text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
```python
>>> labels = tokenizer("The capital of France is Paris.", return_tensors="pt")["input_ids"]
>>> labels = torch.where(inputs.input_ids == tokenizer.mask_token_id, labels, -100)
>>> outputs = model(**inputs, labels=labels)
>>> round(outputs.loss.item(), 2)
0.81
``... | 3,153 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
masked_lm_loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
if not return_dict:
output = (prediction_scores,) + outputs[2:]
return ((masked_lm_lo... | 3,153 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
class AlbertForSequenceClassification(AlbertPreTrainedModel):
def __init__(self, config: AlbertConfig):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.albert = AlbertModel(config)
self.dropout = nn.Dropout(config.classifier_dropout_pro... | 3,154 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
@add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint="textattack/albert-base-v2-imdb",
output_type=SequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
expected_output="'LABEL_1'",
exp... | 3,154 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
labels (`torch.LongTensor` 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 regression loss is computed (Mean-Square loss), If
`config.num_lab... | 3,154 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_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... | 3,154 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
sel... | 3,154 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.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... | 3,154 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
class AlbertForTokenClassification(AlbertPreTrainedModel):
def __init__(self, config: AlbertConfig):
super().__init__(config)
self.num_labels = config.num_labels
self.albert = AlbertModel(config, add_pooling_layer=False)
classifier_dropout_prob = (
config.classifier_drop... | 3,155 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
@add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional... | 3,155 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 3,155 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
outputs = self.albert(
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_... | 3,155 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
) | 3,155 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
class AlbertForQuestionAnswering(AlbertPreTrainedModel):
def __init__(self, config: AlbertConfig):
super().__init__(config)
self.num_labels = config.num_labels
self.albert = AlbertModel(config, add_pooling_layer=False)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_label... | 3,156 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
@add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint="twmkn9/albert-base-v2-squad2",
output_type=QuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
qa_target_start_index=12,
qa_t... | 3,156 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
return_dict: Optional[bool] = None,
) -> Union[AlbertForPreTrainingOutput, Tuple]:
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.
Positi... | 3,156 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_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... | 3,156 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.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:
... | 3,156 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.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,
... | 3,156 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
class AlbertForMultipleChoice(AlbertPreTrainedModel):
def __init__(self, config: AlbertConfig):
super().__init__(config)
self.albert = AlbertModel(config)
self.dropout = nn.Dropout(config.classifier_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, 1)
# Initiali... | 3,157 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
@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=MultipleChoiceModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
in... | 3,157 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.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... | 3,157 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.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
... | 3,157 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
logits: torch.Tensor = self.classifier(pooled_output)
reshaped_logits = logits.view(-1, num_choices)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(r... | 3,157 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
class AlbertTokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "fast" ALBERT tokenizer (backed by HuggingFace's *tokenizers* library). Based on
[Unigram](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=unigram#models). This
tokenizer inherits from [`PreTrainedTokenize... | 3,158 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/tokenization_albert_fast.py |
Args:
vocab_file (`str`):
[SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that
contains the vocabulary necessary to instantiate a tokenizer.
do_lower_case (`bool`, *optional*, defaults to `True`):
Whether or not to lowe... | 3,158 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/tokenization_albert_fast.py |
When building a sequence using special tokens, this is not the token that is used for the beginning of
sequence. The token used is the `cls_token`.
</Tip> | 3,158 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/tokenization_albert_fast.py |
eos_token (`str`, *optional*, defaults to `"[SEP]"`):
The end of sequence token. .. note:: When building a sequence using special tokens, this is not the token
that is used for the end of sequence. The token used is the `sep_token`.
unk_token (`str`, *optional*, defaults to `"<unk>"`):
... | 3,158 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/tokenization_albert_fast.py |
cls_token (`str`, *optional*, defaults to `"[CLS]"`):
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
mask_token (`str`,... | 3,158 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/tokenization_albert_fast.py |
vocab_files_names = VOCAB_FILES_NAMES
slow_tokenizer_class = AlbertTokenizer
def __init__(
self,
vocab_file=None,
tokenizer_file=None,
do_lower_case=True,
remove_space=True,
keep_accents=False,
bos_token="[CLS]",
eos_token="[SEP]",
unk_tok... | 3,158 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/tokenization_albert_fast.py |
super().__init__(
vocab_file,
tokenizer_file=tokenizer_file,
do_lower_case=do_lower_case,
remove_space=remove_space,
keep_accents=keep_accents,
bos_token=bos_token,
eos_token=eos_token,
unk_token=unk_token,
sep_t... | 3,158 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/tokenization_albert_fast.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. An ALBERT sequence ha... | 3,158 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/tokenization_albert_fast.py |
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT
sequence pair mask has the following format:
... | 3,158 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/tokenization_albert_fast.py |
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if not self.can_save_slow_tokenizer:
... | 3,158 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/tokenization_albert_fast.py |
class FlaxAlbertForPreTrainingOutput(ModelOutput):
"""
Output type of [`FlaxAlbertForPreTraining`].
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 befo... | 3,159 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_h... | 3,159 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py |
class FlaxAlbertEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
config: AlbertConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation | 3,160 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py |
def setup(self):
self.word_embeddings = nn.Embed(
self.config.vocab_size,
self.config.embedding_size,
embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),
)
self.position_embeddings = nn.Embed(
self.config.max_position_e... | 3,160 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py |
def __call__(self, input_ids, token_type_ids, position_ids, deterministic: bool = True):
# Embed
inputs_embeds = self.word_embeddings(input_ids.astype("i4"))
position_embeds = self.position_embeddings(position_ids.astype("i4"))
token_type_embeddings = self.token_type_embeddings(token_typ... | 3,160 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py |
class FlaxAlbertSelfAttention(nn.Module):
config: AlbertConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
if self.config.hidden_size % self.config.num_attention_heads != 0:
raise ValueError(
"`config.hidden_size`: {self.config.hidden_... | 3,161 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py |
self.query = nn.Dense(
self.config.hidden_size,
dtype=self.dtype,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
)
self.key = nn.Dense(
self.config.hidden_size,
dtype=self.dtype,
kernel_init=jax.nn.initialize... | 3,161 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py |
def __call__(self, hidden_states, attention_mask, deterministic=True, output_attentions: bool = False):
head_dim = self.config.hidden_size // self.config.num_attention_heads
query_states = self.query(hidden_states).reshape(
hidden_states.shape[:2] + (self.config.num_attention_heads, head_di... | 3,161 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py |
# Convert the boolean attention mask to an attention bias.
if attention_mask is not None:
# attention mask in the form of attention bias
attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2))
attention_bias = lax.select(
attention_mask > 0,
... | 3,161 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py |
attn_weights = dot_product_attention_weights(
query_states,
key_states,
bias=attention_bias,
dropout_rng=dropout_rng,
dropout_rate=self.config.attention_probs_dropout_prob,
broadcast_dropout=True,
deterministic=deterministic,
... | 3,161 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py |
class FlaxAlbertLayer(nn.Module):
config: AlbertConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.attention = FlaxAlbertSelfAttention(self.config, dtype=self.dtype)
self.ffn = nn.Dense(
self.config.intermediate_size,
kernel_i... | 3,162 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py |
def __call__(
self,
hidden_states,
attention_mask,
deterministic: bool = True,
output_attentions: bool = False,
):
attention_outputs = self.attention(
hidden_states, attention_mask, deterministic=deterministic, output_attentions=output_attentions
)... | 3,162 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py |
class FlaxAlbertLayerCollection(nn.Module):
config: AlbertConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.layers = [
FlaxAlbertLayer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.inner_group_num)
]
def __c... | 3,163 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py |
if output_hidden_states:
layer_hidden_states = layer_hidden_states + (hidden_states,)
outputs = (hidden_states,)
if output_hidden_states:
outputs = outputs + (layer_hidden_states,)
if output_attentions:
outputs = outputs + (layer_attentions,)
retu... | 3,163 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py |
class FlaxAlbertLayerCollections(nn.Module):
config: AlbertConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
layer_index: Optional[str] = None
def setup(self):
self.albert_layers = FlaxAlbertLayerCollection(self.config, dtype=self.dtype)
def __call__(
self,
... | 3,164 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py |
class FlaxAlbertLayerGroups(nn.Module):
config: AlbertConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.layers = [
FlaxAlbertLayerCollections(self.config, name=str(i), layer_index=str(i), dtype=self.dtype)
for i in range(self.config.... | 3,165 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py |
for i in range(self.config.num_hidden_layers):
# Index of the hidden group
group_idx = int(i / (self.config.num_hidden_layers / self.config.num_hidden_groups))
layer_group_output = self.layers[group_idx](
hidden_states,
attention_mask,
... | 3,165 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py |
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)
return FlaxBaseModelOutput(
last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
) | 3,165 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py |
class FlaxAlbertEncoder(nn.Module):
config: AlbertConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.embedding_hidden_mapping_in = nn.Dense(
self.config.hidden_size,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range... | 3,166 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py |
class FlaxAlbertOnlyMLMHead(nn.Module):
config: AlbertConfig
dtype: jnp.dtype = jnp.float32
bias_init: Callable[..., np.ndarray] = jax.nn.initializers.zeros
def setup(self):
self.dense = nn.Dense(self.config.embedding_size, dtype=self.dtype)
self.activation = ACT2FN[self.config.hidden_a... | 3,167 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py |
if shared_embedding is not None:
hidden_states = self.decoder.apply({"params": {"kernel": shared_embedding.T}}, hidden_states)
else:
hidden_states = self.decoder(hidden_states)
hidden_states += self.bias
return hidden_states | 3,167 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py |
class FlaxAlbertSOPHead(nn.Module):
config: AlbertConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.dropout = nn.Dropout(self.config.classifier_dropout_prob)
self.classifier = nn.Dense(2, dtype=self.dtype)
def __call__(self, pooled_output, deterministic=True):
pooled_... | 3,168 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py |
class FlaxAlbertPreTrainedModel(FlaxPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = AlbertConfig
base_model_prefix = "albert"
module_class: nn.Module = None
def __init__(
... | 3,169 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py |
def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict:
# init input tensors
input_ids = jnp.zeros(input_shape, dtype="i4")
token_type_ids = jnp.zeros_like(input_ids)
position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids... | 3,169 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py |
if params is not None:
random_params = flatten_dict(unfreeze(random_params))
params = flatten_dict(unfreeze(params))
for missing_key in self._missing_keys:
params[missing_key] = random_params[missing_key]
self._missing_keys = set()
return freez... | 3,169 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py |
@add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
def __call__(
self,
input_ids,
attention_mask=None,
token_type_ids=None,
position_ids=None,
params: dict = None,
dropout_rng: jax.random.PRNGKey = None,
... | 3,169 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py |
if position_ids is None:
position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape)
if attention_mask is None:
attention_mask = jnp.ones_like(input_ids)
# Handle any PRNG if needed
rngs = {}
if dropout_rng is not None:
... | 3,169 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py |
class FlaxAlbertModule(nn.Module):
config: AlbertConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
add_pooling_layer: bool = True
def setup(self):
self.embeddings = FlaxAlbertEmbeddings(self.config, dtype=self.dtype)
self.encoder = FlaxAlbertEncoder(self.config, dtyp... | 3,170 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py |
def __call__(
self,
input_ids,
attention_mask,
token_type_ids: Optional[np.ndarray] = None,
position_ids: Optional[np.ndarray] = None,
deterministic: bool = True,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: boo... | 3,170 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py |
outputs = self.encoder(
hidden_states,
attention_mask,
deterministic=deterministic,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = outputs[0]
if sel... | 3,170 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py |
class FlaxAlbertModel(FlaxAlbertPreTrainedModel):
module_class = FlaxAlbertModule | 3,171 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py |
class FlaxAlbertForPreTrainingModule(nn.Module):
config: AlbertConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.albert = FlaxAlbertModule(config=self.config, dtype=self.dtype)
self.predictions = FlaxAlbertOnlyMLMHead(config=self.config, dtype=self.dtype)
self.sop_classifi... | 3,172 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py |
if self.config.tie_word_embeddings:
shared_embedding = self.albert.variables["params"]["embeddings"]["word_embeddings"]["embedding"]
else:
shared_embedding = None
hidden_states = outputs[0]
pooled_output = outputs[1]
prediction_scores = self.predictions(hidden_s... | 3,172 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py |
class FlaxAlbertForPreTraining(FlaxAlbertPreTrainedModel):
module_class = FlaxAlbertForPreTrainingModule | 3,173 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py |
class FlaxAlbertForMaskedLMModule(nn.Module):
config: AlbertConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.albert = FlaxAlbertModule(config=self.config, add_pooling_layer=False, dtype=self.dtype)
self.predictions = FlaxAlbertOnlyMLMHead(config=self.config, dtype=self.dtype)
... | 3,174 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py |
hidden_states = outputs[0]
if self.config.tie_word_embeddings:
shared_embedding = self.albert.variables["params"]["embeddings"]["word_embeddings"]["embedding"]
else:
shared_embedding = None
# Compute the prediction scores
logits = self.predictions(hidden_states, ... | 3,174 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py |
class FlaxAlbertForMaskedLM(FlaxAlbertPreTrainedModel):
module_class = FlaxAlbertForMaskedLMModule | 3,175 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py |
class FlaxAlbertForSequenceClassificationModule(nn.Module):
config: AlbertConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.albert = FlaxAlbertModule(config=self.config, dtype=self.dtype)
classifier_dropout = (
self.config.classifier_dropout_prob
if self.co... | 3,176 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py |
def __call__(
self,
input_ids,
attention_mask,
token_type_ids,
position_ids,
deterministic: bool = True,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
# Model
outputs = self.al... | 3,176 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py |
return FlaxSequenceClassifierOutput(
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
) | 3,176 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py |
class FlaxAlbertForSequenceClassification(FlaxAlbertPreTrainedModel):
module_class = FlaxAlbertForSequenceClassificationModule | 3,177 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py |
class FlaxAlbertForMultipleChoiceModule(nn.Module):
config: AlbertConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.albert = FlaxAlbertModule(config=self.config, dtype=self.dtype)
self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)
self.classifier = nn.Dense(1,... | 3,178 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py |
def __call__(
self,
input_ids,
attention_mask,
token_type_ids,
position_ids,
deterministic: bool = True,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
num_choices = input_ids.shape[1]
... | 3,178 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py |
# Model
outputs = self.albert(
input_ids,
attention_mask,
token_type_ids,
position_ids,
deterministic=deterministic,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_... | 3,178 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py |
class FlaxAlbertForMultipleChoice(FlaxAlbertPreTrainedModel):
module_class = FlaxAlbertForMultipleChoiceModule | 3,179 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py |
class FlaxAlbertForTokenClassificationModule(nn.Module):
config: AlbertConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.albert = FlaxAlbertModule(config=self.config, dtype=self.dtype, add_pooling_layer=False)
classifier_dropout = (
self.config.classifier_dropout_prob
... | 3,180 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py |
def __call__(
self,
input_ids,
attention_mask,
token_type_ids,
position_ids,
deterministic: bool = True,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
# Model
outputs = self.al... | 3,180 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py |
return FlaxTokenClassifierOutput(
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
) | 3,180 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py |
class FlaxAlbertForTokenClassification(FlaxAlbertPreTrainedModel):
module_class = FlaxAlbertForTokenClassificationModule | 3,181 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py |
class FlaxAlbertForQuestionAnsweringModule(nn.Module):
config: AlbertConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.albert = FlaxAlbertModule(config=self.config, dtype=self.dtype, add_pooling_layer=False)
self.qa_outputs = nn.Dense(self.config.num_labels, dtype=self.dtype)
... | 3,182 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py |
logits = self.qa_outputs(hidden_states)
start_logits, end_logits = logits.split(self.config.num_labels, axis=-1)
start_logits = start_logits.squeeze(-1)
end_logits = end_logits.squeeze(-1)
if not return_dict:
return (start_logits, end_logits) + outputs[1:]
return Fl... | 3,182 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py |
class FlaxAlbertForQuestionAnswering(FlaxAlbertPreTrainedModel):
module_class = FlaxAlbertForQuestionAnsweringModule | 3,183 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_flax_albert.py |
class AlbertTokenizer(PreTrainedTokenizer):
"""
Construct an ALBERT tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information rega... | 3,184 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/tokenization_albert.py |
Args:
vocab_file (`str`):
[SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that
contains the vocabulary necessary to instantiate a tokenizer.
do_lower_case (`bool`, *optional*, defaults to `True`):
Whether or not to lowe... | 3,184 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/tokenization_albert.py |
When building a sequence using special tokens, this is not the token that is used for the beginning of
sequence. The token used is the `cls_token`.
</Tip>
eos_token (`str`, *optional*, defaults to `"[SEP]"`):
The end of sequence token.
<Tip>
When b... | 3,184 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/tokenization_albert.py |
unk_token (`str`, *optional*, defaults to `"<unk>"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
sep_token (`str`, *optional*, defaults to `"[SEP]"`):
The separator token, which is used when build... | 3,184 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/tokenization_albert.py |
instead of per-token classification). It is the first token of the sequence when built with special tokens.
mask_token (`str`, *optional*, defaults to `"[MASK]"`):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is th... | 3,184 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/tokenization_albert.py |
- `enable_sampling`: Enable subword regularization.
- `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
- `nbest_size = {0,1}`: No sampling is performed.
- `nbest_size > 1`: samples from the nbest_size results.
- `nbest_size < 0`: assuming tha... | 3,184 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/tokenization_albert.py |
def __init__(
self,
vocab_file,
do_lower_case=True,
remove_space=True,
keep_accents=False,
bos_token="[CLS]",
eos_token="[SEP]",
unk_token="<unk>",
sep_token="[SEP]",
pad_token="<pad>",
cls_token="[CLS]",
mask_token="[MASK]"... | 3,184 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/tokenization_albert.py |
self.do_lower_case = do_lower_case
self.remove_space = remove_space
self.keep_accents = keep_accents
self.vocab_file = vocab_file
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(vocab_file)
super().__init__(
do_lower_cas... | 3,184 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/tokenization_albert.py |
def __getstate__(self):
state = self.__dict__.copy()
state["sp_model"] = None
return state
def __setstate__(self, d):
self.__dict__ = d
# for backward compatibility
if not hasattr(self, "sp_model_kwargs"):
self.sp_model_kwargs = {}
self.sp_model... | 3,184 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/tokenization_albert.py |
def _tokenize(self, text: str) -> List[str]:
"""Tokenize a string."""
text = self.preprocess_text(text)
pieces = self.sp_model.encode(text, out_type=str)
new_pieces = []
for piece in pieces:
if len(piece) > 1 and piece[-1] == str(",") and piece[-2].isdigit():
... | 3,184 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/tokenization_albert.py |
new_pieces.append(piece) | 3,184 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/tokenization_albert.py |
return new_pieces
def _convert_token_to_id(self, token):
"""Converts a token (str) in an id using the vocab."""
return self.sp_model.PieceToId(token)
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
return self.sp_model.... | 3,184 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/tokenization_albert.py |
def convert_tokens_to_string(self, tokens):
"""Converts a sequence of tokens (string) in a single string."""
current_sub_tokens = []
out_string = ""
prev_is_special = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
... | 3,184 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/tokenization_albert.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. An ALBERT sequence ha... | 3,184 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/tokenization_albert.py |
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