text stringlengths 31 243k | type stringclasses 1
value | start int64 36 275k | end int64 286 280k | depth int64 0 1 | filepath stringlengths 85 188 | parent_class stringclasses 3
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|---|---|---|---|---|---|---|---|
class ConvertCommand(BaseTransformersCLICommand):
@staticmethod
def register_subcommand(parser: ArgumentParser):
"""
Register this command to argparse so it's available for the transformer-cli
Args:
parser: Root parser to register command-specific arguments
"""
... | class_definition | 1,305 | 7,067 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/convert.py | null | 10,600 |
class BaseTransformersCLICommand(ABC):
@staticmethod
@abstractmethod
def register_subcommand(parser: ArgumentParser):
raise NotImplementedError()
@abstractmethod
def run(self):
raise NotImplementedError() | class_definition | 681 | 922 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/__init__.py | null | 10,601 |
class LfsCommands(BaseTransformersCLICommand):
"""
Implementation of a custom transfer agent for the transfer type "multipart" for git-lfs. This lets users upload
large files >5GB 🔥. Spec for LFS custom transfer agent is:
https://github.com/git-lfs/git-lfs/blob/master/docs/custom-transfers.md
This... | class_definition | 941 | 2,550 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/lfs.py | null | 10,602 |
class LfsEnableCommand:
def __init__(self, args):
self.args = args
def run(self):
warnings.warn(
"Managing repositories through transformers-cli is deprecated. Please use `huggingface-cli` instead."
)
local_path = os.path.abspath(self.args.path)
if not os.pat... | class_definition | 2,553 | 3,358 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/lfs.py | null | 10,603 |
class FileSlice(AbstractContextManager):
"""
File-like object that only reads a slice of a file
Inspired by stackoverflow.com/a/29838711/593036
"""
def __init__(self, filepath: str, seek_from: int, read_limit: int):
self.filepath = filepath
self.seek_from = seek_from
self.r... | class_definition | 3,925 | 4,974 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/lfs.py | null | 10,604 |
class LfsUploadCommand:
def __init__(self, args):
self.args = args
def run(self):
# Immediately after invoking a custom transfer process, git-lfs
# sends initiation data to the process over stdin.
# This tells the process useful information about the configuration.
init_... | class_definition | 4,977 | 8,000 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/lfs.py | null | 10,605 |
class TrainCommand(BaseTransformersCLICommand):
@staticmethod
def register_subcommand(parser: ArgumentParser):
"""
Register this command to argparse so it's available for the transformer-cli
Args:
parser: Root parser to register command-specific arguments
"""
... | class_definition | 1,333 | 6,340 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/train.py | null | 10,606 |
class DataCollatorMixin:
def __call__(self, features, return_tensors=None):
if return_tensors is None:
return_tensors = self.return_tensors
if return_tensors == "tf":
return self.tf_call(features)
elif return_tensors == "pt":
return self.torch_call(feature... | class_definition | 1,307 | 1,803 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py | null | 10,607 |
class DefaultDataCollator(DataCollatorMixin):
"""
Very simple data collator that simply collates batches of dict-like objects and performs special handling for
potential keys named:
- `label`: handles a single value (int or float) per object
- `label_ids`: handles a list of values per objec... | class_definition | 3,692 | 4,833 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py | null | 10,608 |
class DataCollatorWithPadding:
"""
Data collator that will dynamically pad the inputs received.
Args:
tokenizer ([`PreTrainedTokenizer`] or [`PreTrainedTokenizerFast`]):
The tokenizer used for encoding the data.
padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, d... | class_definition | 10,078 | 12,399 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py | null | 10,609 |
class DataCollatorForTokenClassification(DataCollatorMixin):
"""
Data collator that will dynamically pad the inputs received, as well as the labels.
Args:
tokenizer ([`PreTrainedTokenizer`] or [`PreTrainedTokenizerFast`]):
The tokenizer used for encoding the data.
padding (`bool... | class_definition | 12,413 | 18,426 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py | null | 10,610 |
class DataCollatorForSeq2Seq:
"""
Data collator that will dynamically pad the inputs received, as well as the labels.
Args:
tokenizer ([`PreTrainedTokenizer`] or [`PreTrainedTokenizerFast`]):
The tokenizer used for encoding the data.
model ([`PreTrainedModel`], *optional*):
... | class_definition | 23,531 | 30,100 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py | null | 10,611 |
class DataCollatorForLanguageModeling(DataCollatorMixin):
"""
Data collator used for language modeling. Inputs are dynamically padded to the maximum length of a batch if they
are not all of the same length.
Args:
tokenizer ([`PreTrainedTokenizer`] or [`PreTrainedTokenizerFast`]):
Th... | class_definition | 30,114 | 46,400 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py | null | 10,612 |
class DataCollatorForWholeWordMask(DataCollatorForLanguageModeling):
"""
Data collator used for language modeling that masks entire words.
- collates batches of tensors, honoring their tokenizer's pad_token
- preprocesses batches for masked language modeling
<Tip>
This collator relies on deta... | class_definition | 46,414 | 59,668 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py | null | 10,613 |
class DataCollatorForSOP(DataCollatorForLanguageModeling):
"""
Data collator used for sentence order prediction task.
- collates batches of tensors, honoring their tokenizer's pad_token
- preprocesses batches for both masked language modeling and sentence order prediction
"""
def __init__(self... | class_definition | 59,682 | 63,851 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py | null | 10,614 |
class DataCollatorForPermutationLanguageModeling(DataCollatorMixin):
"""
Data collator used for permutation language modeling.
- collates batches of tensors, honoring their tokenizer's pad_token
- preprocesses batches for permutation language modeling with procedures specific to XLNet
"""
toke... | class_definition | 63,865 | 84,573 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py | null | 10,615 |
class DataCollatorWithFlattening(DefaultDataCollator):
"""
Data collator used for padding free approach. Does the following:
- concatate the entire mini batch into single long sequence [1, total_tokens]
- uses `separator_id` to separate sequences within the concatenated `labels`, default value is -100
... | class_definition | 84,587 | 86,355 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py | null | 10,616 |
class TextDataset(Dataset):
"""
This will be superseded by a framework-agnostic approach soon.
"""
def __init__(
self,
tokenizer: PreTrainedTokenizer,
file_path: str,
block_size: int,
overwrite_cache=False,
cache_dir: Optional[str] = None,
):
... | class_definition | 1,150 | 4,198 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/language_modeling.py | null | 10,617 |
class LineByLineTextDataset(Dataset):
"""
This will be superseded by a framework-agnostic approach soon.
"""
def __init__(self, tokenizer: PreTrainedTokenizer, file_path: str, block_size: int):
warnings.warn(
DEPRECATION_WARNING.format(
"https://github.com/huggingfac... | class_definition | 4,201 | 5,590 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/language_modeling.py | null | 10,618 |
class LineByLineWithRefDataset(Dataset):
"""
This will be superseded by a framework-agnostic approach soon.
"""
def __init__(self, tokenizer: PreTrainedTokenizer, file_path: str, block_size: int, ref_path: str):
warnings.warn(
DEPRECATION_WARNING.format(
"https://git... | class_definition | 5,593 | 7,853 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/language_modeling.py | null | 10,619 |
class LineByLineWithSOPTextDataset(Dataset):
"""
Dataset for sentence order prediction task, prepare sentence pairs for SOP task
"""
def __init__(self, tokenizer: PreTrainedTokenizer, file_dir: str, block_size: int):
warnings.warn(
DEPRECATION_WARNING.format(
"https:... | class_definition | 7,856 | 15,399 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/language_modeling.py | null | 10,620 |
class TextDatasetForNextSentencePrediction(Dataset):
"""
This will be superseded by a framework-agnostic approach soon.
"""
def __init__(
self,
tokenizer: PreTrainedTokenizer,
file_path: str,
block_size: int,
overwrite_cache=False,
short_seq_probability=0... | class_definition | 15,402 | 23,720 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/language_modeling.py | null | 10,621 |
class SquadDataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
"""
model_type: str = field(
default=None, metadata={"help": "Model type selected in the list: " + ", ".join(MODEL_TYPES)}
)
data_dir: str = field(
defa... | class_definition | 1,296 | 3,767 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/squad.py | null | 10,622 |
class Split(Enum):
train = "train"
dev = "dev" | class_definition | 3,770 | 3,824 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/squad.py | null | 10,623 |
class SquadDataset(Dataset):
"""
This will be superseded by a framework-agnostic approach soon.
"""
args: SquadDataTrainingArguments
features: List[SquadFeatures]
mode: Split
is_language_sensitive: bool
def __init__(
self,
args: SquadDataTrainingArguments,
token... | class_definition | 3,827 | 9,218 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/squad.py | null | 10,624 |
class GlueDataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
Using `HfArgumentParser` we can turn this class into argparse arguments to be able to specify them on the command
line.
"""
task_name: str = field(metadata={"help": "Th... | class_definition | 1,121 | 2,176 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/glue.py | null | 10,625 |
class Split(Enum):
train = "train"
dev = "dev"
test = "test" | class_definition | 2,179 | 2,251 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/glue.py | null | 10,626 |
class GlueDataset(Dataset):
"""
This will be superseded by a framework-agnostic approach soon.
"""
args: GlueDataTrainingArguments
output_mode: str
features: List[InputFeatures]
def __init__(
self,
args: GlueDataTrainingArguments,
tokenizer: PreTrainedTokenizerBase,... | class_definition | 2,254 | 6,162 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/glue.py | null | 10,627 |
class XnliProcessor(DataProcessor):
"""
Processor for the XNLI dataset. Adapted from
https://github.com/google-research/bert/blob/f39e881b169b9d53bea03d2d341b31707a6c052b/run_classifier.py#L207
"""
def __init__(self, language, train_language=None):
self.language = language
self.trai... | class_definition | 889 | 3,330 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/xnli.py | null | 10,628 |
class SquadProcessor(DataProcessor):
"""
Processor for the SQuAD data set. overridden by SquadV1Processor and SquadV2Processor, used by the version 1.1 and
version 2.0 of SQuAD, respectively.
"""
train_file = None
dev_file = None
def _get_example_from_tensor_dict(self, tensor_dict, evaluat... | class_definition | 21,537 | 27,206 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/squad.py | null | 10,629 |
class SquadV1Processor(SquadProcessor):
train_file = "train-v1.1.json"
dev_file = "dev-v1.1.json" | class_definition | 27,209 | 27,314 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/squad.py | null | 10,630 |
class SquadV2Processor(SquadProcessor):
train_file = "train-v2.0.json"
dev_file = "dev-v2.0.json" | class_definition | 27,317 | 27,422 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/squad.py | null | 10,631 |
class SquadExample:
"""
A single training/test example for the Squad dataset, as loaded from disk.
Args:
qas_id: The example's unique identifier
question_text: The question string
context_text: The context string
answer_text: The answer string
start_position_characte... | class_definition | 27,425 | 29,629 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/squad.py | null | 10,632 |
class SquadFeatures:
"""
Single squad example features to be fed to a model. Those features are model-specific and can be crafted from
[`~data.processors.squad.SquadExample`] using the
:method:*~transformers.data.processors.squad.squad_convert_examples_to_features* method.
Args:
input_ids: ... | class_definition | 29,632 | 32,388 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/squad.py | null | 10,633 |
class SquadResult:
"""
Constructs a SquadResult which can be used to evaluate a model's output on the SQuAD dataset.
Args:
unique_id: The unique identifier corresponding to that example.
start_logits: The logits corresponding to the start of the answer
end_logits: The logits corresp... | class_definition | 32,391 | 33,152 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/squad.py | null | 10,634 |
class OutputMode(Enum):
classification = "classification"
regression = "regression" | class_definition | 5,971 | 6,062 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/glue.py | null | 10,635 |
class MrpcProcessor(DataProcessor):
"""Processor for the MRPC data set (GLUE version)."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
warnings.warn(DEPRECATION_WARNING.format("processor"), FutureWarning)
def get_example_from_tensor_dict(self, tensor_dict):
... | class_definition | 6,065 | 7,831 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/glue.py | null | 10,636 |
class MnliProcessor(DataProcessor):
"""Processor for the MultiNLI data set (GLUE version)."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
warnings.warn(DEPRECATION_WARNING.format("processor"), FutureWarning)
def get_example_from_tensor_dict(self, tensor_dict):
... | class_definition | 7,834 | 9,610 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/glue.py | null | 10,637 |
class MnliMismatchedProcessor(MnliProcessor):
"""Processor for the MultiNLI Mismatched data set (GLUE version)."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
warnings.warn(DEPRECATION_WARNING.format("processor"), FutureWarning)
def get_dev_examples(self, data_di... | class_definition | 9,613 | 10,276 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/glue.py | null | 10,638 |
class ColaProcessor(DataProcessor):
"""Processor for the CoLA data set (GLUE version)."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
warnings.warn(DEPRECATION_WARNING.format("processor"), FutureWarning)
def get_example_from_tensor_dict(self, tensor_dict):
... | class_definition | 10,279 | 11,982 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/glue.py | null | 10,639 |
class Sst2Processor(DataProcessor):
"""Processor for the SST-2 data set (GLUE version)."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
warnings.warn(DEPRECATION_WARNING.format("processor"), FutureWarning)
def get_example_from_tensor_dict(self, tensor_dict):
... | class_definition | 11,985 | 13,664 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/glue.py | null | 10,640 |
class StsbProcessor(DataProcessor):
"""Processor for the STS-B data set (GLUE version)."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
warnings.warn(DEPRECATION_WARNING.format("processor"), FutureWarning)
def get_example_from_tensor_dict(self, tensor_dict):
... | class_definition | 13,667 | 15,364 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/glue.py | null | 10,641 |
class QqpProcessor(DataProcessor):
"""Processor for the QQP data set (GLUE version)."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
warnings.warn(DEPRECATION_WARNING.format("processor"), FutureWarning)
def get_example_from_tensor_dict(self, tensor_dict):
... | class_definition | 15,367 | 17,275 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/glue.py | null | 10,642 |
class QnliProcessor(DataProcessor):
"""Processor for the QNLI data set (GLUE version)."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
warnings.warn(DEPRECATION_WARNING.format("processor"), FutureWarning)
def get_example_from_tensor_dict(self, tensor_dict):
... | class_definition | 17,278 | 18,998 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/glue.py | null | 10,643 |
class RteProcessor(DataProcessor):
"""Processor for the RTE data set (GLUE version)."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
warnings.warn(DEPRECATION_WARNING.format("processor"), FutureWarning)
def get_example_from_tensor_dict(self, tensor_dict):
... | class_definition | 19,001 | 20,721 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/glue.py | null | 10,644 |
class WnliProcessor(DataProcessor):
"""Processor for the WNLI data set (GLUE version)."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
warnings.warn(DEPRECATION_WARNING.format("processor"), FutureWarning)
def get_example_from_tensor_dict(self, tensor_dict):
... | class_definition | 20,724 | 22,424 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/glue.py | null | 10,645 |
class InputExample:
"""
A single training/test example for simple sequence classification.
Args:
guid: Unique id for the example.
text_a: string. The untokenized text of the first sequence. For single
sequence tasks, only this sequence must be specified.
text_b: (Optiona... | class_definition | 947 | 1,795 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/utils.py | null | 10,646 |
class InputFeatures:
"""
A single set of features of data. Property names are the same names as the corresponding inputs to a model.
Args:
input_ids: Indices of input sequence tokens in the vocabulary.
attention_mask: Mask to avoid performing attention on padding token indices.
... | class_definition | 1,822 | 2,869 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/utils.py | null | 10,647 |
class DataProcessor:
"""Base class for data converters for sequence classification data sets."""
def get_example_from_tensor_dict(self, tensor_dict):
"""
Gets an example from a dict with tensorflow tensors.
Args:
tensor_dict: Keys and values should match the corresponding G... | class_definition | 2,872 | 4,458 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/utils.py | null | 10,648 |
class SingleSentenceClassificationProcessor(DataProcessor):
"""Generic processor for a single sentence classification data set."""
def __init__(self, labels=None, examples=None, mode="classification", verbose=False):
self.labels = [] if labels is None else labels
self.examples = [] if examples ... | class_definition | 4,461 | 13,828 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/processors/utils.py | null | 10,649 |
class GenerationMode(ExplicitEnum):
"""
Possible generation modes, downstream of the [`~generation.GenerationMixin.generate`] method.
"""
# Non-beam methods
CONTRASTIVE_SEARCH = "contrastive_search"
GREEDY_SEARCH = "greedy_search"
SAMPLE = "sample"
ASSISTED_GENERATION = "assisted_genera... | class_definition | 2,485 | 3,033 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/configuration_utils.py | null | 10,650 |
class GenerationConfig(PushToHubMixin):
# no-format
"""
Class that holds a configuration for a generation task. A `generate` call supports the following generation methods
for text-decoder, text-to-text, speech-to-text, and vision-to-text models:
- *greedy decoding* if `num_beams=1` and `do_sam... | class_definition | 3,036 | 72,891 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/configuration_utils.py | null | 10,651 |
class BaseWatermarkingConfig(ABC):
"""Generic watermarking config"""
@classmethod
def from_dict(cls, config_dict, **kwargs):
"""
Constructs a BaseWatermarkingConfig instance from a dictionary of parameters.
Args:
config_dict (Dict[str, Any]): Dictionary containing confi... | class_definition | 72,905 | 75,561 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/configuration_utils.py | null | 10,652 |
class WatermarkingConfig(BaseWatermarkingConfig):
"""
Class that holds arguments for watermark generation and should be passed into `GenerationConfig` during `generate`.
See [this paper](https://arxiv.org/abs/2306.04634) for more details on the arguments.
Accepts the following keys:
- greenlist... | class_definition | 75,575 | 78,980 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/configuration_utils.py | null | 10,653 |
class SynthIDTextWatermarkingConfig(BaseWatermarkingConfig):
"""
Class that holds arguments for watermark generation and should be passed into `GenerationConfig` during `generate`.
See [this paper](https://www.nature.com/articles/s41586-024-08025-4) for more details on the arguments.
Args:
ngra... | class_definition | 78,994 | 82,722 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/configuration_utils.py | null | 10,654 |
class CompileConfig(object):
"""
Class that holds arguments relative to `torch.compile` behavior, when using automatic compilation in `generate`.
See [`torch.compile`](https://pytorch.org/docs/stable/generated/torch.compile.html) for more details on the arguments.
Args:
fullgraph (`bool`, *opti... | class_definition | 82,736 | 84,724 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/configuration_utils.py | null | 10,655 |
class FlaxGreedySearchOutput(ModelOutput):
"""
Flax Base class for outputs of decoder-only generation models using greedy search.
Args:
sequences (`jnp.ndarray` of shape `(batch_size, max_length)`):
The generated sequences.
"""
sequences: jnp.ndarray = None | class_definition | 1,651 | 1,951 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py | null | 10,656 |
class FlaxSampleOutput(ModelOutput):
"""
Flax Base class for outputs of decoder-only generation models using sampling.
Args:
sequences (`jnp.ndarray` of shape `(batch_size, max_length)`):
The generated sequences.
"""
sequences: jnp.ndarray = None | class_definition | 1,977 | 2,266 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py | null | 10,657 |
class FlaxBeamSearchOutput(ModelOutput):
"""
Flax Base class for outputs of decoder-only generation models using greedy search.
Args:
sequences (`jnp.ndarray` of shape `(batch_size, max_length)`):
The generated sequences.
scores (`jnp.ndarray` of shape `(batch_size,)`):
... | class_definition | 2,292 | 2,749 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py | null | 10,658 |
class GreedyState:
cur_len: jnp.ndarray
sequences: jnp.ndarray
running_token: jnp.ndarray
is_sent_finished: jnp.ndarray
model_kwargs: Dict[str, jnp.ndarray] | class_definition | 2,775 | 2,951 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py | null | 10,659 |
class SampleState:
cur_len: jnp.ndarray
sequences: jnp.ndarray
running_token: jnp.ndarray
is_sent_finished: jnp.ndarray
prng_key: jnp.ndarray
model_kwargs: Dict[str, jnp.ndarray] | class_definition | 2,977 | 3,179 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py | null | 10,660 |
class BeamSearchState:
cur_len: jnp.ndarray
running_sequences: jnp.ndarray
running_scores: jnp.ndarray
sequences: jnp.ndarray
scores: jnp.ndarray
is_sent_finished: jnp.ndarray
model_kwargs: Dict[str, jnp.ndarray] | class_definition | 3,205 | 3,445 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py | null | 10,661 |
class FlaxGenerationMixin:
"""
A class containing all functions for auto-regressive text generation, to be used as a mixin in
[`FlaxPreTrainedModel`].
The class exposes [`~generation.FlaxGenerationMixin.generate`], which can be used for:
- *greedy decoding* by calling [`~generation.FlaxGene... | class_definition | 3,448 | 50,468 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_utils.py | null | 10,662 |
class LogitsProcessor:
"""Abstract base class for all logit processors that can be applied during generation."""
@add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
raise NotImplementedError(
... | class_definition | 1,564 | 1,992 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py | null | 10,663 |
class LogitsProcessorList(list):
"""
This class can be used to create a list of [`LogitsProcessor`] to subsequently process a `scores` input tensor.
This class inherits from list and adds a specific *__call__* method to apply each [`LogitsProcessor`] to the
inputs.
"""
def __call__(self, input_... | class_definition | 1,995 | 3,838 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py | null | 10,664 |
class MinLengthLogitsProcessor(LogitsProcessor):
r"""
[`LogitsProcessor`] enforcing a min-length by setting EOS probability to 0. Note that, for decoder-only models
like most LLMs, the length includes the prompt.
Args:
min_length (`int`):
The minimum length below which the score of ... | class_definition | 3,841 | 6,577 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py | null | 10,665 |
class MinNewTokensLengthLogitsProcessor(LogitsProcessor):
r"""
[`LogitsProcessor`] enforcing a min-length of new tokens by setting EOS (End-Of-Sequence) token probability to 0.
Contrarily to [`MinLengthLogitsProcessor`], this processor ignores the prompt.
Args:
prompt_length_to_skip (`int`):
... | class_definition | 6,580 | 9,664 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py | null | 10,666 |
class TemperatureLogitsWarper(LogitsProcessor):
r"""
[`LogitsProcessor`] for temperature (exponential scaling output probability distribution), which effectively means
that it can control the randomness of the predicted tokens. Often used together with [`TopPLogitsWarper`] and
[`TopKLogitsWarper`].
... | class_definition | 9,667 | 12,690 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py | null | 10,667 |
class RepetitionPenaltyLogitsProcessor(LogitsProcessor):
r"""
[`LogitsProcessor`] that prevents the repetition of previous tokens through a penalty. This penalty is applied at
most once per token. Note that, for decoder-only models like most LLMs, the considered tokens include the prompt.
In the origin... | class_definition | 12,693 | 15,346 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py | null | 10,668 |
class EncoderRepetitionPenaltyLogitsProcessor(LogitsProcessor):
r"""
[`LogitsProcessor`] that works similarly to [`RepetitionPenaltyLogitsProcessor`], but with an *inverse* penalty
that is applied to the tokens present in the prompt. In other words, a penalty above 1.0 increases the odds of
selecting to... | class_definition | 15,349 | 18,067 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py | null | 10,669 |
class TopPLogitsWarper(LogitsProcessor):
"""
[`LogitsProcessor`] that performs top-p, i.e. restricting to top tokens summing to prob_cut_off <= prob_cut_off.
Often used together with [`TemperatureLogitsWarper`] and [`TopKLogitsWarper`].
Args:
top_p (`float`):
If set to < 1, only the... | class_definition | 18,070 | 21,221 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py | null | 10,670 |
class TopKLogitsWarper(LogitsProcessor):
r"""
[`LogitsProcessor`] that performs top-k, i.e. restricting to the k highest probability elements. Often used
together with [`TemperatureLogitsWarper`] and [`TopPLogitsWarper`].
Args:
top_k (`int`):
The number of highest probability vocabu... | class_definition | 21,224 | 23,672 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py | null | 10,671 |
class MinPLogitsWarper(LogitsProcessor):
"""
[`LogitsProcessor`] that performs min-p, i.e. keeps all tokens that are above a minimum probability, scaled by the
probability of the most likely token. As a result, the filter becomes more agressive in the presence of
high-probability tokens, which is a sign... | class_definition | 23,675 | 27,627 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py | null | 10,672 |
class TypicalLogitsWarper(LogitsProcessor):
r"""
[`LogitsProcessor`] that performs typical decoding. Inspired on how humans use language, it prioritizes tokens
whose log probability is close to the entropy of the token probability distribution. This means that the most
likely tokens may be discarded in ... | class_definition | 27,630 | 31,796 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py | null | 10,673 |
class EpsilonLogitsWarper(LogitsProcessor):
r"""
[`LogitsProcessor`] that performs epsilon-sampling, i.e. restricting to tokens with `prob >= epsilon`. Takes the
largest min_tokens_to_keep tokens if no tokens satisfy this constraint. See [Truncation Sampling as Language Model
Desmoothing](https://arxiv.... | class_definition | 31,799 | 35,054 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py | null | 10,674 |
class EtaLogitsWarper(LogitsProcessor):
r"""
[`LogitsProcessor`] that performs eta-sampling, a technique to filter out tokens with probabilities below a dynamic
cutoff value, `eta`, which is calculated based on a combination of the hyperparameter `epsilon` and the entropy of
the token probabilities, i.e... | class_definition | 35,057 | 39,639 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py | null | 10,675 |
class NoRepeatNGramLogitsProcessor(LogitsProcessor):
r"""
N-grams are groups of "n" consecutive words, characters, or tokens taken from a sequence of text. Given the
sentence: "She runs fast", the bi-grams (n=2) would be ("she", "runs") and ("runs", "fast"). In text generation,
avoiding repetitions of w... | class_definition | 42,614 | 45,492 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py | null | 10,676 |
class EncoderNoRepeatNGramLogitsProcessor(LogitsProcessor):
r"""
[`LogitsProcessor`] that works similarly to [`NoRepeatNGramLogitsProcessor`], but applied exclusively to prevent
the repetition of n-grams present in the prompt.
It was designed to promote chattiness in a language model, by preventing the... | class_definition | 45,495 | 48,510 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py | null | 10,677 |
class SequenceBiasLogitsProcessor(LogitsProcessor):
"""
[`LogitsProcessor`] that applies an additive bias on sequences. The bias is applied to the last token of a sequence
when the next generated token can complete it. Consequently, to take the most of biasing sequences with more than
one token, conside... | class_definition | 48,513 | 57,483 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py | null | 10,678 |
class NoBadWordsLogitsProcessor(SequenceBiasLogitsProcessor):
"""
[`LogitsProcessor`] that enforces that specified sequences will never be selected.
<Tip>
In order to get the token ids of the words that should not appear in the generated text, make sure to set
`add_prefix_space=True` when initiali... | class_definition | 57,486 | 61,585 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py | null | 10,679 |
class PrefixConstrainedLogitsProcessor(LogitsProcessor):
r"""
[`LogitsProcessor`] that enforces constrained generation and is useful for prefix-conditioned constrained
generation. See [Autoregressive Entity Retrieval](https://arxiv.org/abs/2010.00904) for more information.
Args:
prefix_allowed_... | class_definition | 61,588 | 65,178 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py | null | 10,680 |
class HammingDiversityLogitsProcessor(LogitsProcessor):
r"""
[`LogitsProcessor`] that enforces diverse beam search.
Note that this logits processor is only effective for [`PreTrainedModel.group_beam_search`]. See [Diverse Beam
Search: Decoding Diverse Solutions from Neural Sequence Models](https://arxi... | class_definition | 65,181 | 72,374 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py | null | 10,681 |
class ForcedBOSTokenLogitsProcessor(LogitsProcessor):
r"""
[`LogitsProcessor`] that enforces the specified token as the first generated token. Used with encoder-decoder
models.
Args:
bos_token_id (`int`):
The id of the token to force as the first generated token.
Examples:
... | class_definition | 72,377 | 74,114 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py | null | 10,682 |
class ForcedEOSTokenLogitsProcessor(LogitsProcessor):
r"""
[`LogitsProcessor`] that enforces the specified token as the last generated token when `max_length` is reached.
Args:
max_length (`int`):
The maximum length of the sequence to be generated.
eos_token_id (`Union[int, List... | class_definition | 74,117 | 76,502 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py | null | 10,683 |
class InfNanRemoveLogitsProcessor(LogitsProcessor):
r"""
[`LogitsProcessor`] that removes all `nan` and `inf` values to avoid the generation method to fail. Note that using
the logits processor should only be used if necessary since it can slow down the generation method.
This logits processor has no `... | class_definition | 76,505 | 77,517 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py | null | 10,684 |
class ExponentialDecayLengthPenalty(LogitsProcessor):
r"""
[`LogitsProcessor`] that exponentially increases the score of the `eos_token_id` after `start_index` has been
reached. This allows generating shorter sequences without having a hard cutoff, allowing the `eos_token` to be
predicted in a meaningfu... | class_definition | 77,520 | 82,275 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py | null | 10,685 |
class LogitNormalization(LogitsProcessor):
r"""
[`LogitsProcessor`] for normalizing the scores using log-softmax. It's important to normalize
the scores during beam search, after applying the logits processors or warpers, since the search algorithm used in
this library doesn't do it (it only does it bef... | class_definition | 82,278 | 84,053 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py | null | 10,686 |
class SuppressTokensAtBeginLogitsProcessor(LogitsProcessor):
r"""
[`SuppressTokensAtBeginLogitsProcessor`] supresses a list of tokens as soon as the `generate` function starts
generating using `begin_index` tokens. This should ensure that the tokens defined by `begin_suppress_tokens` are
not generated a... | class_definition | 84,056 | 86,621 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py | null | 10,687 |
class SuppressTokensLogitsProcessor(LogitsProcessor):
r"""
This processor can be used to suppress a list of tokens. The processor will set their log probs to `-inf` so
that they are not generated. Originally created for
[Whisper](https://huggingface.co/docs/transformers/model_doc/whisper).
Examples... | class_definition | 86,624 | 88,563 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py | null | 10,688 |
class WhisperTimeStampLogitsProcessor(LogitsProcessor):
r"""
[`LogitsProcessor`] that modifies the logits for the generation of timestamps in the transcription. When the input
tokens are at a specific threshold, the processor sets the scores to negative infinity. The processor makes sure
that timestamp... | class_definition | 88,566 | 96,050 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py | null | 10,689 |
class WhisperNoSpeechDetection(LogitsProcessor):
r"""This processor can be used to detect silence when using Whisper. It should take as input unprocessed logits to follow the original implementation"""
def __init__(self, no_speech_token: int, begin_index: int, scores_is_logprobs: bool = False):
self.no... | class_definition | 96,053 | 98,245 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py | null | 10,690 |
class ClassifierFreeGuidanceLogitsProcessor(LogitsProcessor):
r"""
[`LogitsProcessor`] for classifier free guidance (CFG). The scores are split over the batch dimension,
where the first half correspond to the conditional logits (predicted from the input prompt) and the second half
correspond to the unco... | class_definition | 98,248 | 101,292 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py | null | 10,691 |
class AlternatingCodebooksLogitsProcessor(LogitsProcessor):
r"""
[`LogitsProcessor`] enforcing alternated generation between the two codebooks of Bark.
<Tip warning={true}>
This logits processor is exclusively compatible with
[Bark](https://huggingface.co/docs/transformers/en/model_doc/bark)'s fin... | class_definition | 101,295 | 103,114 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py | null | 10,692 |
class UnbatchedClassifierFreeGuidanceLogitsProcessor(LogitsProcessor):
r"""
Logits processor for Classifier-Free Guidance (CFG). The processors computes a weighted average across scores
from prompt conditional and prompt unconditional (or negative) logits, parameterized by the `guidance_scale`.
The unco... | class_definition | 103,117 | 108,999 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py | null | 10,693 |
class BarkEosPrioritizerLogitsProcessor(LogitsProcessor):
r"""This processor ensures that the EOS token is selected if its probability is greater than the `min_eos_p`.
<Tip warning={true}>
This logits processor is exclusively compatible with
[Bark](https://huggingface.co/docs/transformers/en/model_doc... | class_definition | 109,002 | 111,131 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py | null | 10,694 |
class WatermarkLogitsProcessor(LogitsProcessor):
r"""
Logits processor for watermarking generated text. The processor modifies model output scores by adding a small bias to
randomized set of "green" tokens before generating the next token. "Green" tokens selection process depends on the
`seeding_scheme`... | class_definition | 111,134 | 118,503 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py | null | 10,695 |
class SynthIDTextWatermarkState:
"""SynthID watermarking state."""
def __init__(
self,
batch_size: int,
ngram_len: int,
context_history_size: int,
device: torch.device,
):
"""Initializes the state.
Args:
batch_size (`int`): Batch size.
... | class_definition | 118,506 | 119,348 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py | null | 10,696 |
class SynthIDTextWatermarkLogitsProcessor(LogitsProcessor):
r"""
Logits processor that implements watermarking techniques for text generation models.
This class facilitates the application of SynthID text watermarking, a method for embedding imperceptible signals
into generated text to aid in detecting ... | class_definition | 119,351 | 137,792 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/logits_process.py | null | 10,697 |
class TFLogitsProcessor:
"""Abstract base class for all logit processors that can be applied during generation."""
@add_start_docstrings(TF_LOGITS_PROCESSOR_INPUTS_DOCSTRING)
def __call__(self, input_ids: tf.Tensor, scores: tf.Tensor, cur_len: int) -> tf.Tensor:
"""TF method for processing logits."... | class_definition | 2,011 | 2,482 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_logits_process.py | null | 10,698 |
class TFLogitsWarper:
"""Abstract base class for all logit warpers that can be applied during generation with multinomial sampling."""
@add_start_docstrings(TF_LOGITS_PROCESSOR_INPUTS_DOCSTRING)
def __call__(self, input_ids: tf.Tensor, scores: tf.Tensor, cur_len: int) -> tf.Tensor:
"""TF method for... | class_definition | 2,485 | 2,973 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_logits_process.py | null | 10,699 |
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