text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
# 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... | 9,782 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
# Contextualize character embeddings using shallow Transformer.
# We use a 3D attention mask for the local attention.
# `input_char_encoding`: shape (batch_size, char_seq_len, char_dim)
char_attention_mask = self._create_3d_attention_mask_from_input_mask(
input_ids if input_ids is no... | 9,782 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
# Downsample chars to molecules.
# The following lines have dimensions: [batch, molecule_seq, molecule_dim].
# In this transformation, we change the dimensionality from `char_dim` to
# `molecule_dim`, but do *NOT* add a resnet connection. Instead, we rely on
# the resnet connections (a) ... | 9,782 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
init_molecule_encoding = self.chars_to_molecules(input_char_encoding) | 9,782 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
# Deep BERT encoder
# `molecule_sequence_output`: shape (batch_size, mol_seq_len, mol_dim)
encoder_outputs = self.encoder(
init_molecule_encoding,
attention_mask=extended_molecule_attention_mask,
head_mask=head_mask,
output_attentions=output_attentions,
... | 9,782 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
# Concatenate representations (contextualized char embeddings and repeated molecules):
# `concat`: shape [batch_size, char_seq_len, molecule_hidden_size+char_hidden_final]
concat = torch.cat([input_char_encoding, repeated_molecules], dim=-1)
# Project representation dimension back to hidden_siz... | 9,782 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
if output_hidden_states:
deep_encoder_hidden_states = encoder_outputs.hidden_states if return_dict else encoder_outputs[1]
all_hidden_states = (
all_hidden_states
+ init_chars_encoder_outputs.hidden_states
+ deep_encoder_hidden_states
... | 9,782 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
return CanineModelOutputWithPooling(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
) | 9,782 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
class CanineForSequenceClassification(CaninePreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.canine = CanineModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.h... | 9,783 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
@add_start_docstrings_to_model_forward(CANINE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=SequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optio... | 9,783 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
... | 9,783 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
outputs = self.canine(
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,
output_hidden_... | 9,783 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "singl... | 9,783 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
class CanineForMultipleChoice(CaninePreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.canine = CanineModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, 1)
# Initialize weights and app... | 9,784 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
@add_start_docstrings_to_model_forward(CANINE_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MultipleChoiceModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
in... | 9,784 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
`input_ids` above)
"""
return_dict = return_dict if return_dict is not None else self.co... | 9,784 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
... | 9,784 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
reshaped_logits = logits.view(-1, num_choices)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(reshaped_logits... | 9,784 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
class CanineForTokenClassification(CaninePreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.canine = CanineModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidd... | 9,785 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
@add_start_docstrings_to_model_forward(CANINE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torc... | 9,785 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. | 9,785 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, CanineForTokenClassification
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("google/canine-s")
>>> model = CanineForTokenClassification.from_pretrained("google/canine-s")
>... | 9,785 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
>>> # Note that tokens are classified rather then input words which means that
>>> # there might be more predicted token classes than words.
>>> # Multiple token classes might account for the same word
>>> predicted_tokens_classes = [model.config.id2label[t.item()] for t in predicted_token_class... | 9,785 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
outputs = self.canine(
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,
output_hidden_... | 9,785 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
) | 9,785 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
class CanineForQuestionAnswering(CaninePreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.canine = CanineModel(config)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights and ap... | 9,786 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
@add_start_docstrings_to_model_forward(CANINE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint="Splend1dchan/canine-c-squad",
output_type=QuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
expected_output="'nice puppet'",
... | 9,786 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
) -> Union[Tuple, QuestionAnsweringModelOutput]:
r"""
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the s... | 9,786 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
outputs = self.canine(
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,
output_hidden_... | 9,786 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
total_loss = None
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
... | 9,786 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
if not return_dict:
output = (start_logits, end_logits) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return QuestionAnsweringModelOutput(
loss=total_loss,
start_logits=start_logits,
end_logits=end_logits,
... | 9,786 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
class CanineTokenizer(PreTrainedTokenizer):
r"""
Construct a CANINE tokenizer (i.e. a character splitter). It turns text into a sequence of characters, and then
converts each character into its Unicode code point.
[`CanineTokenizer`] inherits from [`PreTrainedTokenizer`].
Refer to superclass [`Pre... | 9,787 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/tokenization_canine.py |
def __init__(
self,
bos_token=chr(CLS),
eos_token=chr(SEP),
sep_token=chr(SEP),
cls_token=chr(CLS),
pad_token=chr(PAD),
mask_token=chr(MASK),
add_prefix_space=False,
model_max_length=2048,
**kwargs,
):
bos_token = AddedToken(bos... | 9,787 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/tokenization_canine.py |
# Mask token behave like a normal word, i.e. include the space before it
mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
# Creates a mapping for looking up the IDs of special symbols.
self._special_codepoints: Dict[str, int] = {}
... | 9,787 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/tokenization_canine.py |
super().__init__(
bos_token=bos_token,
eos_token=eos_token,
sep_token=sep_token,
cls_token=cls_token,
pad_token=pad_token,
mask_token=mask_token,
add_prefix_space=add_prefix_space,
model_max_length=model_max_length,
... | 9,787 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/tokenization_canine.py |
def _convert_token_to_id(self, token: str) -> int:
"""Converts a token (i.e. a Unicode character) in an id (i.e. its integer Unicode code point value)."""
try:
return ord(token)
except TypeError:
raise ValueError(f"invalid token: '{token}'")
def _convert_id_to_token(... | 9,787 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/tokenization_canine.py |
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A CANINE sequence has... | 9,787 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/tokenization_canine.py |
result = cls + token_ids_0 + sep
if token_ids_1 is not None:
result += token_ids_1 + sep
return result
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]:
"""
... | 9,787 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/tokenization_canine.py |
Returns:
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
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=Tru... | 9,787 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/tokenization_canine.py |
If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`Li... | 9,787 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/tokenization_canine.py |
class TFAttention(keras.layers.Layer):
def __init__(self, nx, config, scale=False, **kwargs):
super().__init__(**kwargs)
n_state = nx # in Attention: n_state=768 (nx=n_embd)
# [switch nx => n_state from Block to Attention to keep identical to TF implementation]
assert (
... | 9,788 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.py |
def prune_heads(self, heads):
pass
@staticmethod
def causal_attention_mask(nd, ns):
"""
1's in the lower triangle, counting from the lower right corner. Same as tf.matrix_band_part(tf.ones([nd, ns]),
-1, ns-nd), but doesn't produce garbage on TPUs.
"""
i = tf.ran... | 9,788 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.py |
# w has shape [batch, heads, dst_sequence, src_sequence], where information flows from src to dst.
_, _, nd, ns = shape_list(w)
b = tf.cast(self.causal_attention_mask(nd, ns), dtype=w.dtype)
b = tf.reshape(b, [1, 1, nd, ns])
w = w * b - 1e4 * (1 - b)
if attention_mask is not Non... | 9,788 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.py |
def split_heads(self, x):
x_shape = shape_list(x)
new_x_shape = x_shape[:-1] + [self.n_head, x_shape[-1] // self.n_head]
x = tf.reshape(x, new_x_shape)
return tf.transpose(x, (0, 2, 1, 3)) # (batch, head, seq_length, head_features)
def call(self, x, attention_mask, head_mask, outpu... | 9,788 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "c_attn", None) is not None:
with tf.name_scope(self.c_attn.name):
self.c_attn.build([None, None, self.n_state * 3])
if getattr(self, "c_proj", None) is not... | 9,788 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.py |
class TFMLP(keras.layers.Layer):
def __init__(self, n_state, config, **kwargs):
super().__init__(**kwargs)
nx = config.n_embd
self.c_fc = TFConv1D(n_state, nx, initializer_range=config.initializer_range, name="c_fc")
self.c_proj = TFConv1D(nx, n_state, initializer_range=config.initia... | 9,789 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "c_fc", None) is not None:
with tf.name_scope(self.c_fc.name):
self.c_fc.build([None, None, self.n_state])
if getattr(self, "c_proj", None) is not None:
... | 9,789 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.py |
class TFBlock(keras.layers.Layer):
def __init__(self, config, scale=False, **kwargs):
super().__init__(**kwargs)
nx = config.n_embd
self.attn = TFAttention(nx, config, scale, name="attn")
self.ln_1 = keras.layers.LayerNormalization(epsilon=config.layer_norm_epsilon, name="ln_1")
... | 9,790 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "attn", None) is not None:
with tf.name_scope(self.attn.name):
self.attn.build(None)
if getattr(self, "ln_1", None) is not None:
with tf.name_sc... | 9,790 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.py |
class TFOpenAIGPTMainLayer(keras.layers.Layer):
config_class = OpenAIGPTConfig
def __init__(self, config, *inputs, **kwargs):
super().__init__(*inputs, **kwargs)
self.config = config
self.output_hidden_states = config.output_hidden_states
self.output_attentions = config.output_... | 9,791 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.py |
def build(self, input_shape=None):
with tf.name_scope("positions_embed"):
self.positions_embed = self.add_weight(
name="embeddings",
shape=[self.n_positions, self.n_embd],
initializer=get_initializer(self.initializer_range),
)
if s... | 9,791 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.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}
"""
raise NotImplementedError | 9,791 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.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 ... | 9,791 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.py |
input_shape = shape_list(inputs_embeds)[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds") | 9,791 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.py |
if position_ids is None:
position_ids = tf.expand_dims(tf.range(input_shape[-1]), axis=0)
if attention_mask is not None:
# We create a 3D attention mask from a 2D tensor mask.
# Sizes are [batch_size, 1, 1, to_seq_length]
# So we can broadcast to [batch_size, num... | 9,791 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.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
... | 9,791 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.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... | 9,791 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.py |
if inputs_embeds is None:
check_embeddings_within_bounds(input_ids, self.config.vocab_size)
inputs_embeds = self.tokens_embed(input_ids, mode="embedding")
position_embeds = tf.gather(self.positions_embed, position_ids)
if token_type_ids is not None:
token_type_ids = t... | 9,791 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.py |
all_attentions = () if output_attentions else None
all_hidden_states = () if output_hidden_states else None
for i, block in enumerate(self.h):
if output_hidden_states:
all_hidden_states = all_hidden_states + (tf.reshape(hidden_states, output_shape),)
outputs = bl... | 9,791 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.py |
if output_attentions:
# let the number of heads free (-1) so we can extract attention even after head pruning
attention_output_shape = input_shape[:-1] + [-1] + shape_list(all_attentions[0])[-2:]
all_attentions = tuple(tf.reshape(t, attention_output_shape) for t in all_attentions)
... | 9,791 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.py |
class TFOpenAIGPTPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = OpenAIGPTConfig
base_model_prefix = "transformer" | 9,792 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.py |
class TFOpenAIGPTDoubleHeadsModelOutput(ModelOutput):
"""
Base class for outputs of models predicting if two sentences are consecutive or not.
Args:
logits (`tf.Tensor` of shape `(batch_size, num_choices, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling ... | 9,793 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.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... | 9,793 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.py |
class TFOpenAIGPTModel(TFOpenAIGPTPreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.transformer = TFOpenAIGPTMainLayer(config, name="transformer") | 9,794 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(OPENAI_GPT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFBaseModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids: TFModelInputType | None = None... | 9,794 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.py |
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
return outputs | 9,794 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "transformer", None) is not None:
with tf.name_scope(self.transformer.name):
self.transformer.build(None) | 9,794 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.py |
class TFOpenAIGPTLMHeadModel(TFOpenAIGPTPreTrainedModel, TFCausalLanguageModelingLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.transformer = TFOpenAIGPTMainLayer(config, name="transformer")
# OpenAIGPT does not have past caching featur... | 9,795 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(OPENAI_GPT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFCausalLMOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids: TFModelInputType | None = None,... | 9,795 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.py |
Labels for computing the cross entropy classification loss. Indices should be in `[0, ...,
config.vocab_size - 1]`.
""" | 9,795 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.py |
transformer_outputs = self.transformer(
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,... | 9,795 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.py |
return TFCausalLMOutput(
loss=loss,
logits=logits,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
def prepare_inputs_for_generation(self, inputs, **kwargs):
return {"input_ids": inputs}
def build(sel... | 9,795 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.py |
class TFOpenAIGPTDoubleHeadsModel(TFOpenAIGPTPreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
config.num_labels = 1
self.transformer = TFOpenAIGPTMainLayer(config, name="transformer")
self.multiple_choice_head = TFSequenceSu... | 9,796 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(OPENAI_GPT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=TFOpenAIGPTDoubleHeadsModelOutput, config_class=_CONFIG_FOR_DOC)
def call(
self,
input_ids: TFModelInputType | None = None,
attention_mask: np.ndarray | tf.Tensor... | 9,796 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.py |
mc_token_ids (`tf.Tensor` or `Numpy array` of shape `(batch_size, num_choices)`, *optional*, default to index of the last token of the input):
Index of the classification token in each input sequence. Selected in the range `[0, input_ids.size(-1) -
1]`. | 9,796 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.py |
Return:
Examples:
```python
>>> import tensorflow as tf
>>> from transformers import AutoTokenizer, TFOpenAIGPTDoubleHeadsModel
>>> tokenizer = AutoTokenizer.from_pretrained("openai-community/openai-gpt")
>>> model = TFOpenAIGPTDoubleHeadsModel.from_pretrained("openai-... | 9,796 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.py |
>>> choices = ["Hello, my dog is cute [CLS]", "Hello, my cat is cute [CLS]"]
>>> encoding = tokenizer(choices, return_tensors="tf")
>>> inputs = {k: tf.expand_dims(v, 0) for k, v in encoding.items()}
>>> inputs["mc_token_ids"] = tf.constant(
... [inputs["input_ids"].shape[-1] - 1, in... | 9,796 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.py |
seq_length = input_shapes[-1]
flat_input_ids = tf.reshape(input_ids, (-1, seq_length)) if input_ids is not None else None
flat_attention_mask = tf.reshape(attention_mask, (-1, seq_length)) if attention_mask is not None else None
flat_token_type_ids = tf.reshape(token_type_ids, (-1, seq_length)) ... | 9,796 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.py |
if return_dict and output_hidden_states:
# We do this to match the slightly odd PT behaviour - the final hidden state is reshaped to rank 4 when the
# input is rank 3, but all other hidden states remain at rank-3 (with the first 2 dims merged)
all_hidden_states = transformer_outputs.... | 9,796 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.py |
if not return_dict:
return (lm_logits, mc_logits) + transformer_outputs[1:]
return TFOpenAIGPTDoubleHeadsModelOutput(
logits=lm_logits,
mc_logits=mc_logits,
hidden_states=all_hidden_states,
attentions=transformer_outputs.attentions,
)
@pr... | 9,796 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "transformer", None) is not None:
with tf.name_scope(self.transformer.name):
self.transformer.build(None)
if getattr(self, "multiple_choice_head", None) is ... | 9,796 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.py |
class TFOpenAIGPTForSequenceClassification(TFOpenAIGPTPreTrainedModel, TFSequenceClassificationLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.score = keras.layers.Dense(
config.num_labels,... | 9,797 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(OPENAI_GPT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFSequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
input_ids: TFModelInputType | No... | 9,797 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.py |
labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the cross entropy classification loss. Indices should be in `[0, ...,
config.vocab_size - 1]`.
"""
transformer_outputs = self.transformer(
input_ids=input_ids,
... | 9,797 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.py |
hidden_states = transformer_outputs[0]
logits = self.score(hidden_states)
in_logits = None
if self.config.pad_token_id is None:
sequence_lengths = -1
else:
if input_ids is not None:
sequence_lengths = (
tf.argmax(tf.cast(tf.math... | 9,797 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.py |
if labels is not None:
if input_ids is not None:
batch_size, sequence_length = shape_list(input_ids)[:2]
else:
batch_size, sequence_length = shape_list(inputs_embeds)[:2]
assert (
self.config.pad_token_id is not None or batch_size == 1
... | 9,797 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.py |
return TFSequenceClassifierOutput(
loss=loss,
logits=pooled_logits,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
def build(self, input_shape=None):
if self.built:
return
self.built =... | 9,797 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_tf_openai.py |
class BasicTokenizer:
"""
Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.).
Args:
do_lower_case (`bool`, *optional*, defaults to `True`):
Whether or not to lowercase the input when tokenizing.
never_split (`Iterable`, *opti... | 9,798 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/tokenization_openai.py |
This should likely be deactivated for Japanese (see this
[issue](https://github.com/huggingface/transformers/issues/328)).
strip_accents (`bool`, *optional*):
Whether or not to strip all accents. If this option is not specified, then it will be determined by the
value for `lo... | 9,798 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/tokenization_openai.py |
def __init__(
self,
do_lower_case=True,
never_split=None,
tokenize_chinese_chars=True,
strip_accents=None,
do_split_on_punc=True,
):
if never_split is None:
never_split = []
self.do_lower_case = do_lower_case
self.never_split = set(... | 9,798 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/tokenization_openai.py |
Args:
never_split (`List[str]`, *optional*)
Kept for backward compatibility purposes. Now implemented directly at the base class level (see
[`PreTrainedTokenizer.tokenize`]) List of token not to split.
"""
# union() returns a new set by concatenating the two s... | 9,798 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/tokenization_openai.py |
# This was added on November 1st, 2018 for the multilingual and Chinese
# models. This is also applied to the English models now, but it doesn't
# matter since the English models were not trained on any Chinese data
# and generally don't have any Chinese data in them (there are Chinese
#... | 9,798 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/tokenization_openai.py |
token = self._run_strip_accents(token)
elif self.strip_accents:
token = self._run_strip_accents(token)
split_tokens.extend(self._run_split_on_punc(token, never_split)) | 9,798 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/tokenization_openai.py |
output_tokens = whitespace_tokenize(" ".join(split_tokens))
return output_tokens
def _run_strip_accents(self, text):
"""Strips accents from a piece of text."""
text = unicodedata.normalize("NFD", text)
output = []
for char in text:
cat = unicodedata.category(char... | 9,798 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/tokenization_openai.py |
def _run_split_on_punc(self, text, never_split=None):
"""Splits punctuation on a piece of text."""
if not self.do_split_on_punc or (never_split is not None and text in never_split):
return [text]
chars = list(text)
i = 0
start_new_word = True
output = []
... | 9,798 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/tokenization_openai.py |
def _tokenize_chinese_chars(self, text):
"""Adds whitespace around any CJK character."""
output = []
for char in text:
cp = ord(char)
if self._is_chinese_char(cp):
output.append(" ")
output.append(char)
output.append(" ")
... | 9,798 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/tokenization_openai.py |
def _is_chinese_char(self, cp):
"""Checks whether CP is the codepoint of a CJK character."""
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is ... | 9,798 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/tokenization_openai.py |
or (cp >= 0x2F800 and cp <= 0x2FA1F) #
): #
return True | 9,798 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/tokenization_openai.py |
return False
def _clean_text(self, text):
"""Performs invalid character removal and whitespace cleanup on text."""
output = []
for char in text:
cp = ord(char)
if cp == 0 or cp == 0xFFFD or _is_control(char):
continue
if _is_whitespace(cha... | 9,798 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/tokenization_openai.py |
class OpenAIGPTTokenizer(PreTrainedTokenizer):
"""
Construct a GPT Tokenizer. Based on Byte-Pair-Encoding with the following peculiarities:
- lowercases all inputs,
- uses `SpaCy` tokenizer and `ftfy` for pre-BPE tokenization if they are installed, fallback to BERT's
`BasicTokenizer` if not.
... | 9,799 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/tokenization_openai.py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.