text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
def get_special_tokens_mask(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
) -> List[int]:
"""
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens ... | 3,184 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/tokenization_albert.py |
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
)
if token_ids_1 is not None:
return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]... | 3,184 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/tokenization_albert.py |
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).... | 3,184 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/tokenization_albert.py |
if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
copyfile(self.vocab_file, out_vocab_file)
elif not os.path.isfile(self.vocab_file):
with open(out_vocab_file, "wb") as fi:
content_spiece_model = self.sp_model.seri... | 3,184 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/tokenization_albert.py |
class TFAlbertPreTrainingLoss:
"""
Loss function suitable for ALBERT pretraining, that is, the task of pretraining a language model by combining SOP +
MLM. .. note:: Any label of -100 will be ignored (along with the corresponding logits) in the loss computation.
""" | 3,185 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
def hf_compute_loss(self, labels: tf.Tensor, logits: tf.Tensor) -> tf.Tensor:
loss_fn = keras.losses.SparseCategoricalCrossentropy(from_logits=True, reduction=keras.losses.Reduction.NONE)
if self.config.tf_legacy_loss:
# make sure only labels that are not equal to -100
# are take... | 3,185 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
sentence_order_reduced_logits = tf.boolean_mask(
tensor=tf.reshape(tensor=logits[1], shape=(-1, 2)), mask=sentence_order_active_loss
)
sentence_order_label = tf.boolean_mask(
tensor=tf.reshape(tensor=labels["sentence_order_label"], shape=(-1,)), mask=sentence_orde... | 3,185 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
return masked_lm_loss + sentence_order_loss
# Clip negative labels to zero here to avoid NaNs and errors - those positions will get masked later anyway
unmasked_lm_losses = loss_fn(y_true=tf.nn.relu(labels["labels"]), y_pred=logits[0])
# make sure only labels that are not equal to -100
... | 3,185 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
masked_sop_loss = unmasked_sop_loss * sop_loss_mask
reduced_masked_sop_loss = tf.reduce_sum(masked_sop_loss) / tf.reduce_sum(sop_loss_mask)
return tf.reshape(reduced_masked_lm_loss + reduced_masked_sop_loss, (1,)) | 3,185 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
class TFAlbertEmbeddings(keras.layers.Layer):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config: AlbertConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.embedding_size = config.embedding_size
self.max_p... | 3,186 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
with tf.name_scope("token_type_embeddings"):
self.token_type_embeddings = self.add_weight(
name="embeddings",
shape=[self.config.type_vocab_size, self.embedding_size],
initializer=get_initializer(self.initializer_range),
)
with tf.name_sco... | 3,186 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
# Copied from transformers.models.bert.modeling_tf_bert.TFBertEmbeddings.call
def call(
self,
input_ids: tf.Tensor = None,
position_ids: tf.Tensor = None,
token_type_ids: tf.Tensor = None,
inputs_embeds: tf.Tensor = None,
past_key_values_length=0,
training: bo... | 3,186 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
if position_ids is None:
position_ids = tf.expand_dims(
tf.range(start=past_key_values_length, limit=input_shape[1] + past_key_values_length), axis=0
)
position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids)
token_type_embeds = tf.gathe... | 3,186 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
class TFAlbertAttention(keras.layers.Layer):
"""Contains the complete attention sublayer, including both dropouts and layer norm."""
def __init__(self, config: AlbertConfig, **kwargs):
super().__init__(**kwargs)
if config.hidden_size % config.num_attention_heads != 0:
raise ValueEr... | 3,187 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
self.query = keras.layers.Dense(
units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="query"
)
self.key = keras.layers.Dense(
units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="key"
)
... | 3,187 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
self.output_dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob)
self.config = config | 3,187 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
def transpose_for_scores(self, tensor: tf.Tensor, batch_size: int) -> tf.Tensor:
# Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size]
tensor = tf.reshape(tensor=tensor, shape=(batch_size, -1, self.num_attention_heads, self.attention... | 3,187 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
def call(
self,
input_tensor: tf.Tensor,
attention_mask: tf.Tensor,
head_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> Tuple[tf.Tensor]:
batch_size = shape_list(input_tensor)[0]
mixed_query_layer = self.query(inputs=input_tenso... | 3,187 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
# Take the dot product between "query" and "key" to get the raw attention scores.
# (batch size, num_heads, seq_len_q, seq_len_k)
attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True)
dk = tf.cast(self.sqrt_att_head_size, dtype=attention_scores.dtype)
attention_scores = ... | 3,187 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
# Mask heads if we want to
if head_mask is not None:
attention_probs = tf.multiply(attention_probs, head_mask)
context_layer = tf.matmul(attention_probs, value_layer)
context_layer = tf.transpose(context_layer, perm=[0, 2, 1, 3])
# (batch_size, seq_len_q, all_head_size)
... | 3,187 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "query", None) is not None:
with tf.name_scope(self.query.name):
self.query.build([None, None, self.config.hidden_size])
if getattr(self, "key", None) is no... | 3,187 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
class TFAlbertLayer(keras.layers.Layer):
def __init__(self, config: AlbertConfig, **kwargs):
super().__init__(**kwargs)
self.attention = TFAlbertAttention(config, name="attention")
self.ffn = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(... | 3,188 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
head_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> Tuple[tf.Tensor]:
attention_outputs = self.attention(
input_tensor=hidden_states,
attention... | 3,188 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "attention", None) is not None:
with tf.name_scope(self.attention.name):
self.attention.build(None)
if getattr(self, "ffn", None) is not None:
w... | 3,188 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
class TFAlbertLayerGroup(keras.layers.Layer):
def __init__(self, config: AlbertConfig, **kwargs):
super().__init__(**kwargs)
self.albert_layers = [
TFAlbertLayer(config, name=f"albert_layers_._{i}") for i in range(config.inner_group_num)
]
def call(
self,
hi... | 3,189 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
layer_output = albert_layer(
hidden_states=hidden_states,
attention_mask=attention_mask,
head_mask=head_mask[layer_index],
output_attentions=output_attentions,
training=training,
)
hidden_states = layer_output[0]
... | 3,189 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
class TFAlbertTransformer(keras.layers.Layer):
def __init__(self, config: AlbertConfig, **kwargs):
super().__init__(**kwargs)
self.num_hidden_layers = config.num_hidden_layers
self.num_hidden_groups = config.num_hidden_groups
# Number of layers in a hidden group
self.layers_... | 3,190 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
head_mask: tf.Tensor,
output_attentions: bool,
output_hidden_states: bool,
return_dict: bool,
training: bool = False,
) -> Union[TFBaseModelOutput, Tuple[tf.Tensor]]:
hidden_s... | 3,190 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
for i in range(self.num_hidden_layers):
# Index of the hidden group
group_idx = int(i / (self.num_hidden_layers / self.num_hidden_groups))
layer_group_output = self.albert_layer_groups[group_idx](
hidden_states=hidden_states,
attention_mask=attention_m... | 3,190 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
return TFBaseModelOutput(
last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "embedding_hidden_mapping_in", None) is not Non... | 3,190 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
class TFAlbertPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = AlbertConfig
base_model_prefix = "albert" | 3,191 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
class TFAlbertMLMHead(keras.layers.Layer):
def __init__(self, config: AlbertConfig, input_embeddings: keras.layers.Layer, **kwargs):
super().__init__(**kwargs)
self.config = config
self.embedding_size = config.embedding_size
self.dense = keras.layers.Dense(
config.embedd... | 3,192 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
def build(self, input_shape=None):
self.bias = self.add_weight(shape=(self.config.vocab_size,), initializer="zeros", trainable=True, name="bias")
self.decoder_bias = self.add_weight(
shape=(self.config.vocab_size,), initializer="zeros", trainable=True, name="decoder/bias"
)
... | 3,192 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
def get_bias(self) -> Dict[str, tf.Variable]:
return {"bias": self.bias, "decoder_bias": self.decoder_bias}
def set_bias(self, value: tf.Variable):
self.bias = value["bias"]
self.decoder_bias = value["decoder_bias"]
self.config.vocab_size = shape_list(value["bias"])[0]
def call... | 3,192 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
class TFAlbertMainLayer(keras.layers.Layer):
config_class = AlbertConfig
def __init__(self, config: AlbertConfig, add_pooling_layer: bool = True, **kwargs):
super().__init__(**kwargs)
self.config = config
self.embeddings = TFAlbertEmbeddings(config, name="embeddings")
self.enc... | 3,193 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
raise NotImplementedError | 3,193 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
@unpack_inputs
def call(
self,
input_ids: TFModelInputType | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
token_type_ids: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | None = None,
head_mask: np.ndarray | tf.Tensor ... | 3,193 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
raise ValueError("You have to specify either input_ids or inputs_embeds") | 3,193 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
if attention_mask is None:
attention_mask = tf.fill(dims=input_shape, value=1)
if token_type_ids is None:
token_type_ids = tf.fill(dims=input_shape, value=0)
embedding_output = self.embeddings(
input_ids=input_ids,
position_ids=position_ids,
... | 3,193 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
# masked positions, this operation will create a tensor which is 0.0 for
# positions we want to attend and -10000.0 for masked positions.
# Since we are adding it to the raw scores before the softmax, this is
# eff... | 3,193 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_lengt... | 3,193 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
if not return_dict:
return (
sequence_output,
pooled_output,
) + encoder_outputs[1:]
return TFBaseModelOutputWithPooling(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hid... | 3,193 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
class TFAlbertForPreTrainingOutput(ModelOutput):
"""
Output type of [`TFAlbertForPreTraining`].
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 Sof... | 3,194 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads... | 3,194 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
class TFAlbertModel(TFAlbertPreTrainedModel):
def __init__(self, config: AlbertConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.albert = TFAlbertMainLayer(config, name="albert") | 3,195 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFBaseModelOutputWithPooling,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
... | 3,195 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=retur... | 3,195 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
return outputs
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "albert", None) is not None:
with tf.name_scope(self.albert.name):
self.albert.build(None) | 3,195 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
class TFAlbertForPreTraining(TFAlbertPreTrainedModel, TFAlbertPreTrainingLoss):
# 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"predictions.decoder.weight"]
def __init__(self, config: AlbertConfig, *inp... | 3,196 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=TFAlbertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
def call(
self,
input_ids: TFModelInputType | None = None,
attention... | 3,196 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
Example:
```python
>>> import tensorflow as tf
>>> from transformers import AutoTokenizer, TFAlbertForPreTraining
>>> tokenizer = AutoTokenizer.from_pretrained("albert/albert-base-v2")
>>> model = TFAlbertForPreTraining.from_pretrained("albert/albert-base-v2")
>>> inpu... | 3,196 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
outputs = self.albert(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
outp... | 3,196 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
if not return_dict:
output = (prediction_scores, sop_scores) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return TFAlbertForPreTrainingOutput(
loss=total_loss,
prediction_logits=prediction_scores,
sop_logits=sop_... | 3,196 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
class TFAlbertSOPHead(keras.layers.Layer):
def __init__(self, config: AlbertConfig, **kwargs):
super().__init__(**kwargs)
self.dropout = keras.layers.Dropout(rate=config.classifier_dropout_prob)
self.classifier = keras.layers.Dense(
units=config.num_labels,
kernel_in... | 3,197 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
class TFAlbertForMaskedLM(TFAlbertPreTrainedModel, 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"predictions.decoder.weight"]
def __init__(self, config: Alber... | 3,198 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=TFMaskedLMOutput, config_class=_CONFIG_FOR_DOC)
def call(
self,
input_ids: TFModelInputType | None = None,
attention_mask: np.nd... | 3,198 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` | 3,198 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
Returns:
Example:
```python
>>> import tensorflow as tf
>>> from transformers import AutoTokenizer, TFAlbertForMaskedLM
>>> tokenizer = AutoTokenizer.from_pretrained("albert/albert-base-v2")
>>> model = TFAlbertForMaskedLM.from_pretrained("albert/albert-base-v2")
... | 3,198 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
```python
>>> labels = tokenizer("The capital of France is Paris.", return_tensors="tf")["input_ids"]
>>> labels = tf.where(inputs.input_ids == tokenizer.mask_token_id, labels, -100)
>>> outputs = model(**inputs, labels=labels)
>>> round(float(outputs.loss), 2)
0.81
```
... | 3,198 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
if not return_dict:
output = (prediction_scores,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFMaskedLMOutput(
loss=loss,
logits=prediction_scores,
hidden_states=outputs.hidden_states,
attentions=output... | 3,198 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
class TFAlbertForSequenceClassification(TFAlbertPreTrainedModel, 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"predictions"]
_keys_to_ignore_on_load_missing = [r"dropout"... | 3,199 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint="vumichien/albert-base-v2-imdb",
output_type=TFSequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
expected_output="'LA... | 3,199 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
) -> Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:
r"""
labels (`tf.Tensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regres... | 3,199 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
pooled_output = self.dropout(inputs=pooled_output, training=training)
logits = self.classifier(inputs=pooled_output)
loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=logits) | 3,199 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFSequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions... | 3,199 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
class TFAlbertForTokenClassification(TFAlbertPreTrainedModel, 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"predictions"]
_keys_to_ignore_on_load_missing = [r"dro... | 3,200 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
self.albert = TFAlbertMainLayer(config, add_pooling_layer=False, name="albert")
classifier_dropout_prob = (
config.classifier_dropout_prob
if config.classifier_dropout_prob is not None
else config.hidden_dropout_prob
)
self.dropout = keras.layers.Dropout(rate=... | 3,200 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFTokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
i... | 3,200 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
"""
outputs = self.albert(
input_ids=input_ids,
attention_mask=attention_mask,
... | 3,200 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFTokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
... | 3,200 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
class TFAlbertForQuestionAnswering(TFAlbertPreTrainedModel, 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"predictions"]
def __init__(self, config: AlbertConfig, *i... | 3,201 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint="vumichien/albert-base-v2-squad2",
output_type=TFQuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
qa_target_star... | 3,201 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
start_positions: np.ndarray | tf.Tensor | None = None,
end_positions: np.ndarray | tf.Tensor | None = None,
training: Optional[bool] = False,
) -> Union[TFQuestionAnsweringModelOutput, Tuple[tf.Tensor]]:
r"""
start_positions (`tf.Tensor` of shape `(batch_size,)`, *optional*):
... | 3,201 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
outputs = self.albert(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
outp... | 3,201 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
if start_positions is not None and end_positions is not None:
labels = {"start_position": start_positions}
labels["end_position"] = end_positions
loss = self.hf_compute_loss(labels=labels, logits=(start_logits, end_logits))
if not return_dict:
output = (start_log... | 3,201 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "albert", None) is not None:
with tf.name_scope(self.albert.name):
self.albert.build(None)
if getattr(self, "qa_outputs", None) is not None:
wit... | 3,201 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
class TFAlbertForMultipleChoice(TFAlbertPreTrainedModel, 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"pooler", r"predictions"]
_keys_to_ignore_on_load_missing = [r"dropout"]
... | 3,202 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFMultipleChoiceModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
... | 3,202 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
labels (`tf.Tensor` of shape `(batch_size,)`, *optional*):
Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices]`
where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above)
""" | 3,202 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
if input_ids is not None:
num_choices = shape_list(input_ids)[1]
seq_length = shape_list(input_ids)[2]
else:
num_choices = shape_list(inputs_embeds)[1]
seq_length = shape_list(inputs_embeds)[2] | 3,202 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
flat_input_ids = tf.reshape(input_ids, (-1, seq_length)) if input_ids is not None else None
flat_attention_mask = (
tf.reshape(tensor=attention_mask, shape=(-1, seq_length)) if attention_mask is not None else None
)
flat_token_type_ids = (
tf.reshape(tensor=token_type_ids... | 3,202 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
inputs_embeds=flat_inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
pooled_output = outputs[1]
pooled_output = self.dropout(inputs=pooled_output, training=t... | 3,202 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
if not return_dict:
output = (reshaped_logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFMultipleChoiceModelOutput(
loss=loss,
logits=reshaped_logits,
hidden_states=outputs.hidden_states,
attentions=... | 3,202 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py |
class VideoMAEFeatureExtractor(VideoMAEImageProcessor):
def __init__(self, *args, **kwargs) -> None:
warnings.warn(
"The class VideoMAEFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
" Please use VideoMAEImageProcessor instead.",
FutureWa... | 3,203 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/feature_extraction_videomae.py |
class VideoMAEImageProcessor(BaseImageProcessor):
r"""
Constructs a VideoMAE image processor. | 3,204 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/image_processing_videomae.py |
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 the
`do_resize` parameter in the `preprocess` method.
size (`Dict[str, int]` *optional*, defaults to `{"shortest_edge":... | 3,204 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/image_processing_videomae.py |
parameter in the `preprocess` method.
crop_size (`Dict[str, int]`, *optional*, defaults to `{"height": 224, "width": 224}`):
Size of the image after applying the center crop. Can be overridden by the `crop_size` parameter in the
`preprocess` method.
do_rescale (`bool`, *optional*... | 3,204 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/image_processing_videomae.py |
image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`):
Mean to use if normalizing the image. This is a float or list of floats the length of the number of
channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method.
... | 3,204 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/image_processing_videomae.py |
model_input_names = ["pixel_values"]
def __init__(
self,
do_resize: bool = True,
size: Dict[str, int] = None,
resample: PILImageResampling = PILImageResampling.BILINEAR,
do_center_crop: bool = True,
crop_size: Dict[str, int] = None,
do_rescale: bool = True,
... | 3,204 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/image_processing_videomae.py |
self.do_resize = do_resize
self.size = size
self.do_center_crop = do_center_crop
self.crop_size = crop_size
self.resample = resample
self.do_rescale = do_rescale
self.rescale_factor = rescale_factor
self.do_normalize = do_normalize
self.image_mean = image_... | 3,204 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/image_processing_videomae.py |
Args:
image (`np.ndarray`):
Image to resize.
size (`Dict[str, int]`):
Size of the output image. If `size` is of the form `{"height": h, "width": w}`, the output image will
have the size `(h, w)`. If `size` is of the form `{"shortest_edge": s}`, the... | 3,204 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/image_processing_videomae.py |
size = get_size_dict(size, default_to_square=False)
if "shortest_edge" in size:
output_size = get_resize_output_image_size(
image, size["shortest_edge"], default_to_square=False, input_data_format=input_data_format
)
elif "height" in size and "width" in size:
... | 3,204 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/image_processing_videomae.py |
def _preprocess_image(
self,
image: ImageInput,
do_resize: bool = None,
size: Dict[str, int] = None,
resample: PILImageResampling = None,
do_center_crop: bool = None,
crop_size: Dict[str, int] = None,
do_rescale: bool = None,
rescale_factor: float ... | 3,204 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/image_processing_videomae.py |
do_resize=do_resize,
size=size,
resample=resample,
) | 3,204 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/image_processing_videomae.py |
# All transformations expect numpy arrays.
image = to_numpy_array(image)
if do_rescale and is_scaled_image(image):
logger.warning_once(
"It looks like you are trying to rescale already rescaled images. If the input"
" images have pixel values between 0 and 1,... | 3,204 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/image_processing_videomae.py |
if do_normalize:
image = self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=input_data_format)
image = to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format)
return image | 3,204 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/image_processing_videomae.py |
@filter_out_non_signature_kwargs()
def preprocess(
self,
videos: ImageInput,
do_resize: bool = None,
size: Dict[str, int] = None,
resample: PILImageResampling = None,
do_center_crop: bool = None,
crop_size: Dict[str, int] = None,
do_rescale: bool = Non... | 3,204 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/image_processing_videomae.py |
Args:
images (`ImageInput`):
Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
passing in images with pixel values between 0 and 1, set `do_rescale=False`.
do_resize (`bool`, *optional*, defaults to `self.do_resiz... | 3,204 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/image_processing_videomae.py |
Size of the image after applying the centre crop.
do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
Whether to rescale the image values between [0 - 1].
rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
Rescale factor to rescal... | 3,204 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/image_processing_videomae.py |
- `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
- `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
- `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
- `TensorType.JAX` or `'jax'`: Return a batch... | 3,204 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/image_processing_videomae.py |
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