code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
values |
|---|---|---|---|---|---|---|---|
def mask_tokens(self, inputs: Any) -> Tuple[Any, Any, Any]:
"""
Prepare masked tokens inputs/labels/attention_mask for masked language modeling: 80% MASK, 10% random, 10%
original. N-gram not applied yet.
"""
import torch
if self.tokenizer.mask_token is None:
... |
Prepare masked tokens inputs/labels/attention_mask for masked language modeling: 80% MASK, 10% random, 10%
original. N-gram not applied yet.
| mask_tokens | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/data/data_collator.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/data/data_collator.py | Apache-2.0 |
def torch_mask_tokens(self, inputs: Any) -> Tuple[Any, Any, Any, Any]:
"""
The masked tokens to be predicted for a particular sequence are determined by the following algorithm:
0. Start from the beginning of the sequence by setting `cur_len = 0` (number of tokens processed so far).
... |
The masked tokens to be predicted for a particular sequence are determined by the following algorithm:
0. Start from the beginning of the sequence by setting `cur_len = 0` (number of tokens processed so far).
1. Sample a `span_length` from the interval `[1, max_span_length]` (length of... | torch_mask_tokens | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/data/data_collator.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/data/data_collator.py | Apache-2.0 |
def get_raw_scores(examples, preds):
"""
Computes the exact and f1 scores from the examples and the model predictions
"""
exact_scores = {}
f1_scores = {}
for example in examples:
qas_id = example.qas_id
gold_answers = [answer["text"] for answer in example.answers if normalize_a... |
Computes the exact and f1 scores from the examples and the model predictions
| get_raw_scores | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/data/metrics/squad_metrics.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/data/metrics/squad_metrics.py | Apache-2.0 |
def compute_predictions_logits(
all_examples,
all_features,
all_results,
n_best_size,
max_answer_length,
do_lower_case,
output_prediction_file,
output_nbest_file,
output_null_log_odds_file,
verbose_logging,
version_2_with_negative,
null_score_diff_threshold,
tokenizer... | Write final predictions to the json file and log-odds of null if needed. | compute_predictions_logits | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/data/metrics/squad_metrics.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/data/metrics/squad_metrics.py | Apache-2.0 |
def compute_predictions_log_probs(
all_examples,
all_features,
all_results,
n_best_size,
max_answer_length,
output_prediction_file,
output_nbest_file,
output_null_log_odds_file,
start_n_top,
end_n_top,
version_2_with_negative,
tokenizer,
verbose_logging,
):
"""
... |
XLNet write prediction logic (more complex than Bert's). Write final predictions to the json file and log-odds of
null if needed.
Requires utils_squad_evaluate.py
| compute_predictions_log_probs | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/data/metrics/squad_metrics.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/data/metrics/squad_metrics.py | Apache-2.0 |
def glue_convert_examples_to_features(
examples: Union[List[InputExample], "tf.data.Dataset"],
tokenizer: PreTrainedTokenizer,
max_length: Optional[int] = None,
task=None,
label_list=None,
output_mode=None,
):
"""
Loads a data file into a list of `InputFeatures`
Args:
exampl... |
Loads a data file into a list of `InputFeatures`
Args:
examples: List of `InputExamples` or `tf.data.Dataset` containing the examples.
tokenizer: Instance of a tokenizer that will tokenize the examples
max_length: Maximum example length. Defaults to the tokenizer's max_len
task... | glue_convert_examples_to_features | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/data/processors/glue.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/data/processors/glue.py | Apache-2.0 |
def _tf_glue_convert_examples_to_features(
examples: tf.data.Dataset,
tokenizer: PreTrainedTokenizer,
task=str,
max_length: Optional[int] = None,
) -> tf.data.Dataset:
"""
Returns:
A `tf.data.Dataset` containing the task-specific features.
"""
... |
Returns:
A `tf.data.Dataset` containing the task-specific features.
| _tf_glue_convert_examples_to_features | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/data/processors/glue.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/data/processors/glue.py | Apache-2.0 |
def _create_examples(self, lines, set_type):
"""Creates examples for the training, dev and test sets."""
examples = []
for i, line in enumerate(lines):
if i == 0:
continue
guid = f"{set_type}-{i}"
text_a = line[3]
text_b = line[4]
... | Creates examples for the training, dev and test sets. | _create_examples | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/data/processors/glue.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/data/processors/glue.py | Apache-2.0 |
def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer, orig_answer_text):
"""Returns tokenized answer spans that better match the annotated answer."""
tok_answer_text = " ".join(tokenizer.tokenize(orig_answer_text))
for new_start in range(input_start, input_end + 1):
for new_end in... | Returns tokenized answer spans that better match the annotated answer. | _improve_answer_span | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/data/processors/squad.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/data/processors/squad.py | Apache-2.0 |
def squad_convert_examples_to_features(
examples,
tokenizer,
max_seq_length,
doc_stride,
max_query_length,
is_training,
padding_strategy="max_length",
return_dataset=False,
threads=1,
tqdm_enabled=True,
):
"""
Converts a list of examples into a list of features that can b... |
Converts a list of examples into a list of features that can be directly given as input to a model. It is
model-dependant and takes advantage of many of the tokenizer's features to create the model's inputs.
Args:
examples: list of [`~data.processors.squad.SquadExample`]
tokenizer: an inst... | squad_convert_examples_to_features | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/data/processors/squad.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/data/processors/squad.py | Apache-2.0 |
def get_examples_from_dataset(self, dataset, evaluate=False):
"""
Creates a list of [`~data.processors.squad.SquadExample`] using a TFDS dataset.
Args:
dataset: The tfds dataset loaded from *tensorflow_datasets.load("squad")*
evaluate: Boolean specifying if in evaluation... |
Creates a list of [`~data.processors.squad.SquadExample`] using a TFDS dataset.
Args:
dataset: The tfds dataset loaded from *tensorflow_datasets.load("squad")*
evaluate: Boolean specifying if in evaluation mode or in training mode
Returns:
List of SquadExam... | get_examples_from_dataset | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/data/processors/squad.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/data/processors/squad.py | Apache-2.0 |
def get_train_examples(self, data_dir, filename=None):
"""
Returns the training examples from the data directory.
Args:
data_dir: Directory containing the data files used for training and evaluating.
filename: None by default, specify this if the training file has a diff... |
Returns the training examples from the data directory.
Args:
data_dir: Directory containing the data files used for training and evaluating.
filename: None by default, specify this if the training file has a different name than the original one
which is `train-v... | get_train_examples | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/data/processors/squad.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/data/processors/squad.py | Apache-2.0 |
def get_dev_examples(self, data_dir, filename=None):
"""
Returns the evaluation example from the data directory.
Args:
data_dir: Directory containing the data files used for training and evaluating.
filename: None by default, specify this if the evaluation file has a dif... |
Returns the evaluation example from the data directory.
Args:
data_dir: Directory containing the data files used for training and evaluating.
filename: None by default, specify this if the evaluation file has a different name than the original one
which is `dev-... | get_dev_examples | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/data/processors/squad.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/data/processors/squad.py | Apache-2.0 |
def tfds_map(self, example):
"""
Some tensorflow_datasets datasets are not formatted the same way the GLUE datasets are. This method converts
examples to the correct format.
"""
if len(self.get_labels()) > 1:
example.label = self.get_labels()[int(example.label)]
... |
Some tensorflow_datasets datasets are not formatted the same way the GLUE datasets are. This method converts
examples to the correct format.
| tfds_map | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/data/processors/utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/data/processors/utils.py | Apache-2.0 |
def get_features(
self,
tokenizer,
max_length=None,
pad_on_left=False,
pad_token=0,
mask_padding_with_zero=True,
return_tensors=None,
):
"""
Convert examples in a list of `InputFeatures`
Args:
tokenizer: Instance of a token... |
Convert examples in a list of `InputFeatures`
Args:
tokenizer: Instance of a tokenizer that will tokenize the examples
max_length: Maximum example length
pad_on_left: If set to `True`, the examples will be padded on the left rather than on the right (default)
... | get_features | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/data/processors/utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/data/processors/utils.py | Apache-2.0 |
def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None:
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} ALBERT has
a different architecture in that its layers are shared across groups, which then has inner groups. If an ALBER... |
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} ALBERT has
a different architecture in that its layers are shared across groups, which then has inner groups. If an ALBERT
model has 12 hidden layers and 2 hidden groups, with two inner groups, ... | _prune_heads | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/albert/modeling_albert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/albert/modeling_albert.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
... |
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
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
... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/albert/modeling_albert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/albert/modeling_albert.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
... |
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
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
... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/albert/modeling_albert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/albert/modeling_albert.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
... |
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
| forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/albert/modeling_albert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/albert/modeling_albert.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
... |
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 sequence (`sequence_length`). Position outside of the sequence... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/albert/modeling_albert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/albert/modeling_albert.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
... |
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
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... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/albert/modeling_albert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/albert/modeling_albert.py | Apache-2.0 |
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: bool = False,
) -> tf.Tensor:
"""
Applies embedding based on inp... |
Applies embedding based on inputs tensor.
Returns:
final_embeddings (`tf.Tensor`): output embedding tensor.
| call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/albert/modeling_tf_albert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/albert/modeling_tf_albert.py | Apache-2.0 |
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Uni... |
Return:
Example:
```python
>>> import tensorflow as tf
>>> from transformers import AlbertTokenizer, TFAlbertForPreTraining
>>> tokenizer = AlbertTokenizer.from_pretrained("albert-base-v2")
>>> model = TFAlbertForPreTraining.from_pretrained("albert-base-v2")
... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/albert/modeling_tf_albert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/albert/modeling_tf_albert.py | Apache-2.0 |
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Uni... |
labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
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
... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/albert/modeling_tf_albert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/albert/modeling_tf_albert.py | Apache-2.0 |
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Uni... |
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 regression loss is computed (Mean-Square loss), If
`config.num_l... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/albert/modeling_tf_albert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/albert/modeling_tf_albert.py | Apache-2.0 |
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Uni... |
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]`.
| call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/albert/modeling_tf_albert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/albert/modeling_tf_albert.py | Apache-2.0 |
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Uni... |
start_positions (`tf.Tensor` 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 sequence (`sequence_length`). Position outside of the sequence
... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/albert/modeling_tf_albert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/albert/modeling_tf_albert.py | Apache-2.0 |
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Uni... |
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)
| call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/albert/modeling_tf_albert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/albert/modeling_tf_albert.py | Apache-2.0 |
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... |
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. An ALBERT sequence has the following format:
- single sequence: `[CLS] X [SEP]`
- pair of sequences: `[CLS] A [SEP] B [SEP]`
Args:
... | build_inputs_with_special_tokens | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/albert/tokenization_albert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/albert/tokenization_albert.py | Apache-2.0 |
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 ... |
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer `prepare_for_model` method.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*)... | get_special_tokens_mask | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/albert/tokenization_albert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/albert/tokenization_albert.py | Apache-2.0 |
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create 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:
... |
Create 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:
```
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence | second sequence |
```
If `token_ids_1` is `No... | create_token_type_ids_from_sequences | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/albert/tokenization_albert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/albert/tokenization_albert.py | Apache-2.0 |
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... |
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. An ALBERT sequence has the following format:
- single sequence: `[CLS] X [SEP]`
- pair of sequences: `[CLS] A [SEP] B [SEP]`
Args:
... | build_inputs_with_special_tokens | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/albert/tokenization_albert_fast.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/albert/tokenization_albert_fast.py | Apache-2.0 |
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:
... |
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:
```
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence | second sequence |
```
if token_ids_1 is None... | create_token_type_ids_from_sequences | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/albert/tokenization_albert_fast.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/albert/tokenization_albert_fast.py | Apache-2.0 |
def register(cls, config_class, model_class):
"""
Register a new model for this class.
Args:
config_class ([`PretrainedConfig`]):
The configuration corresponding to the model to register.
model_class ([`PreTrainedModel`]):
The model to reg... |
Register a new model for this class.
Args:
config_class ([`PretrainedConfig`]):
The configuration corresponding to the model to register.
model_class ([`PreTrainedModel`]):
The model to register.
| register | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/auto/auto_factory.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/auto/auto_factory.py | Apache-2.0 |
def register(self, key, value):
"""
Register a new model in this mapping.
"""
if hasattr(key, "__name__") and key.__name__ in self._reverse_config_mapping:
model_type = self._reverse_config_mapping[key.__name__]
if model_type in self._model_mapping.keys():
... |
Register a new model in this mapping.
| register | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/auto/auto_factory.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/auto/auto_factory.py | Apache-2.0 |
def model_type_to_module_name(key):
"""Converts a config key to the corresponding module."""
# Special treatment
if key in SPECIAL_MODEL_TYPE_TO_MODULE_NAME:
return SPECIAL_MODEL_TYPE_TO_MODULE_NAME[key]
return key.replace("-", "_") | Converts a config key to the corresponding module. | model_type_to_module_name | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/auto/configuration_auto.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/auto/configuration_auto.py | Apache-2.0 |
def config_class_to_model_type(config):
"""Converts a config class name to the corresponding model type"""
for key, cls in CONFIG_MAPPING_NAMES.items():
if cls == config:
return key
return None | Converts a config class name to the corresponding model type | config_class_to_model_type | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/auto/configuration_auto.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/auto/configuration_auto.py | Apache-2.0 |
def register(self, key, value):
"""
Register a new configuration in this mapping.
"""
if key in self._mapping.keys():
raise ValueError(f"'{key}' is already used by a Transformers config, pick another name.")
self._extra_content[key] = value |
Register a new configuration in this mapping.
| register | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/auto/configuration_auto.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/auto/configuration_auto.py | Apache-2.0 |
def register(model_type, config):
"""
Register a new configuration for this class.
Args:
model_type (`str`): The model type like "bert" or "gpt".
config ([`PretrainedConfig`]): The config to register.
"""
if issubclass(config, PretrainedConfig) and config... |
Register a new configuration for this class.
Args:
model_type (`str`): The model type like "bert" or "gpt".
config ([`PretrainedConfig`]): The config to register.
| register | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/auto/configuration_auto.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/auto/configuration_auto.py | Apache-2.0 |
def register(config_class, slow_tokenizer_class=None, fast_tokenizer_class=None):
"""
Register a new tokenizer in this mapping.
Args:
config_class ([`PretrainedConfig`]):
The configuration corresponding to the model to register.
slow_tokenizer_class ([`P... |
Register a new tokenizer in this mapping.
Args:
config_class ([`PretrainedConfig`]):
The configuration corresponding to the model to register.
slow_tokenizer_class ([`PretrainedTokenizer`], *optional*):
The slow tokenizer to register.
... | register | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/auto/tokenization_auto.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/auto/tokenization_auto.py | Apache-2.0 |
def load_xsum_checkpoint(checkpoint_path):
"""Checkpoint path should end in model.pt"""
sd = torch.load(checkpoint_path, map_location="cpu")
hub_interface = torch.hub.load("pytorch/fairseq", "bart.large.cnn").eval()
hub_interface.model.load_state_dict(sd["model"])
return hub_interface | Checkpoint path should end in model.pt | load_xsum_checkpoint | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/bart/convert_bart_original_pytorch_checkpoint_to_pytorch.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/bart/convert_bart_original_pytorch_checkpoint_to_pytorch.py | Apache-2.0 |
def convert_bart_checkpoint(checkpoint_path, pytorch_dump_folder_path, hf_checkpoint_name=None):
"""
Copy/paste/tweak model's weights to our BERT structure.
"""
if not os.path.exists(checkpoint_path):
bart = torch.hub.load("pytorch/fairseq", checkpoint_path).eval()
else:
bart = load_... |
Copy/paste/tweak model's weights to our BERT structure.
| convert_bart_checkpoint | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/bart/convert_bart_original_pytorch_checkpoint_to_pytorch.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/bart/convert_bart_original_pytorch_checkpoint_to_pytorch.py | Apache-2.0 |
def forward(self, input_ids: torch.Tensor, past_key_values_length: int = 0):
"""`input_ids' shape is expected to be [bsz x seqlen]."""
bsz, seq_len = input_ids.shape[:2]
positions = torch.arange(
past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=self... | `input_ids' shape is expected to be [bsz x seqlen]. | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/bart/modeling_bart.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/bart/modeling_bart.py | Apache-2.0 |
def forward(
self,
hidden_states: torch.FloatTensor,
attention_mask: torch.FloatTensor,
layer_head_mask: torch.FloatTensor,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]:
"""
Args:
hidden_stat... |
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(seq_len, batch, embed_dim)`
attention_mask (`torch.FloatTensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/bart/modeling_bart.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/bart/modeling_bart.py | Apache-2.0 |
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optio... |
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`BartTokenizer`]. See [`PreTra... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/bart/modeling_bart.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/bart/modeling_bart.py | Apache-2.0 |
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
cr... |
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`BartTokenizer`]. See [`PreTra... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/bart/modeling_bart.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/bart/modeling_bart.py | Apache-2.0 |
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
decoder... |
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/bart/modeling_bart.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/bart/modeling_bart.py | Apache-2.0 |
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
decoder... |
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 classification loss is computed (Cross-Entropy).
| forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/bart/modeling_bart.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/bart/modeling_bart.py | Apache-2.0 |
def forward(
self,
input_ids: torch.Tensor = None,
attention_mask: Optional[torch.Tensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
decoder_hea... |
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 sequence (*sequence_length*). Position outside of the sequence... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/bart/modeling_bart.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/bart/modeling_bart.py | Apache-2.0 |
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.Tensor] = None,
c... |
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`BartTokenizer`]. See [`PreTra... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/bart/modeling_bart.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/bart/modeling_bart.py | Apache-2.0 |
def _concatenate_to_cache(self, key, value, query, attention_mask):
"""
This function takes projected key, value states from a single input token and concatenates the states to cached
states from previous steps. This function is slighly adapted from the official Flax repository:
https://... |
This function takes projected key, value states from a single input token and concatenates the states to cached
states from previous steps. This function is slighly adapted from the official Flax repository:
https://github.com/google/flax/blob/491ce18759622506588784b4fca0e4bf05f8c8cd/flax/linen... | _concatenate_to_cache | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/bart/modeling_flax_bart.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/bart/modeling_flax_bart.py | Apache-2.0 |
def encode(
self,
input_ids: jnp.ndarray,
attention_mask: Optional[jnp.ndarray] = None,
position_ids: Optional[jnp.ndarray] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
t... |
Returns:
Example:
```python
>>> from transformers import BartTokenizer, FlaxBartForConditionalGeneration
>>> model = FlaxBartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn")
>>> tokenizer = BartTokenizer.from_pretrained("facebook/bart-large-cnn")
... | encode | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/bart/modeling_flax_bart.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/bart/modeling_flax_bart.py | Apache-2.0 |
def decode(
self,
decoder_input_ids,
encoder_outputs,
encoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_position_ids: Optional[jnp.ndarray] = None,
past_key_values: dict = None,
output_atten... |
Returns:
Example:
```python
>>> import jax.numpy as jnp
>>> from transformers import BartTokenizer, FlaxBartForConditionalGeneration
>>> model = FlaxBartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn")
>>> tokenizer = BartTokenizer.from_pret... | decode | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/bart/modeling_flax_bart.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/bart/modeling_flax_bart.py | Apache-2.0 |
def decode(
self,
decoder_input_ids,
encoder_outputs,
encoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_position_ids: Optional[jnp.ndarray] = None,
past_key_values: dict = None,
output_atten... |
Returns:
Example:
```python
>>> import jax.numpy as jnp
>>> from transformers import BartTokenizer, FlaxBartForConditionalGeneration
>>> model = FlaxBartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn")
>>> tokenizer = BartTokenizer.from_pret... | decode | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/bart/modeling_flax_bart.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/bart/modeling_flax_bart.py | Apache-2.0 |
def init_cache(self, batch_size, max_length):
r"""
Args:
batch_size (`int`):
batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache.
max_length (`int`):
maximum possible length for auto-regressive decodin... |
Args:
batch_size (`int`):
batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache.
max_length (`int`):
maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized
... | init_cache | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/bart/modeling_flax_bart.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/bart/modeling_flax_bart.py | Apache-2.0 |
def call(
self,
hidden_states: tf.Tensor,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]],
layer_head_mask: Optional[tf.Tensor],
training: Optional[bool] = False,
) -> tf.Tensor:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of s... |
Args:
hidden_states (`tf.Tensor`): input to the layer of shape `(seq_len, batch, embed_dim)`
attention_mask (`tf.Tensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
layer_head_mas... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/bart/modeling_tf_bart.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/bart/modeling_tf_bart.py | Apache-2.0 |
def call(
self,
hidden_states: tf.Tensor,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
encoder_hidden_states: Optional[Union[np.ndarray, tf.Tensor]] = None,
encoder_attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
layer_head_mask: Optional[... |
Args:
hidden_states (`tf.Tensor`): input to the layer of shape `(seq_len, batch, embed_dim)`
attention_mask (`tf.Tensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
encoder_hidden... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/bart/modeling_tf_bart.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/bart/modeling_tf_bart.py | Apache-2.0 |
def call(
self,
input_ids: Optional[TFModelInputType] = None,
inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
output_attentions: Optional... |
Args:
input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`BartTokenizer`]. See [`PreTrainedTok... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/bart/modeling_tf_bart.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/bart/modeling_tf_bart.py | Apache-2.0 |
def call(
self,
input_ids: Optional[TFModelInputType] = None,
inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
encoder_hidden_states: O... |
Args:
input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`BartTokenizer`]. See [`PreTrainedTok... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/bart/modeling_tf_bart.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/bart/modeling_tf_bart.py | Apache-2.0 |
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
decoder_input_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
decoder_attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
decoder_po... |
labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(ma... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/bart/modeling_tf_bart.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/bart/modeling_tf_bart.py | Apache-2.0 |
def get_pairs(word):
"""
Return set of symbol pairs in a word.
Word is represented as tuple of symbols (symbols being variable-length strings).
"""
pairs = set()
prev_char = word[0]
for char in word[1:]:
pairs.add((prev_char, char))
prev_char = char
return pairs |
Return set of symbol pairs in a word.
Word is represented as tuple of symbols (symbols being variable-length strings).
| get_pairs | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/bart/tokenization_bart.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/bart/tokenization_bart.py | Apache-2.0 |
def convert_tokens_to_string(self, tokens):
"""Converts a sequence of tokens (string) in a single string."""
text = "".join(tokens)
text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors)
return text | Converts a sequence of tokens (string) in a single string. | convert_tokens_to_string | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/bart/tokenization_bart.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/bart/tokenization_bart.py | Apache-2.0 |
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 BART sequence has t... |
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A BART sequence has the following format:
- single sequence: `<s> X </s>`
- pair of sequences: `<s> A </s></s> B </s>`
Args:
token... | build_inputs_with_special_tokens | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/bart/tokenization_bart.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/bart/tokenization_bart.py | Apache-2.0 |
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task. BART does not
make use of token type ids, therefore a lis... |
Create a mask from the two sequences passed to be used in a sequence-pair classification task. BART does not
make use of token type ids, therefore a list of zeros is returned.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optio... | create_token_type_ids_from_sequences | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/bart/tokenization_bart.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/bart/tokenization_bart.py | Apache-2.0 |
def mask_token(self) -> str:
"""
`str`: Mask token, to use when training a model with masked-language modeling. Log an error if used while not
having been set.
BART tokenizer has a special mask token to be usable in the fill-mask pipeline. The mask token will greedily
comprise t... |
`str`: Mask token, to use when training a model with masked-language modeling. Log an error if used while not
having been set.
BART tokenizer has a special mask token to be usable in the fill-mask pipeline. The mask token will greedily
comprise the space before the *<mask>*.
| mask_token | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/bart/tokenization_bart_fast.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/bart/tokenization_bart_fast.py | Apache-2.0 |
def mask_token(self, value):
"""
Overriding the default behavior of the mask token to have it eat the space before it.
This is needed to preserve backward compatibility with all the previously used models based on Bart.
"""
# Mask token behave like a normal word, i.e. include th... |
Overriding the default behavior of the mask token to have it eat the space before it.
This is needed to preserve backward compatibility with all the previously used models based on Bart.
| mask_token | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/bart/tokenization_bart_fast.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/bart/tokenization_bart_fast.py | Apache-2.0 |
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 BARThez sequence ha... |
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A BARThez sequence has the following format:
- single sequence: `<s> X </s>`
- pair of sequences: `<s> A </s></s> B </s>`
Args:
to... | build_inputs_with_special_tokens | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/barthez/tokenization_barthez.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/barthez/tokenization_barthez.py | Apache-2.0 |
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task.
Args:
token_ids_0 (`List[int]`):
... |
Create a mask from the two sequences passed to be used in a sequence-pair classification task.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
... | create_token_type_ids_from_sequences | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/barthez/tokenization_barthez.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/barthez/tokenization_barthez.py | Apache-2.0 |
def _convert_token_to_id(self, token):
"""Converts a token (str) in an id using the vocab."""
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
spm_id = self.sp_model.PieceToId(token)
return spm_id if spm_id else self.unk_token_id | Converts a token (str) in an id using the vocab. | _convert_token_to_id | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/barthez/tokenization_barthez.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/barthez/tokenization_barthez.py | Apache-2.0 |
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 BARTPho sequence h... |
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. An BARTPho sequence has the following format:
- single sequence: `<s> X </s>`
- pair of sequences: `<s> A </s></s> B </s>`
Args:
t... | build_inputs_with_special_tokens | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/bartpho/tokenization_bartpho.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/bartpho/tokenization_bartpho.py | Apache-2.0 |
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task. BARTPho does not
make use of token type ids, therefore a ... |
Create a mask from the two sequences passed to be used in a sequence-pair classification task. BARTPho does not
make use of token type ids, therefore a list of zeros is returned.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *op... | create_token_type_ids_from_sequences | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/bartpho/tokenization_bartpho.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/bartpho/tokenization_bartpho.py | Apache-2.0 |
def convert_beit_checkpoint(checkpoint_url, pytorch_dump_folder_path):
"""
Copy/paste/tweak model's weights to our BEiT structure.
"""
# define default BEiT configuration
config = BeitConfig()
has_lm_head = False
is_semantic = False
repo_id = "huggingface/label-files"
# set config p... |
Copy/paste/tweak model's weights to our BEiT structure.
| convert_beit_checkpoint | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/beit/convert_beit_unilm_to_pytorch.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/beit/convert_beit_unilm_to_pytorch.py | Apache-2.0 |
def __call__(
self,
images: ImageInput,
segmentation_maps: ImageInput = None,
return_tensors: Optional[Union[str, TensorType]] = None,
**kwargs
) -> BatchFeature:
"""
Main method to prepare for the model one or several image(s).
<Tip warning={true}>
... |
Main method to prepare for the model one or several image(s).
<Tip warning={true}>
NumPy arrays and PyTorch tensors are converted to PIL images when resizing, so the most efficient is to pass
PIL images.
</Tip>
Args:
images (`PIL.Image.Image`, `np.ndarray... | __call__ | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/beit/feature_extraction_beit.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/beit/feature_extraction_beit.py | Apache-2.0 |
def post_process_semantic_segmentation(self, outputs, target_sizes: List[Tuple] = None):
"""
Converts the output of [`BeitForSemanticSegmentation`] into semantic segmentation maps. Only supports PyTorch.
Args:
outputs ([`BeitForSemanticSegmentation`]):
Raw outputs of... |
Converts the output of [`BeitForSemanticSegmentation`] into semantic segmentation maps. Only supports PyTorch.
Args:
outputs ([`BeitForSemanticSegmentation`]):
Raw outputs of the model.
target_sizes (`List[Tuple]` of length `batch_size`, *optional*):
... | post_process_semantic_segmentation | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/beit/feature_extraction_beit.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/beit/feature_extraction_beit.py | Apache-2.0 |
def drop_path(input: torch.Tensor, drop_prob: float = 0.0, training: bool = False) -> torch.Tensor:
"""
Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
Comment by Ross Wightman: This is the same as the DropConnect impl I created for EfficientNet, etc networks,
h... |
Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
Comment by Ross Wightman: This is the same as the DropConnect impl I created for EfficientNet, etc networks,
however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate pape... | drop_path | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/beit/modeling_beit.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/beit/modeling_beit.py | Apache-2.0 |
def forward(
self,
pixel_values: Optional[torch.Tensor] = None,
bool_masked_pos: Optional[torch.BoolTensor] = None,
head_mask: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Opt... |
bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`):
Boolean masked positions. Indicates which patches are masked (1) and which aren't (0).
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the image classification/regr... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/beit/modeling_beit.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/beit/modeling_beit.py | Apache-2.0 |
def forward(
self,
pixel_values: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = No... |
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the image 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.n... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/beit/modeling_beit.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/beit/modeling_beit.py | Apache-2.0 |
def forward(
self,
pixel_values: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = No... |
labels (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*):
Ground truth semantic segmentation maps for computing the loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels > 1`, a classification loss is computed (Cross-Entropy).
... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/beit/modeling_beit.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/beit/modeling_beit.py | Apache-2.0 |
def relative_position_index_init(window_size: Tuple[int, int]) -> jnp.ndarray:
"""
get pair-wise relative position index for each token inside the window
"""
num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3
coords_h = np.arange(window_size[0])
coords_w = np.arange... |
get pair-wise relative position index for each token inside the window
| relative_position_index_init | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/beit/modeling_flax_beit.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/beit/modeling_flax_beit.py | Apache-2.0 |
def convert_pytorch_checkpoint_to_tf(model: BertModel, ckpt_dir: str, model_name: str):
"""
Args:
model: BertModel Pytorch model instance to be converted
ckpt_dir: Tensorflow model directory
model_name: model name
Currently supported HF models:
- Y BertModel
- N Be... |
Args:
model: BertModel Pytorch model instance to be converted
ckpt_dir: Tensorflow model directory
model_name: model name
Currently supported HF models:
- Y BertModel
- N BertForMaskedLM
- N BertForPreTraining
- N BertForMultipleChoice
- N BertF... | convert_pytorch_checkpoint_to_tf | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/bert/convert_bert_pytorch_checkpoint_to_original_tf.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/bert/convert_bert_pytorch_checkpoint_to_original_tf.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optiona... |
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/bert/modeling_bert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/bert/modeling_bert.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optiona... |
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
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 (maske... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/bert/modeling_bert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/bert/modeling_bert.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optiona... |
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/bert/modeling_bert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/bert/modeling_bert.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optiona... |
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
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
... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/bert/modeling_bert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/bert/modeling_bert.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optiona... |
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair
(see `input_ids` docstring). Indices should be in `[0, 1]`:
- 0 indicates sequence B is a continuation ... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/bert/modeling_bert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/bert/modeling_bert.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optiona... |
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
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... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/bert/modeling_bert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/bert/modeling_bert.py | Apache-2.0 |
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Uni... |
encoder_hidden_states (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (`tf.Tens... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/bert/modeling_tf_bert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/bert/modeling_tf_bert.py | Apache-2.0 |
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Uni... |
labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
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
... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/bert/modeling_tf_bert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/bert/modeling_tf_bert.py | Apache-2.0 |
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Uni... |
labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
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),... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/bert/modeling_tf_bert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/bert/modeling_tf_bert.py | Apache-2.0 |
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Uni... |
encoder_hidden_states (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (`tf.Tens... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/bert/modeling_tf_bert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/bert/modeling_tf_bert.py | Apache-2.0 |
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Uni... |
Return:
Examples:
```python
>>> import tensorflow as tf
>>> from transformers import BertTokenizer, TFBertForNextSentencePrediction
>>> tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
>>> model = TFBertForNextSentencePrediction.from_pretrained("... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/bert/modeling_tf_bert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/bert/modeling_tf_bert.py | Apache-2.0 |
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Uni... |
labels (`tf.Tensor` or `np.ndarray` 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
... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/bert/modeling_tf_bert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/bert/modeling_tf_bert.py | Apache-2.0 |
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Uni... |
labels (`tf.Tensor` or `np.ndarray` 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)
... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/bert/modeling_tf_bert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/bert/modeling_tf_bert.py | Apache-2.0 |
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Uni... |
labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
| call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/bert/modeling_tf_bert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/bert/modeling_tf_bert.py | Apache-2.0 |
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Uni... |
start_positions (`tf.Tensor` or `np.ndarray` 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 sequence (`sequence_length`). Position outside of the... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/bert/modeling_tf_bert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/bert/modeling_tf_bert.py | Apache-2.0 |
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task. A BERT sequence
pair mask has the following format:
... |
Create a mask from the two sequences passed to be used in a sequence-pair classification task. A BERT sequence
pair mask has the following format:
```
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence | second sequence |
```
If `token_ids_1` is `None`... | create_token_type_ids_from_sequences | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/bert/tokenization_bert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/bert/tokenization_bert.py | Apache-2.0 |
def tokenize(self, text, never_split=None):
"""
Basic Tokenization of a piece of text. Split on "white spaces" only, for sub-word tokenization, see
WordPieceTokenizer.
Args:
never_split (`List[str]`, *optional*)
Kept for backward compatibility purposes. Now i... |
Basic Tokenization of a piece of text. Split on "white spaces" only, for sub-word tokenization, see
WordPieceTokenizer.
Args:
never_split (`List[str]`, *optional*)
Kept for backward compatibility purposes. Now implemented directly at the base class level (see
... | tokenize | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/bert/tokenization_bert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/bert/tokenization_bert.py | Apache-2.0 |
def tokenize(self, text):
"""
Tokenizes a piece of text into its word pieces. This uses a greedy longest-match-first algorithm to perform
tokenization using the given vocabulary.
For example, `input = "unaffable"` wil return as output `["un", "##aff", "##able"]`.
Args:
... |
Tokenizes a piece of text into its word pieces. This uses a greedy longest-match-first algorithm to perform
tokenization using the given vocabulary.
For example, `input = "unaffable"` wil return as output `["un", "##aff", "##able"]`.
Args:
text: A single token or whitespac... | tokenize | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/bert/tokenization_bert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/bert/tokenization_bert.py | Apache-2.0 |
def from_tokenizer(cls, tokenizer: "PreTrainedTokenizerBase", **kwargs): # noqa: F821
"""
Initialize a `TFBertTokenizer` from an existing `Tokenizer`.
Args:
tokenizer (`PreTrainedTokenizerBase`):
The tokenizer to use to initialize the `TFBertTokenizer`.
Exa... |
Initialize a `TFBertTokenizer` from an existing `Tokenizer`.
Args:
tokenizer (`PreTrainedTokenizerBase`):
The tokenizer to use to initialize the `TFBertTokenizer`.
Examples:
```python
from transformers import AutoTokenizer, TFBertTokenizer
... | from_tokenizer | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/bert/tokenization_bert_tf.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/bert/tokenization_bert_tf.py | Apache-2.0 |
def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], *init_inputs, **kwargs):
"""
Instantiate a `TFBertTokenizer` from a pre-trained tokenizer.
Args:
pretrained_model_name_or_path (`str` or `os.PathLike`):
The name or path to the pre-train... |
Instantiate a `TFBertTokenizer` from a pre-trained tokenizer.
Args:
pretrained_model_name_or_path (`str` or `os.PathLike`):
The name or path to the pre-trained tokenizer.
Examples:
```python
from transformers import TFBertTokenizer
tf_toke... | from_pretrained | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/bert/tokenization_bert_tf.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/bert/tokenization_bert_tf.py | Apache-2.0 |
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