text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
use of the past hidden states for their predictions. If this argument is set to a positive int, the
`Trainer` will use the corresponding output (usually index 2) as the past state and feed it to the model at
the next training step under the keyword argument `mems`.
tpu_name (`str`, *opti... | 132 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/training_args_tf.py |
framework = "tf"
tpu_name: Optional[str] = field(
default=None,
metadata={"help": "Name of TPU"},
)
tpu_zone: Optional[str] = field(
default=None,
metadata={"help": "Zone of TPU"},
)
gcp_project: Optional[str] = field(
default=None,
metadata={"help":... | 132 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/training_args_tf.py |
if self.no_cuda:
strategy = tf.distribute.OneDeviceStrategy(device="/cpu:0")
else:
try:
if self.tpu_name:
tpu = tf.distribute.cluster_resolver.TPUClusterResolver(
self.tpu_name, zone=self.tpu_zone, project=self.gcp_project
... | 132 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/training_args_tf.py |
elif len(gpus) == 0:
strategy = tf.distribute.OneDeviceStrategy(device="/cpu:0")
elif len(gpus) == 1:
strategy = tf.distribute.OneDeviceStrategy(device="/gpu:0")
elif len(gpus) > 1:
# If you only want to use a specific subset of GPUs use `CUDA_VISI... | 132 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/training_args_tf.py |
@property
def n_replicas(self) -> int:
"""
The number of replicas (CPUs, GPUs or TPU cores) used in this training.
"""
requires_backends(self, ["tf"])
return self._setup_strategy.num_replicas_in_sync
@property
def should_log(self):
"""
Whether or not ... | 132 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/training_args_tf.py |
@property
def train_batch_size(self) -> int:
"""
The actual batch size for training (may differ from `per_gpu_train_batch_size` in distributed training).
"""
if self.per_gpu_train_batch_size:
logger.warning(
"Using deprecated `--per_gpu_train_batch_size` a... | 132 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/training_args_tf.py |
@property
def eval_batch_size(self) -> int:
"""
The actual batch size for evaluation (may differ from `per_gpu_eval_batch_size` in distributed training).
"""
if self.per_gpu_eval_batch_size:
logger.warning(
"Using deprecated `--per_gpu_eval_batch_size` arg... | 132 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/training_args_tf.py |
@property
def n_gpu(self) -> int:
"""
The number of replicas (CPUs, GPUs or TPU cores) used in this training.
"""
requires_backends(self, ["tf"])
warnings.warn(
"The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead.",
... | 132 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/training_args_tf.py |
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... | 133 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
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 ... | 134 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
class Converter:
def __init__(self, original_tokenizer):
self.original_tokenizer = original_tokenizer
def converted(self) -> Tokenizer:
raise NotImplementedError() | 135 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
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
... | 136 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
cls = str(self.original_tokenizer.cls_token)
sep = str(self.original_tokenizer.sep_token)
cls_token_id = self.original_tokenizer.cls_token_id
sep_token_id = self.original_tokenizer.sep_token_id
tokenizer.post_processor = processors.TemplateProcessing(
single=f"{cls}:0 $A:0 {... | 136 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
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
... | 137 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
cls = str(self.original_tokenizer.cls_token)
sep = str(self.original_tokenizer.sep_token)
question = str(self.original_tokenizer.question_token)
dot = "."
cls_token_id = self.original_tokenizer.cls_token_id
sep_token_id = self.original_tokenizer.sep_token_id
question_toke... | 137 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
tokenizer.post_processor = processors.TemplateProcessing(
single=f"{cls}:0 $A:0 {sep}:0",
pair=pair,
special_tokens=[
(cls, cls_token_id),
(sep, sep_token_id),
(question, question_token_id),
(dot, dot_token_id),
... | 137 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
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
... | 138 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
cls = str(self.original_tokenizer.cls_token)
sep = str(self.original_tokenizer.sep_token)
cls_token_id = self.original_tokenizer.cls_token_id
sep_token_id = self.original_tokenizer.sep_token_id
tokenizer.post_processor = processors.TemplateProcessing(
single=f"{cls}:2 $A:0 {... | 138 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
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
... | 139 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
cls = str(self.original_tokenizer.cls_token)
sep = str(self.original_tokenizer.sep_token)
cls_token_id = self.original_tokenizer.cls_token_id
sep_token_id = self.original_tokenizer.sep_token_id
tokenizer.post_processor = processors.TemplateProcessing(
single=f"{cls}:0 $A:0 {... | 139 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
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,
... | 140 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
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... | 141 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
add_prefix_space = getattr(self.original_tokenizer, "add_prefix_space", False)
tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=add_prefix_space)
tokenizer.decoder = decoders.ByteLevel()
if getattr(self.original_tokenizer, "add_bos_token", False):
bos = self.origin... | 141 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
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... | 142 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
tokenizer.normalizer = normalizers.BertNormalizer(lowercase=False, strip_accents=False)
tokenizer.pre_tokenizer = pre_tokenizers.BertPreTokenizer()
tokenizer.decoder = decoders.BPEDecoder(suffix=token_suffix)
tokenizer.post_processor = processors.BertProcessing(
sep=(self.original_to... | 142 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
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... | 143 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
tokenizer.pre_tokenizer = pre_tokenizers.Sequence(
[
pre_tokenizers.Split(
Regex(
r"""(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+"""
),
behav... | 143 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
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,
... | 144 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
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... | 145 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
cls = str(self.original_tokenizer.cls_token)
sep = str(self.original_tokenizer.sep_token)
cls_token_id = self.original_tokenizer.cls_token_id
sep_token_id = self.original_tokenizer.sep_token_id
tokenizer.post_processor = processors.TemplateProcessing(
single=f"{cls}:0 $A:0 {... | 145 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
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,
... | 146 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
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_... | 147 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
if self.proto.trainer_spec.byte_fallback and not self.handle_byte_fallback:
warnings.warn(
"The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option"
" which is not implemented in the fast tokenizers. In practice this means that th... | 147 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
if model_type == 1:
tokenizer = Tokenizer(
Unigram(
vocab_scores,
unk_id=self.unk_id(proto),
byte_fallback=self.handle_byte_fallback,
)
)
elif model_type == 2:
_, merges = self.SpmExt... | 147 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
# control tokens are special
# user defined symbols are not
# both user and control tokens are AddedTokens
# Add user defined symbols (type == 4) from sentencepiece (https://github.com/google/sentencepiece/blob/6225e08edb2577757163b3f5dbba4c0b670ef445/src/sentencepiece_model.proto#L299C29-L299C3... | 147 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
def normalizer(self, proto):
precompiled_charsmap = proto.normalizer_spec.precompiled_charsmap
_normalizers = [
normalizers.Strip(left=False, right=True), # stripping is important
normalizers.Replace(Regex(" {2,}"), "▁"),
]
if not precompiled_charsmap:
... | 147 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
def converted(self) -> Tokenizer:
tokenizer = self.tokenizer(self.proto)
# Tokenizer assemble
normalizer = self.normalizer(self.proto)
if normalizer is not None:
tokenizer.normalizer = normalizer
replacement = "▁"
add_prefix_space = True
if hasattr(s... | 147 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
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... | 148 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
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]")),
("[SEP]", self.original... | 148 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
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> $B </s>",
special_tokens=[
("<s>", sel... | 149 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
class CamembertConverter(SpmConverter):
def vocab(self, proto):
vocab = [
("<s>NOTUSED", 0.0),
("<pad>", 0.0),
("</s>NOTUSED", 0.0),
("<unk>", 0.0),
("<unk>NOTUSED", -100),
]
# We down-grade the original SentencePiece by -100 to avo... | 150 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
class DebertaV2Converter(SpmConverter):
def pre_tokenizer(self, replacement, add_prefix_space):
list_pretokenizers = []
if self.original_tokenizer.split_by_punct:
list_pretokenizers.append(pre_tokenizers.Punctuation(behavior="isolated"))
prepend_scheme = _get_prepend_scheme(add_p... | 151 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
precompiled_charsmap = proto.normalizer_spec.precompiled_charsmap
if precompiled_charsmap:
list_normalizers.append(normalizers.Precompiled(precompiled_charsmap))
list_normalizers.append(normalizers.Replace(Regex(" {2,}"), " "))
return normalizers.Sequence(list_normalizers)
def ... | 151 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
class MBartConverter(SpmConverter):
def vocab(self, proto):
vocab = [
("<s>", 0.0),
("<pad>", 0.0),
("</s>", 0.0),
("<unk>", 0.0),
]
vocab += [(piece.piece, piece.score) for piece in proto.pieces[3:]]
vocab += [
("ar_AR", 0.... | 152 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
vocab += [("<mask>", 0.0)]
return vocab | 152 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
def unk_id(self, proto):
return 3
def post_processor(self):
return processors.TemplateProcessing(
single="$A </s> en_XX",
pair="$A $B </s> en_XX",
special_tokens=[
("en_XX", self.original_tokenizer.convert_tokens_to_ids("en_XX")),
... | 152 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
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:]] | 153 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
vocab += [("ar_AR", 0.0), ("cs_CZ", 0.0), ("de_DE", 0.0), ("en_XX", 0.0), ("es_XX", 0.0), ("et_EE", 0.0), ("fi_FI", 0.0), ("fr_XX", 0.0), ("gu_IN", 0.0), ("hi_IN", 0.0), ("it_IT", 0.0), ("ja_XX", 0.0), ("kk_KZ", 0.0), ("ko_KR", 0.0), ("lt_LT", 0.0), ("lv_LV", 0.0), ("my_MM", 0.0), ("ne_NP", 0.0), ("nl_XX", 0.0), ("ro_R... | 153 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
def unk_id(self, proto):
return 3
def post_processor(self):
return processors.TemplateProcessing(
single="en_XX $A </s>",
pair="en_XX $A $B </s>",
special_tokens=[
("en_XX", self.original_tokenizer.convert_tokens_to_ids("en_XX")),
... | 153 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
class NllbConverter(SpmConverter):
def vocab(self, proto):
vocab = [
("<s>", 0.0),
("<pad>", 0.0),
("</s>", 0.0),
("<unk>", 0.0),
]
vocab += [(piece.piece, piece.score) for piece in proto.pieces[3:]]
return vocab
def unk_id(self, p... | 154 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
class SeamlessM4TConverter(SpmConverter):
def vocab(self, proto):
vocab = [
("<pad>", 0.0),
("<unk>", 0.0),
("<s>", 0.0),
("</s>", 0.0),
]
vocab += [(piece.piece, piece.score) for piece in proto.pieces[3:]]
return vocab
def unk_id(... | 155 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
class XLMRobertaConverter(SpmConverter):
def vocab(self, proto):
vocab = [
("<s>", 0.0),
("<pad>", 0.0),
("</s>", 0.0),
("<unk>", 0.0),
]
vocab += [(piece.piece, piece.score) for piece in proto.pieces[3:]]
vocab += [("<mask>", 0.0)]
... | 156 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
class XLNetConverter(SpmConverter):
def vocab(self, proto):
return [
(piece.piece, piece.score) if check_number_comma(piece.piece) else (piece.piece, piece.score - 100)
for piece in proto.pieces
]
def normalizer(self, proto):
list_normalizers = [
norm... | 157 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
def post_processor(self):
return processors.TemplateProcessing(
single="$A:0 <sep>:0 <cls>:2",
pair="$A:0 <sep>:0 $B:1 <sep>:1 <cls>:2",
special_tokens=[
("<sep>", self.original_tokenizer.convert_tokens_to_ids("<sep>")),
("<cls>", self.original... | 157 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
class ReformerConverter(SpmConverter):
pass | 158 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
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... | 159 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
def post_processor(self):
return processors.TemplateProcessing(
single="[CLS]:0 $A:0 [SEP]:0",
pair="[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1",
special_tokens=[
("[CLS]", self.original_tokenizer.convert_tokens_to_ids("[CLS]")),
("[SEP]", self.original... | 159 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
class BertGenerationConverter(SpmConverter):
pass | 160 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
class PegasusConverter(SpmConverter):
def vocab(self, proto):
vocab = [
(self.original_tokenizer.pad_token, 0.0),
(self.original_tokenizer.eos_token, 0.0),
]
if self.original_tokenizer.mask_token_sent is not None:
vocab += [(self.original_tokenizer.mask_t... | 161 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
def pre_tokenizer(self, replacement, add_prefix_space):
prepend_scheme = _get_prepend_scheme(add_prefix_space, self.original_tokenizer)
return pre_tokenizers.Sequence(
[
pre_tokenizers.WhitespaceSplit(),
pre_tokenizers.Metaspace(replacement=replacement, prepen... | 161 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
class T5Converter(SpmConverter):
def vocab(self, proto):
num_extra_ids = self.original_tokenizer._extra_ids
vocab = [(piece.piece, piece.score) for piece in proto.pieces]
vocab += [(f"<extra_id_{i}>", 0.0) for i in range(num_extra_ids - 1, -1, -1)]
return vocab
def post_processo... | 162 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
class UdopConverter(SpmConverter):
def post_processor(self):
return processors.TemplateProcessing(
single=["$A", "</s>"],
pair=["$A", "</s>", "$B", "</s>"],
special_tokens=[
("</s>", self.original_tokenizer.convert_tokens_to_ids("</s>")),
],
... | 163 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
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... | 164 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
prefix_token_ids = self.original_tokenizer.prefix_tokens
prefixes = self.original_tokenizer.convert_ids_to_tokens(prefix_token_ids)
eos = self.original_tokenizer.eos_token
eos_token_id = self.original_tokenizer.eos_token_id
prefix_template = " ".join([f"{token}:0" for token in prefixes])... | 164 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
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]")... | 165 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
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,
... | 166 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
tokenizer.normalizer = normalizers.Sequence(
[normalizers.NFC(), normalizers.Replace(Regex(r"\s+"), " "), normalizers.Lowercase()]
)
tokenizer.pre_tokenizer = pre_tokenizers.Sequence(
[
pre_tokenizers.Split(
Regex(r"""'s|'t|'re|'ve|'m|'ll|'d|[\... | 166 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
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
... | 167 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
cls = str(self.original_tokenizer.cls_token)
sep = str(self.original_tokenizer.sep_token)
cls_token_id = self.original_tokenizer.cls_token_id
sep_token_id = self.original_tokenizer.sep_token_id
tokenizer.post_processor = processors.TemplateProcessing(
single=f"{cls}:0 $A:0 {... | 167 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
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... | 168 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
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), ("... | 169 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
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... | 170 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
def vocab(self, proto):
vocab = [
(self.original_tokenizer.pad_token, 0.0),
(self.original_tokenizer.eos_token, 0.0),
(self.original_tokenizer.bos_token, 0.0),
]
for piece in proto.pieces[3:]:
if piece.piece == "<0x09>":
vocab += [(... | 170 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
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)... | 171 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
def normalizer(self, proto):
if getattr(self.original_tokenizer, "legacy", True):
sequence = []
if getattr(self.original_tokenizer, "add_prefix_space", True):
sequence += [normalizers.Prepend(prepend="▁")]
sequence += [normalizers.Replace(pattern=" ", content=... | 171 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
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,
... | 172 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
tokenizer.post_processor = processors.TemplateProcessing(
single=f"{cls} $A {sep}",
pair=f"{cls} $A {sep} $B {sep}",
special_tokens=[
(cls, cls_token_id),
(sep, sep_token_id),
],
)
return tokenizer | 172 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
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... | 173 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
def decoder(self, replacement, add_prefix_space):
sequence = [
decoders.Replace("▁", " "),
decoders.ByteFallback(),
decoders.Fuse(),
]
if add_prefix_space:
sequence += [decoders.Strip(content=" ", left=1)]
return decoders.Sequence(sequence)... | 173 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
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... | 174 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
def tokenizer(self, proto):
vocab_scores = self.vocab(proto)
tokenizer = Tokenizer(
Unigram(
vocab_scores,
unk_id=self.unk_id(proto),
byte_fallback=self.handle_byte_fallback,
)
)
# control tokens are special
... | 174 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
]
)
tokenizer.add_tokens([AddedToken("\n", normalized=False, special=False)])
tokenizer.enable_padding(pad_token="<pad>", pad_id=3)
return tokenizer | 174 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
def vocab(self, proto):
vocab = []
for piece in proto.pieces:
if piece.piece == "<0x0A>":
vocab += [("\n", piece.score)]
else:
vocab += [(piece.piece, piece.score)]
return vocab
def unk_id(self, proto):
unk_id = 0
retur... | 174 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
def post_processor(self):
return processors.TemplateProcessing(
single=[
"<s>",
"$A",
],
pair=[
"<s>",
"$A",
"<s>",
"$B",
],
special_tokens=[
... | 174 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
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... | 175 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
def token_bytes_to_string(b):
return "".join([byte_encoder[ord(char)] for char in b.decode("latin-1")])
merges = []
vocab = {}
for token, rank in bpe_ranks.items():
vocab[token_bytes_to_string(token)] = rank
if len(token) == 1:
continue
... | 175 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
def tokenizer(self):
vocab_scores, merges = self.extract_vocab_merges_from_model(self.vocab_file)
tokenizer = Tokenizer(BPE(vocab_scores, merges, fuse_unk=False))
if hasattr(tokenizer.model, "ignore_merges"):
tokenizer.model.ignore_merges = True
return tokenizer
def conv... | 175 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_slow_tokenizer.py |
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... | 176 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/image_processing_utils.py |
Args:
image (`np.ndarray`):
Image to rescale.
scale (`float`):
The scaling factor to rescale pixel values by.
data_format (`str` or `ChannelDimension`, *optional*):
The channel dimension format for the output image. If unset, the channe... | 176 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/image_processing_utils.py |
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. | 176 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/image_processing_utils.py |
Returns:
`np.ndarray`: The rescaled image.
"""
return rescale(image, scale=scale, data_format=data_format, input_data_format=input_data_format, **kwargs)
def normalize(
self,
image: np.ndarray,
mean: Union[float, Iterable[float]],
std: Union[float, Iterab... | 176 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/image_processing_utils.py |
Args:
image (`np.ndarray`):
Image to normalize.
mean (`float` or `Iterable[float]`):
Image mean to use for normalization.
std (`float` or `Iterable[float]`):
Image standard deviation to use for normalization.
data_format (`s... | 176 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/image_processing_utils.py |
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. | 176 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/image_processing_utils.py |
Returns:
`np.ndarray`: The normalized image.
"""
return normalize(
image, mean=mean, std=std, data_format=data_format, input_data_format=input_data_format, **kwargs
)
def center_crop(
self,
image: np.ndarray,
size: Dict[str, int],
data... | 176 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/image_processing_utils.py |
Args:
image (`np.ndarray`):
Image to center crop.
size (`Dict[str, int]`):
Size of the output image.
data_format (`str` or `ChannelDimension`, *optional*):
The channel dimension format for the output image. If unset, the channel dimensi... | 176 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/image_processing_utils.py |
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
"""
size = get_size_dict(size)
if "height" not in size or "width" not in size:
raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}")
return... | 176 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/image_processing_utils.py |
def to_dict(self):
encoder_dict = super().to_dict()
encoder_dict.pop("_valid_processor_keys", None)
return encoder_dict | 176 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/image_processing_utils.py |
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... | 177 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/feature_extraction_sequence_utils.py |
def pad(
self,
processed_features: Union[
BatchFeature,
List[BatchFeature],
Dict[str, BatchFeature],
Dict[str, List[BatchFeature]],
List[Dict[str, BatchFeature]],
],
padding: Union[bool, str, PaddingStrategy] = True,
max... | 177 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/feature_extraction_sequence_utils.py |
If the `processed_features` passed are dictionary of numpy arrays, PyTorch tensors or TensorFlow tensors, the
result will use the same type unless you provide a different tensor type with `return_tensors`. In the case of
PyTorch tensors, you will lose the specific device of your tensors however.
... | 177 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/feature_extraction_sequence_utils.py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.