnsweringLoss):
def __init__(self, config: DebertaV2Config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.deberta = TFDebertaV2MainLayer(config, name="debe... | class_definition | 71,974 | 76,164 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta_v2/modeling_tf_deberta_v2.py | null | 8,460 |
class TFDebertaV2ForMultipleChoice(TFDebertaV2PreTrainedModel, TFMultipleChoiceLoss):
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
# _keys_to_ignore_on_load_unexpected = [r"mlm___cls", r"nsp___cls", r"cls.predictions", r"cls.seq_relationship"]
... | class_definition | 76,401 | 81,403 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta_v2/modeling_tf_deberta_v2.py | null | 8,461 |
class EnglishNumberNormalizer:
def __init__(self):
self.ones = ["", "one", "two", "three", "four", "five", "six", "seven", "eight", "nine"]
self.teens = [
"",
"eleven",
"twelve",
"thirteen",
"fourteen",
"fifteen",
"s... | class_definition | 728 | 7,018 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speecht5/number_normalizer.py | null | 8,462 |
class SpeechT5FeatureExtractor(SequenceFeatureExtractor):
r"""
Constructs a SpeechT5 feature extractor.
This class can pre-process a raw speech signal by (optionally) normalizing to zero-mean unit-variance, for use by
the SpeechT5 speech encoder prenet.
This class can also extract log-mel filter b... | class_definition | 1,080 | 17,808 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speecht5/feature_extraction_speecht5.py | null | 8,463 |
class SpeechT5Processor(ProcessorMixin):
r"""
Constructs a SpeechT5 processor which wraps a feature extractor and a tokenizer into a single processor.
[`SpeechT5Processor`] offers all the functionalities of [`SpeechT5FeatureExtractor`] and [`SpeechT5Tokenizer`]. See
the docstring of [`~SpeechT5Processo... | class_definition | 719 | 7,561 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speecht5/processing_speecht5.py | null | 8,464 |
class SpeechT5NoLayerNormConvLayer(nn.Module):
def __init__(self, config, layer_id=0):
super().__init__()
self.in_conv_dim = config.conv_dim[layer_id - 1] if layer_id > 0 else 1
self.out_conv_dim = config.conv_dim[layer_id]
self.conv = nn.Conv1d(
self.in_conv_dim,
... | class_definition | 8,590 | 9,319 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speecht5/modeling_speecht5.py | null | 8,465 |
class SpeechT5LayerNormConvLayer(nn.Module):
def __init__(self, config, layer_id=0):
super().__init__()
self.in_conv_dim = config.conv_dim[layer_id - 1] if layer_id > 0 else 1
self.out_conv_dim = config.conv_dim[layer_id]
self.conv = nn.Conv1d(
self.in_conv_dim,
... | class_definition | 9,434 | 10,413 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speecht5/modeling_speecht5.py | null | 8,466 |
class SpeechT5GroupNormConvLayer(nn.Module):
def __init__(self, config, layer_id=0):
super().__init__()
self.in_conv_dim = config.conv_dim[layer_id - 1] if layer_id > 0 else 1
self.out_conv_dim = config.conv_dim[layer_id]
self.conv = nn.Conv1d(
self.in_conv_dim,
... | class_definition | 10,528 | 11,425 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speecht5/modeling_speecht5.py | null | 8,467 |
class SpeechT5SinusoidalPositionalEmbedding(nn.Module):
"""This module produces sinusoidal positional embeddings of any length."""
def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int] = None):
super().__init__()
self.offset = 2
self.embedding_dim = embed... | class_definition | 11,569 | 14,981 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speecht5/modeling_speecht5.py | null | 8,468 |
class SpeechT5PositionalConvEmbedding(nn.Module):
def __init__(self, config):
super().__init__()
self.conv = nn.Conv1d(
config.hidden_size,
config.hidden_size,
kernel_size=config.num_conv_pos_embeddings,
padding=config.num_conv_pos_embeddings // 2,
... | class_definition | 15,101 | 16,894 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speecht5/modeling_speecht5.py | null | 8,469 |
class SpeechT5ScaledPositionalEncoding(nn.Module):
"""
Scaled positional encoding, see §3.2 in https://arxiv.org/abs/1809.08895
"""
def __init__(self, dropout, dim, max_len=5000):
pe = torch.zeros(max_len, dim)
position = torch.arange(0, max_len).unsqueeze(1)
div_term = torch.ex... | class_definition | 16,897 | 17,803 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speecht5/modeling_speecht5.py | null | 8,470 |
class SpeechT5RelativePositionalEncoding(torch.nn.Module):
def __init__(self, dim, max_length=1000):
super().__init__()
self.dim = dim
self.max_length = max_length
self.pe_k = torch.nn.Embedding(2 * max_length, dim)
def forward(self, hidden_states):
seq_len = hidden_stat... | class_definition | 17,806 | 18,475 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speecht5/modeling_speecht5.py | null | 8,471 |
class SpeechT5SamePadLayer(nn.Module):
def __init__(self, num_conv_pos_embeddings):
super().__init__()
self.num_pad_remove = 1 if num_conv_pos_embeddings % 2 == 0 else 0
def forward(self, hidden_states):
if self.num_pad_remove > 0:
hidden_states = hidden_states[:, :, : -self... | class_definition | 18,584 | 18,949 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speecht5/modeling_speecht5.py | null | 8,472 |
class SpeechT5FeatureEncoder(nn.Module):
"""Construct the features from raw audio waveform"""
def __init__(self, config):
super().__init__()
if config.feat_extract_norm == "group":
conv_layers = [SpeechT5GroupNormConvLayer(config, layer_id=0)] + [
SpeechT5NoLayerNor... | class_definition | 19,060 | 20,794 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speecht5/modeling_speecht5.py | null | 8,473 |
class SpeechT5FeatureProjection(nn.Module):
def __init__(self, config):
super().__init__()
self.layer_norm = nn.LayerNorm(config.conv_dim[-1], eps=config.layer_norm_eps)
self.projection = nn.Linear(config.conv_dim[-1], config.hidden_size)
self.dropout = nn.Dropout(config.feat_proj_dr... | class_definition | 20,908 | 21,560 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speecht5/modeling_speecht5.py | null | 8,474 |
class SpeechT5SpeechEncoderPrenet(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.feature_encoder = SpeechT5FeatureEncoder(config)
self.feature_projection = SpeechT5FeatureProjection(config)
# model only needs masking vector if mask prob ... | class_definition | 21,563 | 28,015 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speecht5/modeling_speecht5.py | null | 8,475 |
class SpeechT5SpeechDecoderPrenet(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layers = nn.ModuleList(
[
nn.Linear(
config.num_mel_bins if i == 0 else config.speech_decoder_prenet_units,
... | class_definition | 28,018 | 30,179 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speecht5/modeling_speecht5.py | null | 8,476 |
class SpeechT5BatchNormConvLayer(nn.Module):
def __init__(self, config, layer_id=0):
super().__init__()
if layer_id == 0:
in_conv_dim = config.num_mel_bins
else:
in_conv_dim = config.speech_decoder_postnet_units
if layer_id == config.speech_decoder_postnet_l... | class_definition | 30,182 | 31,512 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speecht5/modeling_speecht5.py | null | 8,477 |
class SpeechT5SpeechDecoderPostnet(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.feat_out = nn.Linear(config.hidden_size, config.num_mel_bins * config.reduction_factor)
self.prob_out = nn.Linear(config.hidden_size, config.reduction_factor)
... | class_definition | 31,515 | 32,619 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speecht5/modeling_speecht5.py | null | 8,478 |
class SpeechT5TextEncoderPrenet(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, config.pad_token_id)
self.encode_positions = SpeechT5ScaledPositionalEncoding(
config.po... | class_definition | 32,622 | 33,380 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speecht5/modeling_speecht5.py | null | 8,479 |
class SpeechT5TextDecoderPrenet(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.dropout = nn.Dropout(config.positional_dropout)
self.embed_scale = math.sqrt(config.hidden_size) if config.scale_embedding else 1.0
self.embed_tokens = nn.Emb... | class_definition | 33,383 | 34,934 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speecht5/modeling_speecht5.py | null | 8,480 |
class SpeechT5TextDecoderPostnet(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
def forward(self, hidden_states: torch.Tensor):
return self.lm_head(hidden_states)
de... | class_definition | 34,937 | 35,407 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speecht5/modeling_speecht5.py | null | 8,481 |
class SpeechT5Attention(nn.Module):
"""
Multi-headed attention from 'Attention Is All You Need' paper with relative position bias (see
https://aclanthology.org/N18-2074.pdf)
"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_deco... | class_definition | 35,410 | 42,932 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speecht5/modeling_speecht5.py | null | 8,482 |
class SpeechT5FeedForward(nn.Module):
def __init__(self, config, intermediate_size):
super().__init__()
self.intermediate_dropout = nn.Dropout(config.activation_dropout)
self.intermediate_dense = nn.Linear(config.hidden_size, intermediate_size)
if isinstance(config.hidden_act, str):... | class_definition | 42,935 | 43,910 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speecht5/modeling_speecht5.py | null | 8,483 |
class SpeechT5EncoderLayer(nn.Module):
def __init__(self, config: SpeechT5Config):
super().__init__()
self.attention = SpeechT5Attention(
embed_dim=config.hidden_size,
num_heads=config.encoder_attention_heads,
dropout=config.attention_dropout,
is_decod... | class_definition | 43,913 | 46,488 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speecht5/modeling_speecht5.py | null | 8,484 |
class SpeechT5DecoderLayer(nn.Module):
def __init__(self, config: SpeechT5Config):
super().__init__()
self.self_attn = SpeechT5Attention(
embed_dim=config.hidden_size,
num_heads=config.decoder_attention_heads,
dropout=config.attention_dropout,
is_decod... | class_definition | 46,491 | 51,671 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speecht5/modeling_speecht5.py | null | 8,485 |
class SpeechT5PreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = SpeechT5Config
base_model_prefix = "speecht5"
main_input_name = "input_values"
supports_gradien... | class_definition | 51,674 | 53,531 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speecht5/modeling_speecht5.py | null | 8,486 |
class SpeechT5Encoder(SpeechT5PreTrainedModel):
"""
Transformer encoder consisting of *config.encoder_layers* layers. Each layer is a [`SpeechT5EncoderLayer`].
"""
def __init__(self, config: SpeechT5Config):
super().__init__(config)
self.layer_norm = nn.LayerNorm(config.hidden_size, eps... | class_definition | 53,534 | 59,714 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speecht5/modeling_speecht5.py | null | 8,487 |
class SpeechT5EncoderWithSpeechPrenet(SpeechT5PreTrainedModel):
"""
Wrapper around SpeechT5Encoder that applies SpeechT5SpeechEncoderPrenet to convert the audio waveform data to
hidden features.
"""
def __init__(self, config: SpeechT5Config):
super().__init__(config)
self.prenet = S... | class_definition | 59,717 | 60,988 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speecht5/modeling_speecht5.py | null | 8,488 |
class SpeechT5EncoderWithTextPrenet(SpeechT5PreTrainedModel):
"""
Wrapper around SpeechT5Encoder that applies SpeechT5TextEncoderPrenet to convert the input_ids to hidden features.
"""
def __init__(self, config: SpeechT5Config):
super().__init__(config)
self.prenet = SpeechT5TextEncoder... | class_definition | 60,991 | 62,389 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speecht5/modeling_speecht5.py | null | 8,489 |
class SpeechT5EncoderWithoutPrenet(SpeechT5PreTrainedModel):
"""
This wrapper class is a helper class to correctly load pretrained checkpoints when used in combination with
[`SpeechT5Model`].
"""
def __init__(self, config: SpeechT5Config):
super().__init__(config)
self.wrapped_encod... | class_definition | 62,392 | 63,491 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speecht5/modeling_speecht5.py | null | 8,490 |
class SpeechT5Decoder(SpeechT5PreTrainedModel):
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`SpeechT5DecoderLayer`]
"""
def __init__(self, config: SpeechT5Config):
super().__init__(config)
self.layerdrop = config.decoder_layerdrop
self... | class_definition | 63,494 | 74,229 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speecht5/modeling_speecht5.py | null | 8,491 |
class SpeechT5DecoderWithSpeechPrenet(SpeechT5PreTrainedModel):
"""
Wrapper around SpeechT5Decoder that applies SpeechT5SpeechDecoderPrenet to convert log-mel filterbanks to hidden
features.
"""
def __init__(self, config: SpeechT5Config):
super().__init__(config)
self.prenet = Speec... | class_definition | 74,232 | 76,162 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speecht5/modeling_speecht5.py | null | 8,492 |
class SpeechT5DecoderWithTextPrenet(SpeechT5PreTrainedModel):
"""
Wrapper around SpeechT5Decoder that applies SpeechT5TextDecoderPrenet to convert input tokens to hidden features.
"""
def __init__(self, config: SpeechT5Config):
super().__init__(config)
self.prenet = SpeechT5TextDecoderP... | class_definition | 76,165 | 78,227 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speecht5/modeling_speecht5.py | null | 8,493 |
class SpeechT5DecoderWithoutPrenet(SpeechT5PreTrainedModel):
"""
This wrapper class is a helper class to correctly load pretrained checkpoints when used in combination with
[`SpeechT5Model`].
"""
def __init__(self, config: SpeechT5Config):
super().__init__(config)
self.wrapped_decod... | class_definition | 78,230 | 79,955 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speecht5/modeling_speecht5.py | null | 8,494 |
class SpeechT5GuidedMultiheadAttentionLoss(nn.Module):
"""
Guided attention loss from the paper [Efficiently Trainable Text-to-Speech System Based on Deep Convolutional
Networks with Guided Attention](https://arxiv.org/abs/1710.08969), adapted for multi-head attention.
"""
def __init__(self, config... | class_definition | 79,958 | 82,568 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speecht5/modeling_speecht5.py | null | 8,495 |
class SpeechT5SpectrogramLoss(nn.Module):
"""
Loss computation used by SpeechT5ForTextToSpeech.
"""
def __init__(self, config: SpeechT5Config):
super().__init__()
self.use_guided_attention_loss = config.use_guided_attention_loss
self.guided_attention_loss_num_heads = config.guid... | class_definition | 82,571 | 84,984 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speecht5/modeling_speecht5.py | null | 8,496 |
class SpeechT5Model(SpeechT5PreTrainedModel):
def __init__(
self,
config: SpeechT5Config,
encoder: Optional[nn.Module] = None,
decoder: Optional[nn.Module] = None,
):
super().__init__(config)
self.config = config
self.encoder = SpeechT5EncoderWithoutPrenet... | class_definition | 92,941 | 99,719 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speecht5/modeling_speecht5.py | null | 8,497 |
class SpeechT5ForSpeechToText(SpeechT5PreTrainedModel):
_tied_weights_keys = ["text_decoder_postnet.lm_head.weight"]
def __init__(self, config: SpeechT5Config):
super().__init__(config)
if config.vocab_size is None:
raise ValueError(
f"You are trying to instantiate ... | class_definition | 99,845 | 109,423 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speecht5/modeling_speecht5.py | null | 8,498 |
class SpeechT5ForTextToSpeech(SpeechT5PreTrainedModel):
main_input_name = "input_ids"
def __init__(self, config: SpeechT5Config):
super().__init__(config)
if config.vocab_size is None:
raise ValueError(
f"You are trying to instantiate {self.__class__} with a configu... | class_definition | 116,051 | 134,097 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speecht5/modeling_speecht5.py | null | 8,499 |
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