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class TFXLMWithLMHeadModelOutput(ModelOutput):
"""
Base class for [`TFXLMWithLMHeadModel`] outputs.
Args:
logits (`tf.Tensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
... | class_definition | 22,585 | 23,897 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm/modeling_tf_xlm.py | null | 7,000 |
class TFXLMModel(TFXLMPreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.transformer = TFXLMMainLayer(config, name="transformer")
@unpack_inputs
@add_start_docstrings_to_model_forward(XLM_INPUTS_DOCSTRING.format("batch_size, seq... | class_definition | 31,292 | 33,269 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm/modeling_tf_xlm.py | null | 7,001 |
class TFXLMPredLayer(keras.layers.Layer):
"""
Prediction layer (cross_entropy or adaptive_softmax).
"""
def __init__(self, config, input_embeddings, **kwargs):
super().__init__(**kwargs)
self.asm = config.asm
self.n_words = config.n_words
self.pad_index = config.pad_ind... | class_definition | 33,272 | 34,932 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm/modeling_tf_xlm.py | null | 7,002 |
class TFXLMWithLMHeadModel(TFXLMPreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.transformer = TFXLMMainLayer(config, name="transformer")
self.pred_layer = TFXLMPredLayer(config, self.transformer.embeddings, name="pred_layer_._... | class_definition | 35,131 | 38,705 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm/modeling_tf_xlm.py | null | 7,003 |
class TFXLMForSequenceClassification(TFXLMPreTrainedModel, TFSequenceClassificationLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.transformer = TFXLMMainLayer(config, name="transformer")
self... | class_definition | 38,913 | 42,397 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm/modeling_tf_xlm.py | null | 7,004 |
class TFXLMForMultipleChoice(TFXLMPreTrainedModel, TFMultipleChoiceLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.transformer = TFXLMMainLayer(config, name="transformer")
self.sequence_summary = TFSequenceSummary(config, initializer_ra... | class_definition | 42,626 | 47,964 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm/modeling_tf_xlm.py | null | 7,005 |
class TFXLMForTokenClassification(TFXLMPreTrainedModel, TFTokenClassificationLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.transformer = TFXLMMainLayer(config, name="transformer")
self.dropo... | class_definition | 48,191 | 51,713 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm/modeling_tf_xlm.py | null | 7,006 |
class TFXLMForQuestionAnsweringSimple(TFXLMPreTrainedModel, TFQuestionAnsweringLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.transformer = TFXLMMainLayer(config, name="transformer")
self.qa_outputs = keras.layers.Dense(
con... | class_definition | 51,997 | 56,409 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm/modeling_tf_xlm.py | null | 7,007 |
class XLMConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`XLMModel`] or a [`TFXLMModel`]. It is used to
instantiate a XLM model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield... | class_definition | 875 | 10,439 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm/configuration_xlm.py | null | 7,008 |
class XLMOnnxConfig(OnnxConfig):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
else:
dynamic_axis = {0: "batch", 1: "sequence"}
return OrderedDict(
... | class_definition | 10,515 | 11,009 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm/configuration_xlm.py | null | 7,009 |
class MultiHeadAttention(nn.Module):
NEW_ID = itertools.count()
def __init__(self, n_heads, dim, config):
super().__init__()
self.layer_id = next(MultiHeadAttention.NEW_ID)
self.dim = dim
self.n_heads = n_heads
self.dropout = config.attention_dropout
assert self.... | class_definition | 2,818 | 6,986 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm/modeling_xlm.py | null | 7,010 |
class TransformerFFN(nn.Module):
def __init__(self, in_dim, dim_hidden, out_dim, config):
super().__init__()
self.dropout = config.dropout
self.lin1 = nn.Linear(in_dim, dim_hidden)
self.lin2 = nn.Linear(dim_hidden, out_dim)
self.act = gelu if config.gelu_activation else nn.fu... | class_definition | 6,989 | 7,767 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm/modeling_xlm.py | null | 7,011 |
class XLMPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = XLMConfig
load_tf_weights = None
base_model_prefix = "transformer"
def __init__(self, *inputs, **kw... | class_definition | 7,770 | 9,723 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm/modeling_xlm.py | null | 7,012 |
class XLMForQuestionAnsweringOutput(ModelOutput):
"""
Base class for outputs of question answering models using a `SquadHead`.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned if both `start_positions` and `end_positions` are provided):
Classification loss as the su... | class_definition | 9,737 | 12,866 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm/modeling_xlm.py | null | 7,013 |
class XLMModel(XLMPreTrainedModel):
def __init__(self, config):
super().__init__(config)
# encoder / decoder, output layer
self.is_encoder = config.is_encoder
self.is_decoder = not config.is_encoder
if self.is_decoder:
raise NotImplementedError("Currently XLM can... | class_definition | 18,065 | 27,749 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm/modeling_xlm.py | null | 7,014 |
class XLMPredLayer(nn.Module):
"""
Prediction layer (cross_entropy or adaptive_softmax).
"""
def __init__(self, config):
super().__init__()
self.asm = config.asm
self.n_words = config.n_words
self.pad_index = config.pad_index
dim = config.emb_dim
if conf... | class_definition | 27,752 | 29,102 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm/modeling_xlm.py | null | 7,015 |
class XLMWithLMHeadModel(XLMPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["pred_layer.proj.weight"]
def __init__(self, config):
super().__init__(config)
self.transformer = XLMModel(config)
self.pred_layer = XLMPredLayer(config)
# Initialize weights and apply final pr... | class_definition | 29,301 | 33,058 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm/modeling_xlm.py | null | 7,016 |
class XLMForSequenceClassification(XLMPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.transformer = XLMModel(config)
self.sequence_summary = SequenceSummary(config)
# Initialize wei... | class_definition | 33,266 | 37,259 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm/modeling_xlm.py | null | 7,017 |
class XLMForQuestionAnsweringSimple(XLMPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.transformer = XLMModel(config)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights and apply final processing
self.post_ini... | class_definition | 37,544 | 41,969 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm/modeling_xlm.py | null | 7,018 |
class XLMForQuestionAnswering(XLMPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.transformer = XLMModel(config)
self.qa_outputs = SQuADHead(config)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_mod... | class_definition | 42,266 | 47,338 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm/modeling_xlm.py | null | 7,019 |
class XLMForTokenClassification(XLMPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.transformer = XLMModel(config)
self.dropout = nn.Dropout(config.dropout)
self.classifier = nn.Linear(config.hidden_size, config.... | class_definition | 47,565 | 50,452 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm/modeling_xlm.py | null | 7,020 |
class XLMForMultipleChoice(XLMPreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.transformer = XLMModel(config)
self.sequence_summary = SequenceSummary(config)
self.logits_proj = nn.Linear(config.num_labels, 1)
... | class_definition | 50,681 | 54,809 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm/modeling_xlm.py | null | 7,021 |
class TFBertPreTrainingLoss:
"""
Loss function suitable for BERT-like pretraining, that is, the task of pretraining a language model by combining
NSP + MLM. .. note:: Any label of -100 will be ignored (along with the corresponding logits) in the loss
computation.
"""
def hf_compute_loss(self, l... | class_definition | 2,848 | 4,460 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_tf_bert.py | null | 7,022 |
class TFBertEmbeddings(keras.layers.Layer):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.hidden_size = config.hidden_size
self.max_position_em... | class_definition | 4,463 | 7,804 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_tf_bert.py | null | 7,023 |
class TFBertSelfAttention(keras.layers.Layer):
def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the number... | class_definition | 7,807 | 14,624 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_tf_bert.py | null | 7,024 |
class TFBertSelfOutput(keras.layers.Layer):
def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
self.LayerNorm... | class_definition | 14,627 | 15,954 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_tf_bert.py | null | 7,025 |
class TFBertAttention(keras.layers.Layer):
def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.self_attention = TFBertSelfAttention(config, name="self")
self.dense_output = TFBertSelfOutput(config, name="output")
def prune_heads(self, heads):
raise... | class_definition | 15,957 | 17,789 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_tf_bert.py | null | 7,026 |
class TFBertIntermediate(keras.layers.Layer):
def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
if is... | class_definition | 17,792 | 18,814 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_tf_bert.py | null | 7,027 |
class TFBertOutput(keras.layers.Layer):
def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
self.LayerNorm = k... | class_definition | 18,817 | 20,146 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_tf_bert.py | null | 7,028 |
class TFBertLayer(keras.layers.Layer):
def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.attention = TFBertAttention(config, name="attention")
self.is_decoder = config.is_decoder
self.add_cross_attention = config.add_cross_attention
if self.ad... | class_definition | 20,149 | 24,878 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_tf_bert.py | null | 7,029 |
class TFBertEncoder(keras.layers.Layer):
def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.layer = [TFBertLayer(config, name=f"layer_._{i}") for i in range(config.num_hidden_layers)]
def call(
self,
hidden_states: tf.T... | class_definition | 24,881 | 27,962 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_tf_bert.py | null | 7,030 |
class TFBertPooler(keras.layers.Layer):
def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.hidden_size,
kernel_initializer=get_initializer(config.initializer_range),
activation="tanh",
... | class_definition | 27,965 | 28,934 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_tf_bert.py | null | 7,031 |
class TFBertPredictionHeadTransform(keras.layers.Layer):
def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.hidden_size,
kernel_initializer=get_initializer(config.initializer_range),
name="de... | class_definition | 28,937 | 30,334 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_tf_bert.py | null | 7,032 |
class TFBertLMPredictionHead(keras.layers.Layer):
def __init__(self, config: BertConfig, input_embeddings: keras.layers.Layer, **kwargs):
super().__init__(**kwargs)
self.config = config
self.hidden_size = config.hidden_size
self.transform = TFBertPredictionHeadTransform(config, nam... | class_definition | 30,337 | 32,296 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_tf_bert.py | null | 7,033 |
class TFBertMLMHead(keras.layers.Layer):
def __init__(self, config: BertConfig, input_embeddings: keras.layers.Layer, **kwargs):
super().__init__(**kwargs)
self.predictions = TFBertLMPredictionHead(config, input_embeddings, name="predictions")
def call(self, sequence_output: tf.Tensor) -> tf.T... | class_definition | 32,299 | 33,002 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_tf_bert.py | null | 7,034 |
class TFBertNSPHead(keras.layers.Layer):
def __init__(self, config: BertConfig, **kwargs):
super().__init__(**kwargs)
self.seq_relationship = keras.layers.Dense(
units=2,
kernel_initializer=get_initializer(config.initializer_range),
name="seq_relationship",
... | class_definition | 33,005 | 33,849 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_tf_bert.py | null | 7,035 |
class TFBertMainLayer(keras.layers.Layer):
config_class = BertConfig
def __init__(self, config: BertConfig, add_pooling_layer: bool = True, **kwargs):
super().__init__(**kwargs)
self.config = config
self.is_decoder = config.is_decoder
self.embeddings = TFBertEmbeddings(config,... | class_definition | 33,872 | 43,608 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_tf_bert.py | null | 7,036 |
class TFBertPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = BertConfig
base_model_prefix = "bert" | class_definition | 43,611 | 43,865 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_tf_bert.py | null | 7,037 |
class TFBertForPreTrainingOutput(ModelOutput):
"""
Output type of [`TFBertForPreTraining`].
Args:
prediction_logits (`tf.Tensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax... | class_definition | 43,879 | 45,539 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_tf_bert.py | null | 7,038 |
class TFBertModel(TFBertPreTrainedModel):
def __init__(self, config: BertConfig, add_pooling_layer: bool = True, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.bert = TFBertMainLayer(config, add_pooling_layer, name="bert")
@unpack_inputs
@add_start_docstrings_to_model... | class_definition | 51,512 | 55,418 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_tf_bert.py | null | 7,039 |
class TFBertForPreTraining(TFBertPreTrainedModel, TFBertPreTrainingLoss):
# 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"position_ids",
r"cls.predictions.decoder.weight",
r"cls.pred... | class_definition | 55,648 | 61,142 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_tf_bert.py | null | 7,040 |
class TFBertForMaskedLM(TFBertPreTrainedModel, TFMaskedLanguageModelingLoss):
# 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"pooler",
r"cls.seq_relationship",
r"cls.predictions.deco... | class_definition | 61,247 | 65,321 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_tf_bert.py | null | 7,041 |
class TFBertLMHeadModel(TFBertPreTrainedModel, TFCausalLanguageModelingLoss):
# 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"pooler",
r"cls.seq_relationship",
r"cls.predictions.deco... | class_definition | 65,324 | 71,833 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_tf_bert.py | null | 7,042 |
class TFBertForNextSentencePrediction(TFBertPreTrainedModel, TFNextSentencePredictionLoss):
# 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"cls.predictions"]
def __init__(self, config: Ber... | class_definition | 71,973 | 75,819 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_tf_bert.py | null | 7,043 |
class TFBertForSequenceClassification(TFBertPreTrainedModel, TFSequenceClassificationLoss):
# 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_relationshi... | class_definition | 76,041 | 80,079 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_tf_bert.py | null | 7,044 |
class TFBertForMultipleChoice(TFBertPreTrainedModel, 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"]
_keys_to... | class_definition | 80,310 | 85,071 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_tf_bert.py | null | 7,045 |
class TFBertForTokenClassification(TFBertPreTrainedModel, TFTokenClassificationLoss):
# 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"pooler",
r"mlm___cls",
r"nsp___cls",
r"c... | class_definition | 85,300 | 89,237 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_tf_bert.py | null | 7,046 |
class TFBertForQuestionAnswering(TFBertPreTrainedModel, TFQuestionAnsweringLoss):
# 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"pooler",
r"mlm___cls",
r"nsp___cls",
r"cls.p... | class_definition | 89,523 | 94,292 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_tf_bert.py | null | 7,047 |
class BertConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`BertModel`] or a [`TFBertModel`]. It is used to
instantiate a BERT model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will ... | class_definition | 959 | 6,745 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/configuration_bert.py | null | 7,048 |
class BertOnnxConfig(OnnxConfig):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
else:
dynamic_axis = {0: "batch", 1: "sequence"}
return OrderedDict(
... | class_definition | 6,748 | 7,243 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/configuration_bert.py | null | 7,049 |
class TFBertTokenizer(keras.layers.Layer):
"""
This is an in-graph tokenizer for BERT. It should be initialized similarly to other tokenizers, using the
`from_pretrained()` method. It can also be initialized with the `from_tokenizer()` method, which imports settings
from an existing standard tokenizer o... | class_definition | 337 | 11,894 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/tokenization_bert_tf.py | null | 7,050 |
class FlaxBertForPreTrainingOutput(ModelOutput):
"""
Output type of [`BertForPreTraining`].
Args:
prediction_logits (`jnp.ndarray` of shape `(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftM... | class_definition | 2,022 | 3,632 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_flax_bert.py | null | 7,051 |
class FlaxBertEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
config: BertConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.word_embeddings = nn.Embed(
self.config.vocab_size,
... | class_definition | 8,196 | 10,017 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_flax_bert.py | null | 7,052 |
class FlaxBertSelfAttention(nn.Module):
config: BertConfig
causal: bool = False
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.head_dim = self.config.hidden_size // self.config.num_attention_heads
if self.config.hidden_size % self.config.num_attenti... | class_definition | 10,020 | 17,911 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_flax_bert.py | null | 7,053 |
class FlaxBertSelfOutput(nn.Module):
config: BertConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.dense = nn.Dense(
self.config.hidden_size,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
dtype=se... | class_definition | 17,914 | 18,728 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_flax_bert.py | null | 7,054 |
class FlaxBertAttention(nn.Module):
config: BertConfig
causal: bool = False
dtype: jnp.dtype = jnp.float32
def setup(self):
self.self = FlaxBertSelfAttention(self.config, causal=self.causal, dtype=self.dtype)
self.output = FlaxBertSelfOutput(self.config, dtype=self.dtype)
def __cal... | class_definition | 18,731 | 20,135 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_flax_bert.py | null | 7,055 |
class FlaxBertIntermediate(nn.Module):
config: BertConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.dense = nn.Dense(
self.config.intermediate_size,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
... | class_definition | 20,138 | 20,716 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_flax_bert.py | null | 7,056 |
class FlaxBertOutput(nn.Module):
config: BertConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.dense = nn.Dense(
self.config.hidden_size,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
dtype=self.d... | class_definition | 20,719 | 21,537 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_flax_bert.py | null | 7,057 |
class FlaxBertLayer(nn.Module):
config: BertConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.attention = FlaxBertAttention(self.config, causal=self.config.is_decoder, dtype=self.dtype)
self.intermediate = FlaxBertIntermediate(self.config, dtype=sel... | class_definition | 21,540 | 23,680 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_flax_bert.py | null | 7,058 |
class FlaxBertLayerCollection(nn.Module):
config: BertConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
gradient_checkpointing: bool = False
def setup(self):
if self.gradient_checkpointing:
FlaxBertCheckpointLayer = remat(FlaxBertLayer, static_argnums=(5, 6, 7))
... | class_definition | 23,683 | 26,675 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_flax_bert.py | null | 7,059 |
class FlaxBertEncoder(nn.Module):
config: BertConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
gradient_checkpointing: bool = False
def setup(self):
self.layer = FlaxBertLayerCollection(
self.config,
dtype=self.dtype,
gradient_checkpointi... | class_definition | 26,678 | 27,915 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_flax_bert.py | null | 7,060 |
class FlaxBertPooler(nn.Module):
config: BertConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.dense = nn.Dense(
self.config.hidden_size,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
dtype=self.d... | class_definition | 27,918 | 28,437 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_flax_bert.py | null | 7,061 |
class FlaxBertPredictionHeadTransform(nn.Module):
config: BertConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.dense = nn.Dense(self.config.hidden_size, dtype=self.dtype)
self.activation = ACT2FN[self.config.hidden_act]
self.LayerNorm = nn.LayerNorm(epsilon=self.config.la... | class_definition | 28,440 | 28,981 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_flax_bert.py | null | 7,062 |
class FlaxBertLMPredictionHead(nn.Module):
config: BertConfig
dtype: jnp.dtype = jnp.float32
bias_init: Callable[..., np.ndarray] = jax.nn.initializers.zeros
def setup(self):
self.transform = FlaxBertPredictionHeadTransform(self.config, dtype=self.dtype)
self.decoder = nn.Dense(self.con... | class_definition | 28,984 | 29,880 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_flax_bert.py | null | 7,063 |
class FlaxBertOnlyMLMHead(nn.Module):
config: BertConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.predictions = FlaxBertLMPredictionHead(self.config, dtype=self.dtype)
def __call__(self, hidden_states, shared_embedding=None):
hidden_states = self.predictions(hidden_states, ... | class_definition | 29,883 | 30,266 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_flax_bert.py | null | 7,064 |
class FlaxBertOnlyNSPHead(nn.Module):
dtype: jnp.dtype = jnp.float32
def setup(self):
self.seq_relationship = nn.Dense(2, dtype=self.dtype)
def __call__(self, pooled_output):
return self.seq_relationship(pooled_output) | class_definition | 30,269 | 30,517 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_flax_bert.py | null | 7,065 |
class FlaxBertPreTrainingHeads(nn.Module):
config: BertConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.predictions = FlaxBertLMPredictionHead(self.config, dtype=self.dtype)
self.seq_relationship = nn.Dense(2, dtype=self.dtype)
def __call__(self, hidden_states, pooled_output... | class_definition | 30,520 | 31,087 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_flax_bert.py | null | 7,066 |
class FlaxBertPreTrainedModel(FlaxPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = BertConfig
base_model_prefix = "bert"
module_class: nn.Module = None
def __init__(
... | class_definition | 31,090 | 38,996 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_flax_bert.py | null | 7,067 |
class FlaxBertModule(nn.Module):
config: BertConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
add_pooling_layer: bool = True
gradient_checkpointing: bool = False
def setup(self):
self.embeddings = FlaxBertEmbeddings(self.config, dtype=self.dtype)
self.encoder = ... | class_definition | 38,999 | 41,719 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_flax_bert.py | null | 7,068 |
class FlaxBertModel(FlaxBertPreTrainedModel):
module_class = FlaxBertModule | class_definition | 41,875 | 41,954 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_flax_bert.py | null | 7,069 |
class FlaxBertForPreTrainingModule(nn.Module):
config: BertConfig
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self):
self.bert = FlaxBertModule(
config=self.config,
dtype=self.dtype,
gradient_checkpointing=self.gradient_check... | class_definition | 42,073 | 43,947 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_flax_bert.py | null | 7,070 |
class FlaxBertForPreTraining(FlaxBertPreTrainedModel):
module_class = FlaxBertForPreTrainingModule | class_definition | 44,181 | 44,283 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_flax_bert.py | null | 7,071 |
class FlaxBertForMaskedLMModule(nn.Module):
config: BertConfig
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self):
self.bert = FlaxBertModule(
config=self.config,
add_pooling_layer=False,
dtype=self.dtype,
gradient... | class_definition | 45,151 | 46,860 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_flax_bert.py | null | 7,072 |
class FlaxBertForMaskedLM(FlaxBertPreTrainedModel):
module_class = FlaxBertForMaskedLMModule | class_definition | 46,965 | 47,061 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_flax_bert.py | null | 7,073 |
class FlaxBertForNextSentencePredictionModule(nn.Module):
config: BertConfig
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self):
self.bert = FlaxBertModule(
config=self.config,
dtype=self.dtype,
gradient_checkpointing=self.gra... | class_definition | 47,174 | 48,718 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_flax_bert.py | null | 7,074 |
class FlaxBertForNextSentencePrediction(FlaxBertPreTrainedModel):
module_class = FlaxBertForNextSentencePredictionModule | class_definition | 48,858 | 48,982 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_flax_bert.py | null | 7,075 |
class FlaxBertForSequenceClassificationModule(nn.Module):
config: BertConfig
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self):
self.bert = FlaxBertModule(
config=self.config,
dtype=self.dtype,
gradient_checkpointing=self.gra... | class_definition | 50,087 | 51,879 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_flax_bert.py | null | 7,076 |
class FlaxBertForSequenceClassification(FlaxBertPreTrainedModel):
module_class = FlaxBertForSequenceClassificationModule | class_definition | 52,101 | 52,225 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_flax_bert.py | null | 7,077 |
class FlaxBertForMultipleChoiceModule(nn.Module):
config: BertConfig
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self):
self.bert = FlaxBertModule(
config=self.config,
dtype=self.dtype,
gradient_checkpointing=self.gradient_ch... | class_definition | 52,381 | 54,495 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_flax_bert.py | null | 7,078 |
class FlaxBertForMultipleChoice(FlaxBertPreTrainedModel):
module_class = FlaxBertForMultipleChoiceModule | class_definition | 54,726 | 54,834 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_flax_bert.py | null | 7,079 |
class FlaxBertForTokenClassificationModule(nn.Module):
config: BertConfig
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self):
self.bert = FlaxBertModule(
config=self.config,
dtype=self.dtype,
add_pooling_layer=False,
... | class_definition | 55,102 | 56,890 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_flax_bert.py | null | 7,080 |
class FlaxBertForTokenClassification(FlaxBertPreTrainedModel):
module_class = FlaxBertForTokenClassificationModule | class_definition | 57,119 | 57,237 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_flax_bert.py | null | 7,081 |
class FlaxBertForQuestionAnsweringModule(nn.Module):
config: BertConfig
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self):
self.bert = FlaxBertModule(
config=self.config,
dtype=self.dtype,
add_pooling_layer=False,
... | class_definition | 57,374 | 59,079 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_flax_bert.py | null | 7,082 |
class FlaxBertForQuestionAnswering(FlaxBertPreTrainedModel):
module_class = FlaxBertForQuestionAnsweringModule | class_definition | 59,366 | 59,480 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_flax_bert.py | null | 7,083 |
class FlaxBertForCausalLMModule(nn.Module):
config: BertConfig
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self):
self.bert = FlaxBertModule(
config=self.config,
add_pooling_layer=False,
dtype=self.dtype,
gradient... | class_definition | 59,635 | 61,786 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_flax_bert.py | null | 7,084 |
class FlaxBertForCausalLM(FlaxBertPreTrainedModel):
module_class = FlaxBertForCausalLMModule
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_values = s... | class_definition | 61,995 | 63,523 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_flax_bert.py | null | 7,085 |
class BertEmbeddings(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 | 5,755 | 8,928 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_bert.py | null | 7,086 |
class BertSelfAttention(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 | 8,931 | 16,273 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_bert.py | null | 7,087 |
class BertSdpaSelfAttention(BertSelfAttention):
def __init__(self, config, position_embedding_type=None):
super().__init__(config, position_embedding_type=position_embedding_type)
self.dropout_prob = config.attention_probs_dropout_prob
self.require_contiguous_qkv = version.parse(get_torch_ve... | class_definition | 16,276 | 21,889 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_bert.py | null | 7,088 |
class BertSelfOutput(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 | 21,892 | 22,498 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_bert.py | null | 7,089 |
class BertAttention(nn.Module):
def __init__(self, config, position_embedding_type=None):
super().__init__()
self.self = BERT_SELF_ATTENTION_CLASSES[config._attn_implementation](
config, position_embedding_type=position_embedding_type
)
self.output = BertSelfOutput(config... | class_definition | 22,604 | 24,726 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_bert.py | null | 7,090 |
class BertIntermediate(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 | 24,729 | 25,294 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_bert.py | null | 7,091 |
class BertOutput(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 | 25,297 | 25,905 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_bert.py | null | 7,092 |
class BertLayer(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 = BertAttention(config)
self.is_decoder = config.is_decoder
self.add_cross_attention = config.a... | class_definition | 25,908 | 29,815 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_bert.py | null | 7,093 |
class BertEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([BertLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
hidden_states: torch.... | class_definition | 29,818 | 33,608 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_bert.py | null | 7,094 |
class BertPooler(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 hidde... | class_definition | 33,611 | 34,170 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_bert.py | null | 7,095 |
class BertPredictionHeadTransform(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 | 34,173 | 34,873 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_bert.py | null | 7,096 |
class BertLMPredictionHead(nn.Module):
def __init__(self, config):
super().__init__()
self.transform = BertPredictionHeadTransform(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 | 34,876 | 35,708 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_bert.py | null | 7,097 |
class BertOnlyMLMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = BertLMPredictionHead(config)
def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
prediction_scores = self.predictions(sequence_output)
return prediction_scores | class_definition | 35,711 | 36,025 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_bert.py | null | 7,098 |
class BertOnlyNSPHead(nn.Module):
def __init__(self, config):
super().__init__()
self.seq_relationship = nn.Linear(config.hidden_size, 2)
def forward(self, pooled_output):
seq_relationship_score = self.seq_relationship(pooled_output)
return seq_relationship_score | class_definition | 36,028 | 36,332 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_bert.py | null | 7,099 |
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