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
values | class_index int64 0 10.8k |
|---|---|---|---|---|---|---|---|
class EvalPrediction:
"""
Evaluation output (always contains labels), to be used to compute metrics.
Parameters:
predictions (`np.ndarray`): Predictions of the model.
label_ids (`np.ndarray`): Targets to be matched.
inputs (`np.ndarray`, *optional*): Input data passed to the model.
... | class_definition | 5,118 | 6,410 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_utils.py | null | 100 |
class EvalLoopOutput(NamedTuple):
predictions: Union[np.ndarray, Tuple[np.ndarray]]
label_ids: Optional[Union[np.ndarray, Tuple[np.ndarray]]]
metrics: Optional[Dict[str, float]]
num_samples: Optional[int] | class_definition | 6,413 | 6,633 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_utils.py | null | 101 |
class PredictionOutput(NamedTuple):
predictions: Union[np.ndarray, Tuple[np.ndarray]]
label_ids: Optional[Union[np.ndarray, Tuple[np.ndarray]]]
metrics: Optional[Dict[str, float]] | class_definition | 6,636 | 6,827 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_utils.py | null | 102 |
class TrainOutput(NamedTuple):
global_step: int
training_loss: float
metrics: Dict[str, float] | class_definition | 6,830 | 6,936 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_utils.py | null | 103 |
class IntervalStrategy(ExplicitEnum):
NO = "no"
STEPS = "steps"
EPOCH = "epoch" | class_definition | 7,434 | 7,525 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_utils.py | null | 104 |
class SaveStrategy(ExplicitEnum):
NO = "no"
STEPS = "steps"
EPOCH = "epoch"
BEST = "best" | class_definition | 7,528 | 7,633 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_utils.py | null | 105 |
class EvaluationStrategy(ExplicitEnum):
NO = "no"
STEPS = "steps"
EPOCH = "epoch" | class_definition | 7,636 | 7,729 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_utils.py | null | 106 |
class HubStrategy(ExplicitEnum):
END = "end"
EVERY_SAVE = "every_save"
CHECKPOINT = "checkpoint"
ALL_CHECKPOINTS = "all_checkpoints" | class_definition | 7,732 | 7,880 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_utils.py | null | 107 |
class BestRun(NamedTuple):
"""
The best run found by a hyperparameter search (see [`~Trainer.hyperparameter_search`]).
Parameters:
run_id (`str`):
The id of the best run (if models were saved, the corresponding checkpoint will be in the folder ending
with run-{run_id}).
... | class_definition | 7,883 | 8,663 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_utils.py | null | 108 |
class HPSearchBackend(ExplicitEnum):
OPTUNA = "optuna"
RAY = "ray"
SIGOPT = "sigopt"
WANDB = "wandb" | class_definition | 11,757 | 11,873 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_utils.py | null | 109 |
class SchedulerType(ExplicitEnum):
"""
Scheduler names for the parameter `lr_scheduler_type` in [`TrainingArguments`].
By default, it uses "linear". Internally, this retrieves `get_linear_schedule_with_warmup` scheduler from [`Trainer`].
Scheduler types:
- "linear" = get_linear_schedule_with_warm... | class_definition | 13,839 | 15,104 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_utils.py | null | 110 |
class TrainerMemoryTracker:
"""
A helper class that tracks cpu and gpu memory.
This class will silently skip unless `psutil` is available. Install with `pip install psutil`.
When a stage completes, it can pass metrics dict to update with the memory metrics gathered during this stage.
Example :
... | class_definition | 15,107 | 26,652 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_utils.py | null | 111 |
class FSDPOption(ExplicitEnum):
FULL_SHARD = "full_shard"
SHARD_GRAD_OP = "shard_grad_op"
NO_SHARD = "no_shard"
HYBRID_SHARD = "hybrid_shard"
HYBRID_SHARD_ZERO2 = "hybrid_shard_zero2"
OFFLOAD = "offload"
AUTO_WRAP = "auto_wrap" | class_definition | 29,159 | 29,414 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_utils.py | null | 112 |
class RemoveColumnsCollator:
"""Wrap the data collator to remove unused columns before they are passed to the collator."""
def __init__(
self,
data_collator,
signature_columns,
logger=None,
model_name: Optional[str] = None,
description: Optional[str] = None,
... | class_definition | 29,417 | 31,116 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_utils.py | null | 113 |
class HfArgumentParser(ArgumentParser):
"""
This subclass of `argparse.ArgumentParser` uses type hints on dataclasses to generate arguments.
The class is designed to play well with the native argparse. In particular, you can add more (non-dataclass backed)
arguments to the parser after initialization a... | class_definition | 4,276 | 20,377 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/hf_argparser.py | null | 114 |
class TextKwargs(TypedDict, total=False):
"""
Keyword arguments for text processing. For extended documentation, check out tokenization_utils_base methods and
docstrings associated.
Attributes:
add_special_tokens (`bool`, *optional*)
Whether or not to add special tokens when encodin... | class_definition | 2,088 | 5,006 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/processing_utils.py | null | 115 |
class ImagesKwargs(TypedDict, total=False):
"""
Keyword arguments for image processing. For extended documentation, check the appropriate ImageProcessor
class methods and docstrings.
Attributes:
do_resize (`bool`, *optional*):
Whether to resize the image.
size (`Dict[str, in... | class_definition | 5,009 | 7,547 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/processing_utils.py | null | 116 |
class VideosKwargs(TypedDict, total=False):
"""
Keyword arguments for video processing.
Attributes:
do_resize (`bool`):
Whether to resize the image.
size (`Dict[str, int]`, *optional*):
Resize the shorter side of the input to `size["shortest_edge"]`.
size_div... | class_definition | 7,550 | 9,650 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/processing_utils.py | null | 117 |
class AudioKwargs(TypedDict, total=False):
"""
Keyword arguments for audio processing.
Attributes:
sampling_rate (`int`, *optional*):
The sampling rate at which the `raw_speech` input was sampled.
raw_speech (`np.ndarray`, `List[float]`, `List[np.ndarray]`, `List[List[float]]`):... | class_definition | 9,653 | 11,731 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/processing_utils.py | null | 118 |
class CommonKwargs(TypedDict, total=False):
return_tensors: Optional[Union[str, TensorType]] | class_definition | 11,734 | 11,830 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/processing_utils.py | null | 119 |
class ProcessingKwargs(TextKwargs, ImagesKwargs, VideosKwargs, AudioKwargs, CommonKwargs, total=False):
"""
Base class for kwargs passing to processors.
A model should have its own `ModelProcessorKwargs` class that inherits from `ProcessingKwargs` to provide:
1) Additional typed keys and that this m... | class_definition | 11,833 | 13,668 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/processing_utils.py | null | 120 |
class ChatTemplateKwargs(TypedDict, total=False):
"""
Keyword arguments for processor chat templates.
tokenize (`bool`, *optional*, defaults to `False`):
Whether to tokenize the output or not.
return_dict (`bool`, defaults to `False`):
Whether to return a dictionary with named outputs. ... | class_definition | 13,671 | 17,208 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/processing_utils.py | null | 121 |
class AllKwargsForChatTemplate(
TextKwargs, ImagesKwargs, VideosKwargs, AudioKwargs, CommonKwargs, ChatTemplateKwargs
): ... | class_definition | 17,211 | 17,339 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/processing_utils.py | null | 122 |
class ProcessorMixin(PushToHubMixin):
"""
This is a mixin used to provide saving/loading functionality for all processor classes.
"""
attributes = ["feature_extractor", "tokenizer"]
optional_attributes = ["chat_template"]
optional_call_args: List[str] = []
# Names need to be attr_class for ... | class_definition | 17,342 | 57,730 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/processing_utils.py | null | 123 |
class BatchFeature(BaseBatchFeature):
r"""
Holds the output of the image processor specific `__call__` methods.
This class is derived from a python dictionary and can be used as a dictionary.
Args:
data (`dict`):
Dictionary of lists/arrays/tensors returned by the __call__ method ('... | class_definition | 1,522 | 2,081 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/image_processing_base.py | null | 124 |
class ImageProcessingMixin(PushToHubMixin):
"""
This is an image processor mixin used to provide saving/loading functionality for sequential and image feature
extractors.
"""
_auto_class = None
def __init__(self, **kwargs):
"""Set elements of `kwargs` as attributes."""
# This k... | class_definition | 2,162 | 25,084 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/image_processing_base.py | null | 125 |
class Seq2SeqTrainingArguments(TrainingArguments):
"""
Args:
predict_with_generate (`bool`, *optional*, defaults to `False`):
Whether to use generate to calculate generative metrics (ROUGE, BLEU).
generation_max_length (`int`, *optional*):
The `max_length` to use on each ... | class_definition | 971 | 3,895 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/training_args_seq2seq.py | null | 126 |
class ModelCard:
r"""
Structured Model Card class. Store model card as well as methods for loading/downloading/saving model cards.
Please read the following paper for details and explanation on the sections: "Model Cards for Model Reporting" by
Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barn... | class_definition | 2,944 | 10,867 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modelcard.py | null | 127 |
class TrainingSummary:
model_name: str
language: Optional[Union[str, List[str]]] = None
license: Optional[str] = None
tags: Optional[Union[str, List[str]]] = None
finetuned_from: Optional[str] = None
tasks: Optional[Union[str, List[str]]] = None
dataset: Optional[Union[str, List[str]]] = Non... | class_definition | 13,723 | 27,456 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modelcard.py | null | 128 |
class AdamW(Optimizer):
"""
Implements Adam algorithm with weight decay fix as introduced in [Decoupled Weight Decay
Regularization](https://arxiv.org/abs/1711.05101).
Parameters:
params (`Iterable[nn.parameter.Parameter]`):
Iterable of parameters to optimize or dictionaries definin... | class_definition | 22,633 | 27,872 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/optimization.py | null | 129 |
class Adafactor(Optimizer):
"""
AdaFactor pytorch implementation can be used as a drop in replacement for Adam original fairseq code:
https://github.com/pytorch/fairseq/blob/master/fairseq/optim/adafactor.py
Paper: *Adafactor: Adaptive Learning Rates with Sublinear Memory Cost* https://arxiv.org/abs/18... | class_definition | 27,875 | 37,642 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/optimization.py | null | 130 |
class AdafactorSchedule(LambdaLR):
"""
Since [`~optimization.Adafactor`] performs its own scheduling, if the training loop relies on a scheduler (e.g.,
for logging), this class creates a proxy object that retrieves the current lr values from the optimizer.
It returns `initial_lr` during startup and the... | class_definition | 37,645 | 38,668 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/optimization.py | null | 131 |
class TFTrainingArguments(TrainingArguments):
"""
TrainingArguments is the subset of the arguments we use in our example scripts **which relate to the training loop
itself**.
Using [`HfArgumentParser`] we can turn this class into
[argparse](https://docs.python.org/3/library/argparse#module-argparse... | class_definition | 971 | 14,571 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/training_args_tf.py | null | 132 |
class SentencePieceExtractor:
"""
Extractor implementation for SentencePiece trained models. https://github.com/google/sentencepiece
"""
def __init__(self, model: str):
requires_backends(self, "sentencepiece")
from sentencepiece import SentencePieceProcessor
self.sp = SentenceP... | class_definition | 2,936 | 3,780 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py | null | 133 |
class GemmaSentencePieceExtractor(SentencePieceExtractor):
def extract(self, vocab_scores=None) -> Tuple[Dict[str, int], List[Tuple]]:
"""
By default will return vocab and merges with respect to their order, by sending `vocab_scores` we're going to
order the merges with respect to the piece ... | class_definition | 3,783 | 4,497 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py | null | 134 |
class Converter:
def __init__(self, original_tokenizer):
self.original_tokenizer = original_tokenizer
def converted(self) -> Tokenizer:
raise NotImplementedError() | class_definition | 4,619 | 4,807 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py | null | 135 |
class BertConverter(Converter):
def converted(self) -> Tokenizer:
vocab = self.original_tokenizer.vocab
tokenizer = Tokenizer(WordPiece(vocab, unk_token=str(self.original_tokenizer.unk_token)))
tokenize_chinese_chars = False
strip_accents = False
do_lower_case = False
... | class_definition | 4,810 | 6,360 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py | null | 136 |
class SplinterConverter(Converter):
def converted(self) -> Tokenizer:
vocab = self.original_tokenizer.vocab
tokenizer = Tokenizer(WordPiece(vocab, unk_token=str(self.original_tokenizer.unk_token)))
tokenize_chinese_chars = False
strip_accents = False
do_lower_case = False
... | class_definition | 6,363 | 8,415 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py | null | 137 |
class FunnelConverter(Converter):
def converted(self) -> Tokenizer:
vocab = self.original_tokenizer.vocab
tokenizer = Tokenizer(WordPiece(vocab, unk_token=str(self.original_tokenizer.unk_token)))
tokenize_chinese_chars = False
strip_accents = False
do_lower_case = False
... | class_definition | 8,418 | 10,015 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py | null | 138 |
class MPNetConverter(Converter):
def converted(self) -> Tokenizer:
vocab = self.original_tokenizer.vocab
tokenizer = Tokenizer(WordPiece(vocab, unk_token=str(self.original_tokenizer.unk_token)))
tokenize_chinese_chars = False
strip_accents = False
do_lower_case = False
... | class_definition | 10,018 | 11,608 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py | null | 139 |
class OpenAIGPTConverter(Converter):
def converted(self) -> Tokenizer:
vocab = self.original_tokenizer.encoder
merges = list(self.original_tokenizer.bpe_ranks.keys())
unk_token = self.original_tokenizer.unk_token
tokenizer = Tokenizer(
BPE(
vocab=vocab,
... | class_definition | 11,611 | 12,485 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py | null | 140 |
class GPT2Converter(Converter):
def converted(self, vocab: Dict[str, int] = None, merges: List[Tuple[str, str]] = None) -> Tokenizer:
if not vocab:
vocab = self.original_tokenizer.encoder
if not merges:
merges = list(self.original_tokenizer.bpe_ranks)
tokenizer = Tok... | class_definition | 12,488 | 13,972 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py | null | 141 |
class HerbertConverter(Converter):
def converted(self) -> Tokenizer:
tokenizer_info_str = "#version:"
token_suffix = "</w>"
vocab = self.original_tokenizer.encoder
merges = list(self.original_tokenizer.bpe_ranks.keys())
if tokenizer_info_str in merges[0][0]:
merg... | class_definition | 13,975 | 15,084 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py | null | 142 |
class Qwen2Converter(Converter):
def converted(self, vocab: Dict[str, int] = None, merges: List[Tuple[str, str]] = None) -> Tokenizer:
if not vocab:
vocab = self.original_tokenizer.encoder
if not merges:
merges = list(self.original_tokenizer.bpe_ranks.keys())
tokeniz... | class_definition | 15,087 | 16,559 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py | null | 143 |
class RobertaConverter(Converter):
def converted(self) -> Tokenizer:
ot = self.original_tokenizer
vocab = ot.encoder
merges = list(ot.bpe_ranks.keys())
tokenizer = Tokenizer(
BPE(
vocab=vocab,
merges=merges,
dropout=None,
... | class_definition | 16,562 | 17,491 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py | null | 144 |
class RoFormerConverter(Converter):
def converted(self) -> Tokenizer:
from .models.roformer.tokenization_utils import JiebaPreTokenizer
vocab = self.original_tokenizer.vocab
tokenizer = Tokenizer(WordPiece(vocab, unk_token=str(self.original_tokenizer.unk_token)))
strip_accents = Fa... | class_definition | 17,494 | 18,994 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py | null | 145 |
class DebertaConverter(Converter):
def converted(self) -> Tokenizer:
ot = self.original_tokenizer
vocab = ot.encoder
merges = list(ot.bpe_ranks.keys())
tokenizer = Tokenizer(
BPE(
vocab=vocab,
merges=merges,
dropout=None,
... | class_definition | 18,997 | 20,012 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py | null | 146 |
class SpmConverter(Converter):
handle_byte_fallback = False
SpmExtractor = SentencePieceExtractor
special_tokens = {}
def __init__(self, *args):
requires_backends(self, "protobuf")
super().__init__(*args)
# from .utils import sentencepiece_model_pb2 as model_pb2
model_... | class_definition | 20,015 | 25,015 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py | null | 147 |
class AlbertConverter(SpmConverter):
def vocab(self, proto):
return [
(piece.piece, piece.score) if check_number_comma(piece.piece) else (piece.piece, piece.score - 100)
for piece in proto.pieces
]
def normalizer(self, proto):
list_normalizers = [
nor... | class_definition | 25,018 | 26,419 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py | null | 148 |
class BarthezConverter(SpmConverter):
def unk_id(self, proto):
unk_id = 3
return unk_id
def post_processor(self):
return processors.TemplateProcessing(
single="<s> $A </s>",
pair="<s> $A </s> </s> $B </s>",
special_tokens=[
("<s>", sel... | class_definition | 26,422 | 26,899 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py | null | 149 |
class CamembertConverter(SpmConverter):
def vocab(self, proto):
vocab = [
("<s>NOTUSED", 0.0),
("<pad>", 0.0),
("</s>NOTUSED", 0.0),
("<unk>", 0.0),
("<unk>NOTUSED", -100),
]
# We down-grade the original SentencePiece by -100 to avo... | class_definition | 26,902 | 27,847 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py | null | 150 |
class DebertaV2Converter(SpmConverter):
def pre_tokenizer(self, replacement, add_prefix_space):
list_pretokenizers = []
if self.original_tokenizer.split_by_punct:
list_pretokenizers.append(pre_tokenizers.Punctuation(behavior="isolated"))
prepend_scheme = _get_prepend_scheme(add_p... | class_definition | 27,850 | 29,324 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py | null | 151 |
class MBartConverter(SpmConverter):
def vocab(self, proto):
vocab = [
("<s>", 0.0),
("<pad>", 0.0),
("</s>", 0.0),
("<unk>", 0.0),
]
vocab += [(piece.piece, piece.score) for piece in proto.pieces[3:]]
vocab += [
("ar_AR", 0.... | class_definition | 29,327 | 30,803 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py | null | 152 |
class MBart50Converter(SpmConverter):
def vocab(self, proto):
vocab = [
("<s>", 0.0),
("<pad>", 0.0),
("</s>", 0.0),
("<unk>", 0.0),
]
vocab += [(piece.piece, piece.score) for piece in proto.pieces[3:]]
vocab += [("ar_AR", 0.0), ("cs_CZ... | class_definition | 30,806 | 32,418 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py | null | 153 |
class NllbConverter(SpmConverter):
def vocab(self, proto):
vocab = [
("<s>", 0.0),
("<pad>", 0.0),
("</s>", 0.0),
("<unk>", 0.0),
]
vocab += [(piece.piece, piece.score) for piece in proto.pieces[3:]]
return vocab
def unk_id(self, p... | class_definition | 32,421 | 33,144 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py | null | 154 |
class SeamlessM4TConverter(SpmConverter):
def vocab(self, proto):
vocab = [
("<pad>", 0.0),
("<unk>", 0.0),
("<s>", 0.0),
("</s>", 0.0),
]
vocab += [(piece.piece, piece.score) for piece in proto.pieces[3:]]
return vocab
def unk_id(... | class_definition | 33,147 | 33,908 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py | null | 155 |
class XLMRobertaConverter(SpmConverter):
def vocab(self, proto):
vocab = [
("<s>", 0.0),
("<pad>", 0.0),
("</s>", 0.0),
("<unk>", 0.0),
]
vocab += [(piece.piece, piece.score) for piece in proto.pieces[3:]]
vocab += [("<mask>", 0.0)]
... | class_definition | 33,911 | 34,689 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py | null | 156 |
class XLNetConverter(SpmConverter):
def vocab(self, proto):
return [
(piece.piece, piece.score) if check_number_comma(piece.piece) else (piece.piece, piece.score - 100)
for piece in proto.pieces
]
def normalizer(self, proto):
list_normalizers = [
norm... | class_definition | 34,692 | 36,092 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py | null | 157 |
class ReformerConverter(SpmConverter):
pass | class_definition | 36,095 | 36,142 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py | null | 158 |
class RemBertConverter(SpmConverter):
# Inspired from AlbertConverter
def normalizer(self, proto):
list_normalizers = [
normalizers.Replace("``", '"'),
normalizers.Replace("''", '"'),
normalizers.Replace(Regex(" {2,}"), " "),
]
if not self.original_tok... | class_definition | 36,145 | 37,357 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py | null | 159 |
class BertGenerationConverter(SpmConverter):
pass | class_definition | 37,360 | 37,413 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py | null | 160 |
class PegasusConverter(SpmConverter):
def vocab(self, proto):
vocab = [
(self.original_tokenizer.pad_token, 0.0),
(self.original_tokenizer.eos_token, 0.0),
]
if self.original_tokenizer.mask_token_sent is not None:
vocab += [(self.original_tokenizer.mask_t... | class_definition | 37,416 | 38,950 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py | null | 161 |
class T5Converter(SpmConverter):
def vocab(self, proto):
num_extra_ids = self.original_tokenizer._extra_ids
vocab = [(piece.piece, piece.score) for piece in proto.pieces]
vocab += [(f"<extra_id_{i}>", 0.0) for i in range(num_extra_ids - 1, -1, -1)]
return vocab
def post_processo... | class_definition | 38,953 | 39,544 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py | null | 162 |
class UdopConverter(SpmConverter):
def post_processor(self):
return processors.TemplateProcessing(
single=["$A", "</s>"],
pair=["$A", "</s>", "$B", "</s>"],
special_tokens=[
("</s>", self.original_tokenizer.convert_tokens_to_ids("</s>")),
],
... | class_definition | 39,547 | 39,874 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py | null | 163 |
class WhisperConverter(Converter):
def converted(self) -> Tokenizer:
vocab = self.original_tokenizer.encoder
merges = list(self.original_tokenizer.bpe_ranks.keys())
tokenizer = Tokenizer(
BPE(
vocab=vocab,
merges=merges,
dropout=No... | class_definition | 39,877 | 41,183 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py | null | 164 |
class BigBirdConverter(SpmConverter):
def post_processor(self):
return processors.TemplateProcessing(
single="[CLS]:0 $A:0 [SEP]:0",
pair="[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1",
special_tokens=[
("[CLS]", self.original_tokenizer.convert_tokens_to_ids("[CLS]")... | class_definition | 41,186 | 41,616 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py | null | 165 |
class CLIPConverter(Converter):
def converted(self) -> Tokenizer:
vocab = self.original_tokenizer.encoder
merges = list(self.original_tokenizer.bpe_ranks.keys())
unk_token = self.original_tokenizer.unk_token
tokenizer = Tokenizer(
BPE(
vocab=vocab,
... | class_definition | 41,619 | 43,185 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py | null | 166 |
class LayoutLMv2Converter(Converter):
def converted(self) -> Tokenizer:
vocab = self.original_tokenizer.vocab
tokenizer = Tokenizer(WordPiece(vocab, unk_token=str(self.original_tokenizer.unk_token)))
tokenize_chinese_chars = False
strip_accents = False
do_lower_case = True
... | class_definition | 43,188 | 44,743 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py | null | 167 |
class BlenderbotConverter(Converter):
def converted(self) -> Tokenizer:
ot = self.original_tokenizer
vocab = ot.encoder
merges = list(ot.bpe_ranks.keys())
tokenizer = Tokenizer(
BPE(
vocab=vocab,
merges=merges,
dropout=None... | class_definition | 44,746 | 45,595 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py | null | 168 |
class XGLMConverter(SpmConverter):
def vocab(self, proto):
vocab = [
("<s>", 0.0),
("<pad>", 0.0),
("</s>", 0.0),
("<unk>", 0.0),
]
vocab += [(piece.piece, piece.score) for piece in proto.pieces[3:]]
vocab += [("<madeupword0>", 0.0), ("... | class_definition | 45,598 | 46,526 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py | null | 169 |
class GemmaConverter(SpmConverter):
handle_byte_fallback = True
SpmExtractor = GemmaSentencePieceExtractor
# start and end of turn tokens must be marked as special
special_tokens = {"<start_of_turn>", "<end_of_turn>"}
""""
split_by_unicode_script: true
split_by_number: true
split_by_whi... | class_definition | 46,529 | 48,035 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py | null | 170 |
class LlamaConverter(SpmConverter):
handle_byte_fallback = True
def vocab(self, proto):
vocab = [
(self.original_tokenizer.convert_ids_to_tokens(0), 0.0),
(self.original_tokenizer.convert_ids_to_tokens(1), 0.0),
(self.original_tokenizer.convert_ids_to_tokens(2), 0.0)... | class_definition | 48,038 | 49,802 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py | null | 171 |
class MarkupLMConverter(Converter):
def converted(self) -> Tokenizer:
ot = self.original_tokenizer
vocab = ot.encoder
merges = list(ot.bpe_ranks.keys())
tokenizer = Tokenizer(
BPE(
vocab=vocab,
merges=merges,
dropout=None,
... | class_definition | 49,805 | 51,004 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py | null | 172 |
class MoshiConverter(SpmConverter):
handle_byte_fallback = True
def __init__(self, vocab_file, model_max_length=None, **kwargs):
requires_backends(self, "protobuf")
Converter.__init__(self, vocab_file)
# from .utils import sentencepiece_model_pb2 as model_pb2
model_pb2 = impor... | class_definition | 51,007 | 52,408 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py | null | 173 |
class HeliumConverter(SpmConverter):
handle_byte_fallback = True
def __init__(self, vocab_file=None, *args):
requires_backends(self, "protobuf")
Converter.__init__(self, vocab_file)
model_pb2 = import_protobuf()
m = model_pb2.ModelProto()
with open(vocab_file, "rb") a... | class_definition | 52,411 | 55,282 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py | null | 174 |
class TikTokenConverter:
"""
A general tiktoken converter.
"""
def __init__(
self,
vocab_file=None,
pattern=r"""(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+""",
add_prefix_space=False,
additio... | class_definition | 56,315 | 59,177 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py | null | 175 |
class BaseImageProcessor(ImageProcessingMixin):
def __init__(self, **kwargs):
super().__init__(**kwargs)
def __call__(self, images, **kwargs) -> BatchFeature:
"""Preprocess an image or a batch of images."""
return self.preprocess(images, **kwargs)
def preprocess(self, images, **kwa... | class_definition | 1,000 | 7,073 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/image_processing_utils.py | null | 176 |
class SequenceFeatureExtractor(FeatureExtractionMixin):
"""
This is a general feature extraction class for speech recognition.
Args:
feature_size (`int`):
The feature dimension of the extracted features.
sampling_rate (`int`):
The sampling rate at which the audio fil... | class_definition | 985 | 18,306 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/feature_extraction_sequence_utils.py | null | 177 |
class OptimizerNames(ExplicitEnum):
"""
Stores the acceptable string identifiers for optimizers.
"""
ADAMW_HF = "adamw_hf"
ADAMW_TORCH = "adamw_torch"
ADAMW_TORCH_FUSED = "adamw_torch_fused"
ADAMW_TORCH_XLA = "adamw_torch_xla"
ADAMW_TORCH_NPU_FUSED = "adamw_torch_npu_fused"
ADAMW_AP... | class_definition | 4,378 | 5,940 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/training_args.py | null | 178 |
class TrainingArguments:
"""
TrainingArguments is the subset of the arguments we use in our example scripts **which relate to the training loop
itself**.
Using [`HfArgumentParser`] we can turn this class into
[argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can ... | class_definition | 7,264 | 158,184 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/training_args.py | null | 179 |
class ParallelMode(Enum):
NOT_PARALLEL = "not_parallel"
NOT_DISTRIBUTED = "not_distributed"
DISTRIBUTED = "distributed"
SAGEMAKER_MODEL_PARALLEL = "sagemaker_model_parallel"
SAGEMAKER_DATA_PARALLEL = "sagemaker_data_parallel"
TPU = "tpu" | class_definition | 158,187 | 158,448 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/training_args.py | null | 180 |
class GGUFTensor(NamedTuple):
weights: np.ndarray
name: str
metadata: dict | class_definition | 1,675 | 1,761 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_gguf_pytorch_utils.py | null | 181 |
class TensorProcessor:
def __init__(self, config=None):
self.config = config or {}
def process(self, weights, name, **kwargs):
return GGUFTensor(weights, name, {}) | class_definition | 1,764 | 1,952 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_gguf_pytorch_utils.py | null | 182 |
class LlamaTensorProcessor(TensorProcessor):
def __init__(self, config=None):
super().__init__(config=config)
def process(self, weights, name, **kwargs):
if ".attn_k." in name or ".attn_q." in name:
num_heads = self.config.get("num_attention_heads")
num_kv_heads = self.c... | class_definition | 1,955 | 3,283 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_gguf_pytorch_utils.py | null | 183 |
class Qwen2MoeTensorProcessor(TensorProcessor):
def __init__(self, config=None):
super().__init__(config=config)
def process(self, weights, name, **kwargs):
if "_exp" in name:
tensor_key_mapping = kwargs.get("tensor_key_mapping")
parsed_parameters = kwargs.get("parsed_pa... | class_definition | 3,286 | 4,682 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_gguf_pytorch_utils.py | null | 184 |
class BloomTensorProcessor(TensorProcessor):
def __init__(self, config=None):
super().__init__(config=config)
def process(self, weights, name, **kwargs):
if "attn_qkv" in name:
num_heads = self.config["n_head"]
n_embed = self.config["hidden_size"]
if "weight"... | class_definition | 4,685 | 6,381 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_gguf_pytorch_utils.py | null | 185 |
class T5TensorProcessor(TensorProcessor):
def __init__(self, config=None):
super().__init__(config=config)
def process(self, weights, name, **kwargs):
bid = None
for chunk in name.split("."):
if chunk.isdigit():
bid = int(chunk)
break
... | class_definition | 6,384 | 6,750 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_gguf_pytorch_utils.py | null | 186 |
class GPT2TensorProcessor(TensorProcessor):
def __init__(self, config=None):
super().__init__(config=config)
def process(self, weights, name, **kwargs):
# Original transpose implementation
# https://github.com/ggerganov/llama.cpp/blob/a38b884c6c4b0c256583acfaaabdf556c62fabea/convert_hf_... | class_definition | 6,753 | 7,853 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_gguf_pytorch_utils.py | null | 187 |
class MambaTensorProcessor(TensorProcessor):
def __init__(self, config=None):
super().__init__(config=config)
def process(self, weights, name, **kwargs):
if "ssm_conv1d.weight" in name:
# for compatibility tensor ssm_conv1d must be (5120, 1, 4]) dim,
# quantized one is (... | class_definition | 7,856 | 8,499 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_gguf_pytorch_utils.py | null | 188 |
class NemotronTensorProcessor(TensorProcessor):
def __init__(self, config=None):
super().__init__(config=config)
# ref : https://github.com/ggerganov/llama.cpp/blob/master/convert_hf_to_gguf.py#L4666
def process(self, weights, name, **kwargs):
if "norm.weight" in name:
weights =... | class_definition | 8,502 | 8,879 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_gguf_pytorch_utils.py | null | 189 |
class Gemma2TensorProcessor(TensorProcessor):
def __init__(self, config=None):
super().__init__(config=config)
# ref: https://github.com/ggerganov/llama.cpp/blob/d79d8f39b4da6deca4aea8bf130c6034c482b320/convert_hf_to_gguf.py#L3191
# ref: https://github.com/huggingface/transformers/blob/fc37f3891537... | class_definition | 8,882 | 9,443 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_gguf_pytorch_utils.py | null | 190 |
class FlaxBaseModelOutput(ModelOutput):
"""
Base class for model's outputs, with potential hidden states and attentions.
Args:
last_hidden_state (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.... | class_definition | 741 | 2,075 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py | null | 191 |
class FlaxBaseModelOutputWithNoAttention(ModelOutput):
"""
Base class for model's outputs, with potential hidden states.
Args:
last_hidden_state (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)`):
Sequence of hidden-states at the output of the last layer of the model.
... | class_definition | 2,101 | 3,005 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py | null | 192 |
class FlaxBaseModelOutputWithPoolingAndNoAttention(ModelOutput):
"""
Base class for model's outputs that also contains a pooling of the last hidden states.
Args:
last_hidden_state (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)`):
Sequence of hidden-states at the outp... | class_definition | 3,031 | 4,173 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py | null | 193 |
class FlaxImageClassifierOutputWithNoAttention(ModelOutput):
"""
Base class for outputs of image classification models.
Args:
logits (`jnp.ndarray` of shape `(batch_size, config.num_labels)`):
Classification (or regression if config.num_labels==1) scores (before SoftMax).
hidden... | class_definition | 4,199 | 5,069 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py | null | 194 |
class FlaxBaseModelOutputWithPast(ModelOutput):
"""
Base class for model's outputs, with potential hidden states and attentions.
Args:
last_hidden_state (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of th... | class_definition | 5,095 | 6,790 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py | null | 195 |
class FlaxBaseModelOutputWithPooling(ModelOutput):
"""
Base class for model's outputs that also contains a pooling of the last hidden states.
Args:
last_hidden_state (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the las... | class_definition | 6,816 | 8,589 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py | null | 196 |
class FlaxBaseModelOutputWithPoolingAndCrossAttentions(ModelOutput):
"""
Base class for model's outputs that also contains a pooling of the last hidden states.
Args:
last_hidden_state (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the... | class_definition | 8,615 | 12,042 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py | null | 197 |
class FlaxBaseModelOutputWithPastAndCrossAttentions(ModelOutput):
"""
Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding).
Args:
last_hidden_state (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidd... | class_definition | 12,068 | 15,024 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py | null | 198 |
class FlaxSeq2SeqModelOutput(ModelOutput):
"""
Base class for model encoder's outputs that also contains : pre-computed hidden states that can speed up sequential
decoding.
Args:
last_hidden_state (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hid... | class_definition | 15,050 | 19,205 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_outputs.py | null | 199 |
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