id int64 0 328k | repository_name stringlengths 7 58 | file_path stringlengths 9 302 | class_name stringlengths 5 256 | human_written_code stringlengths 16 2.16M | class_skeleton stringlengths 18 1.49M ⌀ | total_program_units int64 1 1.76k | total_doc_str int64 0 771 | AvgCountLine float64 0 7.89k | AvgCountLineBlank float64 0 297 | AvgCountLineCode float64 0 7.89k | AvgCountLineComment float64 0 7.89k | AvgCyclomatic float64 0 130 | CommentToCodeRatio float64 0 168 | CountClassBase float64 0 40 | CountClassCoupled float64 0 583 | CountClassCoupledModified float64 0 575 | CountClassDerived float64 0 5.35k | CountDeclInstanceMethod float64 0 529 | CountDeclInstanceVariable float64 0 296 | CountDeclMethod float64 0 599 | CountDeclMethodAll float64 0 1.12k | CountLine float64 1 40.4k | CountLineBlank float64 0 8.16k | CountLineCode float64 1 25.7k | CountLineCodeDecl float64 1 8.15k | CountLineCodeExe float64 0 24.2k | CountLineComment float64 0 16.5k | CountStmt float64 1 9.71k | CountStmtDecl float64 1 8.15k | CountStmtExe float64 0 9.69k | MaxCyclomatic float64 0 759 | MaxInheritanceTree float64 0 16 | MaxNesting float64 0 34 | SumCyclomatic float64 0 2.9k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
200 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/commands/chat.py | transformers.commands.chat.ChatArguments | from dataclasses import dataclass, field
from typing import Optional
@dataclass
class ChatArguments:
"""
Arguments for the chat CLI.
See the metadata arg for each argument's description -- the medatata will be printed with
`transformers chat --help`
"""
model_name_or_path: Optional[str] = fiel... | @dataclass
class ChatArguments:
'''
Arguments for the chat CLI.
See the metadata arg for each argument's description -- the medatata will be printed with
`transformers chat --help`
'''
def __post_init__(self):
'''Only used for BC `torch_dtype` argument.'''
pass | 3 | 2 | 0 | 0 | 0 | 0 | 0 | 1.02 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 113 | 4 | 54 | 24 | 53 | 55 | 24 | 24 | 23 | 0 | 0 | 0 | 0 |
201 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/commands/chat.py | transformers.commands.chat.ChatCommand | import asyncio
import copy
from transformers import AutoTokenizer, GenerationConfig, PreTrainedTokenizer
import platform
import os
import yaml
from argparse import ArgumentParser, Namespace
from transformers.commands import BaseTransformersCLICommand
from transformers.utils import is_rich_available, is_torch_available
... |
class ChatCommand(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
'''
... | 21 | 8 | 39 | 6 | 31 | 2 | 6 | 0.05 | 1 | 7 | 2 | 0 | 2 | 1 | 3 | 25 | 121 | 20 | 96 | 28 | 91 | 5 | 74 | 26 | 70 | 15 | 5 | 4 | 17 |
202 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/commands/chat.py | transformers.commands.chat.RichInterface | import re
from transformers import AutoTokenizer, GenerationConfig, PreTrainedTokenizer
from typing import Optional
from huggingface_hub import AsyncInferenceClient, ChatCompletionStreamOutput
from collections.abc import AsyncIterator
class RichInterface:
def __init__(self, model_name: Optional[str]=None, user_na... |
class RichInterface:
def __init__(self, model_name: Optional[str]=None, user_name: Optional[str]=None):
pass
async def stream_output(self, stream: AsyncIterator[ChatCompletionStreamOutput]) -> tuple[str, int]:
pass
def input(self) -> str:
'''Gets user input from the console.'''
... | 9 | 6 | 8 | 0 | 6 | 2 | 2 | 0.38 | 0 | 1 | 0 | 0 | 8 | 3 | 8 | 8 | 73 | 7 | 48 | 18 | 39 | 18 | 45 | 17 | 36 | 5 | 0 | 4 | 14 |
203 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/commands/download.py | transformers.commands.download.DownloadCommand | from . import BaseTransformersCLICommand
from argparse import ArgumentParser
class DownloadCommand(BaseTransformersCLICommand):
@staticmethod
def register_subcommand(parser: ArgumentParser):
download_parser = parser.add_parser('download')
download_parser.add_argument('--cache-dir', type=str, d... |
class DownloadCommand(BaseTransformersCLICommand):
@staticmethod
def register_subcommand(parser: ArgumentParser):
pass
def __init__(self, model: str, cache: str, force: bool, trust_remote_code: bool):
pass
def run(self):
pass | 5 | 0 | 10 | 0 | 9 | 0 | 1 | 0 | 1 | 5 | 2 | 0 | 2 | 4 | 3 | 25 | 33 | 3 | 30 | 11 | 24 | 0 | 17 | 10 | 12 | 1 | 5 | 0 | 3 |
204 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/commands/env.py | transformers.commands.env.EnvironmentCommand | from .. import __version__ as version
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
import platform
import io
import importlib.util
import os
from ..integrations.deepspeed import is_deepspeed_available
import huggingface_hub
from ..utils import is_accelerate_available, is_safetensors_avai... |
class EnvironmentCommand(BaseTransformersCLICommand):
@staticmethod
def register_subcommand(parser: ArgumentParser):
pass
def __init__(self, accelerate_config_file, *args) -> None:
pass
def run(self):
pass
@staticmethod
def format_dict(d):
pass | 7 | 0 | 26 | 4 | 22 | 1 | 4 | 0.03 | 1 | 4 | 0 | 0 | 2 | 1 | 4 | 26 | 111 | 17 | 91 | 31 | 75 | 3 | 65 | 29 | 51 | 13 | 5 | 2 | 16 |
205 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/commands/run.py | transformers.commands.run.RunCommand | from argparse import ArgumentParser
from . import BaseTransformersCLICommand
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
class RunCommand(BaseTransformersCLICommand):
def __init__(self, nlp: Pipeline, reader: PipelineDataFormat):
self._nlp = nlp
self._reader... |
class RunCommand(BaseTransformersCLICommand):
def __init__(self, nlp: Pipeline, reader: PipelineDataFormat):
pass
@staticmethod
def register_subcommand(parser: ArgumentParser):
pass
def run(self):
pass | 5 | 0 | 16 | 1 | 15 | 0 | 2 | 0.02 | 1 | 6 | 2 | 0 | 2 | 2 | 3 | 25 | 53 | 4 | 48 | 12 | 43 | 1 | 28 | 11 | 24 | 5 | 5 | 2 | 7 |
206 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/commands/serving.py | transformers.commands.serving.ServeCommand | import functools
import uuid
from transformers.models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES, MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING_NAMES
import base64
from threading import Thread
from io import BytesIO
import asyncio
from tokenizers.decoders import DecodeStream
from typing import Optional, Union
i... |
class ServeCommand(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
'''
... | 50 | 16 | 18 | 1 | 15 | 3 | 2 | 0.19 | 1 | 11 | 5 | 0 | 6 | 5 | 7 | 29 | 137 | 13 | 104 | 27 | 90 | 20 | 48 | 18 | 40 | 3 | 5 | 2 | 13 |
207 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/configuration_utils.py | transformers.configuration_utils.PretrainedConfig | import os
from .utils.generic import is_timm_config_dict
from .dynamic_module_utils import custom_object_save
import copy
import warnings
from .modeling_gguf_pytorch_utils import load_gguf_checkpoint
import json
from typing import TYPE_CHECKING, Any, Optional, TypeVar, Union
from . import __version__
from .utils import... |
class PretrainedConfig(PushToHubMixin):
'''
Base class for all configuration classes. Handles a few parameters common to all models' configurations as well as
methods for loading/downloading/saving configurations.
<Tip>
A configuration file can be loaded and saved to disk. Loading the configuration... | 63 | 24 | 28 | 4 | 16 | 8 | 4 | 0.67 | 1 | 19 | 0 | 38 | 23 | 33 | 32 | 32 | 1,084 | 169 | 551 | 179 | 487 | 370 | 383 | 147 | 348 | 16 | 1 | 3 | 143 |
208 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/convert_slow_tokenizer.py | transformers.convert_slow_tokenizer.AlbertConverter | from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors
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]
de... |
class AlbertConverter(SpmConverter):
def vocab(self, proto):
pass
def normalizer(self, proto):
pass
def post_processor(self):
pass | 4 | 0 | 11 | 1 | 10 | 0 | 2 | 0 | 1 | 0 | 0 | 0 | 3 | 0 | 3 | 14 | 35 | 5 | 30 | 6 | 26 | 0 | 17 | 6 | 13 | 4 | 2 | 1 | 7 |
209 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/convert_slow_tokenizer.py | transformers.convert_slow_tokenizer.BarthezConverter | from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors
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>... |
class BarthezConverter(SpmConverter):
def unk_id(self, proto):
pass
def post_processor(self):
pass | 3 | 0 | 6 | 0 | 6 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 2 | 0 | 2 | 13 | 14 | 1 | 13 | 4 | 10 | 0 | 6 | 4 | 3 | 1 | 2 | 0 | 2 |
210 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/convert_slow_tokenizer.py | transformers.convert_slow_tokenizer.BertConverter | from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import BPE, Unigram, WordPiece
class BertConverter(Converter):
def converted(self) -> Tokenizer:
vocab = self.original_tokenizer.vocab
tokenizer = Tokenizer(WordPiece(vocab... |
class BertConverter(Converter):
def converted(self) -> Tokenizer:
pass | 2 | 0 | 36 | 5 | 31 | 1 | 2 | 0.03 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 3 | 37 | 5 | 32 | 11 | 30 | 1 | 20 | 11 | 18 | 2 | 1 | 1 | 2 |
211 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/convert_slow_tokenizer.py | transformers.convert_slow_tokenizer.BertGenerationConverter | class BertGenerationConverter(SpmConverter):
pass | class BertGenerationConverter(SpmConverter):
pass | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 11 | 2 | 0 | 2 | 1 | 1 | 0 | 2 | 1 | 1 | 0 | 2 | 0 | 0 |
212 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/convert_slow_tokenizer.py | transformers.convert_slow_tokenizer.BigBirdConverter | from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors
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]', sel... |
class BigBirdConverter(SpmConverter):
def post_processor(self):
pass | 2 | 0 | 9 | 0 | 9 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 12 | 10 | 0 | 10 | 2 | 8 | 0 | 3 | 2 | 1 | 1 | 2 | 0 | 1 |
213 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/convert_slow_tokenizer.py | transformers.convert_slow_tokenizer.BlenderbotConverter | from tokenizers.models import BPE, Unigram, WordPiece
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors
class BlenderbotConverter(Converter):
def converted(self) -> Tokenizer:
ot = self.original_tokenizer
vocab = ot.encoder
merges = list... |
class BlenderbotConverter(Converter):
def converted(self) -> Tokenizer:
pass | 2 | 0 | 26 | 3 | 23 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 3 | 27 | 3 | 24 | 6 | 22 | 0 | 10 | 6 | 8 | 1 | 1 | 0 | 1 |
214 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/convert_slow_tokenizer.py | transformers.convert_slow_tokenizer.CLIPConverter | from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import BPE, Unigram, WordPiece
class CLIPConverter(Converter):
def converted(self) -> Tokenizer:
vocab = self.original_tokenizer.encoder
merges = list(self.original_tokeniz... |
class CLIPConverter(Converter):
def converted(self) -> Tokenizer:
pass | 2 | 0 | 40 | 3 | 36 | 1 | 1 | 0.03 | 1 | 2 | 0 | 0 | 1 | 0 | 1 | 3 | 41 | 3 | 37 | 6 | 35 | 1 | 11 | 6 | 9 | 1 | 1 | 0 | 1 |
215 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/convert_slow_tokenizer.py | transformers.convert_slow_tokenizer.CamembertConverter | from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors
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)]
vocab += [(piece.piece,... |
class CamembertConverter(SpmConverter):
def vocab(self, proto):
pass
def unk_id(self, proto):
pass
def post_processor(self):
pass | 4 | 0 | 8 | 0 | 7 | 1 | 1 | 0.09 | 1 | 0 | 0 | 0 | 3 | 0 | 3 | 14 | 27 | 2 | 23 | 5 | 19 | 2 | 10 | 5 | 6 | 1 | 2 | 0 | 3 |
216 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/convert_slow_tokenizer.py | transformers.convert_slow_tokenizer.Converter | from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors
class Converter:
def __init__(self, original_tokenizer):
self.original_tokenizer = original_tokenizer
def converted(self) -> Tokenizer:
raise NotImplementedError() |
class Converter:
def __init__(self, original_tokenizer):
pass
def converted(self) -> Tokenizer:
pass | 3 | 0 | 2 | 0 | 2 | 0 | 1 | 0 | 0 | 1 | 0 | 17 | 2 | 1 | 2 | 2 | 6 | 1 | 5 | 4 | 2 | 0 | 5 | 4 | 2 | 1 | 0 | 0 | 2 |
217 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/convert_slow_tokenizer.py | transformers.convert_slow_tokenizer.DebertaConverter | from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import BPE, Unigram, WordPiece
class DebertaConverter(Converter):
def converted(self) -> Tokenizer:
ot = self.original_tokenizer
vocab = ot.encoder
merges = list(ot... |
class DebertaConverter(Converter):
def converted(self) -> Tokenizer:
pass | 2 | 0 | 28 | 3 | 25 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 3 | 29 | 3 | 26 | 6 | 24 | 0 | 10 | 6 | 8 | 1 | 1 | 0 | 1 |
218 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/convert_slow_tokenizer.py | transformers.convert_slow_tokenizer.DebertaV2Converter | from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors
class DebertaV2Converter(SpmConverter):
def pre_tokenizer(self, replacement, add_prefix_space):
list_pretokenizers = []
if self.original_tokenizer.split_by_punct:
list_pretokenizers.a... |
class DebertaV2Converter(SpmConverter):
def pre_tokenizer(self, replacement, add_prefix_space):
pass
def normalizer(self, proto):
pass
def post_processor(self):
pass | 4 | 0 | 9 | 1 | 9 | 0 | 2 | 0 | 1 | 0 | 0 | 0 | 3 | 0 | 3 | 14 | 31 | 4 | 27 | 8 | 23 | 0 | 20 | 8 | 16 | 3 | 2 | 1 | 6 |
219 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/convert_slow_tokenizer.py | transformers.convert_slow_tokenizer.FunnelConverter | from tokenizers.models import BPE, Unigram, WordPiece
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors
class FunnelConverter(Converter):
def converted(self) -> Tokenizer:
vocab = self.original_tokenizer.vocab
tokenizer = Tokenizer(WordPiece(voc... |
class FunnelConverter(Converter):
def converted(self) -> Tokenizer:
pass | 2 | 0 | 36 | 5 | 31 | 2 | 2 | 0.06 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 3 | 37 | 5 | 32 | 11 | 30 | 2 | 20 | 11 | 18 | 2 | 1 | 1 | 2 |
220 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/convert_slow_tokenizer.py | transformers.convert_slow_tokenizer.GPT2Converter | from tokenizers.models import BPE, Unigram, WordPiece
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors
from typing import Optional
class GPT2Converter(Converter):
def converted(self, vocab: Optional[dict[str, int]]=None, merges: Optional[list[tuple[str, str]]]... |
class GPT2Converter(Converter):
def converted(self, vocab: Optional[dict[str, int]]=None, merges: Optional[list[tuple[str, str]]]=None) -> Tokenizer:
pass | 2 | 0 | 35 | 2 | 31 | 2 | 4 | 0.06 | 1 | 3 | 0 | 1 | 1 | 0 | 1 | 3 | 36 | 2 | 32 | 6 | 30 | 2 | 16 | 6 | 14 | 4 | 1 | 1 | 4 |
221 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/convert_slow_tokenizer.py | transformers.convert_slow_tokenizer.GemmaConverter | from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors
class GemmaConverter(SpmConverter):
handle_byte_fallback = True
SpmExtractor = GemmaSentencePieceExtractor
special_tokens = {'<start_of_turn>', '<end_of_turn>'}
'"\n split_by_unicode_script: true\n... |
class GemmaConverter(SpmConverter):
def normalizer(self, proto):
pass
def vocab(self, proto):
pass
def pre_tokenizer(self, replacement, add_prefix_space):
pass
def unk_id(self, proto):
pass
def decoder(self, replacement, add_prefix_space):
pass | 6 | 0 | 6 | 0 | 5 | 0 | 1 | 0.35 | 1 | 0 | 0 | 1 | 5 | 0 | 5 | 16 | 48 | 6 | 31 | 12 | 25 | 11 | 20 | 12 | 14 | 3 | 2 | 2 | 7 |
222 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/convert_slow_tokenizer.py | transformers.convert_slow_tokenizer.GemmaSentencePieceExtractor | 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 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... | 2 | 1 | 14 | 2 | 6 | 6 | 1 | 0.86 | 1 | 3 | 0 | 0 | 1 | 0 | 1 | 3 | 15 | 2 | 7 | 5 | 5 | 6 | 7 | 5 | 5 | 1 | 1 | 0 | 1 |
223 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/convert_slow_tokenizer.py | transformers.convert_slow_tokenizer.HeliumConverter | from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import BPE, Unigram, WordPiece
from .utils import is_protobuf_available, is_sentencepiece_available, logging, requires_backends
class HeliumConverter(SpmConverter):
handle_byte_fallback = T... |
class HeliumConverter(SpmConverter):
def __init__(self, vocab_file=None, *args):
pass
def tokenizer(self, proto):
pass
def vocab(self, proto):
pass
def unk_id(self, proto):
pass
def decoder(self, replacement, add_prefix_space):
pass
def normalizer(s... | 9 | 0 | 10 | 0 | 9 | 1 | 1 | 0.06 | 1 | 1 | 0 | 0 | 8 | 1 | 8 | 19 | 87 | 11 | 72 | 22 | 63 | 4 | 38 | 20 | 29 | 3 | 2 | 2 | 10 |
224 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/convert_slow_tokenizer.py | transformers.convert_slow_tokenizer.HerbertConverter | from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import BPE, Unigram, WordPiece
class HerbertConverter(Converter):
def converted(self) -> Tokenizer:
tokenizer_info_str = '#version:'
token_suffix = '</w>'
vocab = s... |
class HerbertConverter(Converter):
def converted(self) -> Tokenizer:
pass | 2 | 0 | 28 | 4 | 24 | 1 | 2 | 0.04 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 3 | 29 | 4 | 25 | 7 | 23 | 1 | 14 | 7 | 12 | 2 | 1 | 1 | 2 |
225 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/convert_slow_tokenizer.py | transformers.convert_slow_tokenizer.LayoutLMv2Converter | from tokenizers.models import BPE, Unigram, WordPiece
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors
class LayoutLMv2Converter(Converter):
def converted(self) -> Tokenizer:
vocab = self.original_tokenizer.vocab
tokenizer = Tokenizer(WordPiece... |
class LayoutLMv2Converter(Converter):
def converted(self) -> Tokenizer:
pass | 2 | 0 | 36 | 5 | 31 | 1 | 2 | 0.03 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 3 | 37 | 5 | 32 | 11 | 30 | 1 | 20 | 11 | 18 | 2 | 1 | 1 | 2 |
226 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/convert_slow_tokenizer.py | transformers.convert_slow_tokenizer.LlamaConverter | from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors
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_token... |
class LlamaConverter(SpmConverter):
def vocab(self, proto):
pass
def unk_id(self, proto):
pass
def decoder(self, replacement, add_prefix_space):
pass
def normalizer(self, proto):
pass
def pre_tokenizer(self, replacement, add_prefix_space):
pass
def ... | 7 | 0 | 6 | 0 | 6 | 1 | 2 | 0.08 | 1 | 0 | 0 | 2 | 6 | 0 | 6 | 17 | 44 | 6 | 37 | 13 | 30 | 3 | 29 | 13 | 22 | 3 | 2 | 2 | 10 |
227 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/convert_slow_tokenizer.py | transformers.convert_slow_tokenizer.MBart50Converter | from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors
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[... |
class MBart50Converter(SpmConverter):
def vocab(self, proto):
pass
def unk_id(self, proto):
pass
def post_processor(self):
pass | 4 | 0 | 7 | 0 | 7 | 0 | 1 | 0.04 | 1 | 0 | 0 | 0 | 3 | 0 | 3 | 14 | 25 | 2 | 23 | 5 | 19 | 1 | 11 | 5 | 7 | 1 | 2 | 0 | 3 |
228 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/convert_slow_tokenizer.py | transformers.convert_slow_tokenizer.MBartConverter | from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors
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:... |
class MBartConverter(SpmConverter):
def vocab(self, proto):
pass
def unk_id(self, proto):
pass
def post_processor(self):
pass | 4 | 0 | 16 | 0 | 16 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 3 | 0 | 3 | 14 | 51 | 2 | 49 | 5 | 45 | 0 | 11 | 5 | 7 | 1 | 2 | 0 | 3 |
229 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/convert_slow_tokenizer.py | transformers.convert_slow_tokenizer.MPNetConverter | from tokenizers.models import BPE, Unigram, WordPiece
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors
class MPNetConverter(Converter):
def converted(self) -> Tokenizer:
vocab = self.original_tokenizer.vocab
tokenizer = Tokenizer(WordPiece(voca... |
class MPNetConverter(Converter):
def converted(self) -> Tokenizer:
pass | 2 | 0 | 36 | 5 | 31 | 2 | 2 | 0.06 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 3 | 37 | 5 | 32 | 11 | 30 | 2 | 20 | 11 | 18 | 2 | 1 | 1 | 2 |
230 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/convert_slow_tokenizer.py | transformers.convert_slow_tokenizer.MarkupLMConverter | from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import BPE, Unigram, WordPiece
class MarkupLMConverter(Converter):
def converted(self) -> Tokenizer:
ot = self.original_tokenizer
vocab = ot.encoder
merges = list(o... |
class MarkupLMConverter(Converter):
def converted(self) -> Tokenizer:
pass | 2 | 0 | 35 | 5 | 30 | 0 | 1 | 0 | 1 | 2 | 0 | 0 | 1 | 0 | 1 | 3 | 36 | 5 | 31 | 10 | 29 | 0 | 14 | 10 | 12 | 1 | 1 | 0 | 1 |
231 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/convert_slow_tokenizer.py | transformers.convert_slow_tokenizer.MoshiConverter | from .utils import is_protobuf_available, is_sentencepiece_available, logging, requires_backends
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors
class MoshiConverter(SpmConverter):
handle_byte_fallback = True
def __init__(self, vocab_file, model_max_lengt... |
class MoshiConverter(SpmConverter):
def __init__(self, vocab_file, model_max_length=None, **kwargs):
pass
def normalizer(self, proto):
pass
def decoder(self, replacement, add_prefix_space):
pass
def pre_tokenizer(self, replacement, add_prefix_space):
pass | 5 | 0 | 8 | 1 | 7 | 0 | 2 | 0.03 | 1 | 0 | 0 | 0 | 4 | 1 | 4 | 15 | 39 | 7 | 31 | 14 | 26 | 1 | 24 | 13 | 19 | 2 | 2 | 1 | 6 |
232 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/convert_slow_tokenizer.py | transformers.convert_slow_tokenizer.NllbConverter | from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors
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:]... |
class NllbConverter(SpmConverter):
def vocab(self, proto):
pass
def unk_id(self, proto):
pass
def post_processor(self):
pass | 4 | 0 | 7 | 0 | 7 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 3 | 0 | 3 | 14 | 23 | 2 | 21 | 5 | 17 | 0 | 9 | 5 | 5 | 1 | 2 | 0 | 3 |
233 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/convert_slow_tokenizer.py | transformers.convert_slow_tokenizer.OpenAIGPTConverter | from tokenizers.models import BPE, Unigram, WordPiece
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors
class OpenAIGPTConverter(Converter):
def converted(self) -> Tokenizer:
vocab = self.original_tokenizer.encoder
merges = list(self.original_to... |
class OpenAIGPTConverter(Converter):
def converted(self) -> Tokenizer:
pass | 2 | 0 | 24 | 4 | 20 | 0 | 2 | 0 | 1 | 2 | 0 | 0 | 1 | 0 | 1 | 3 | 25 | 4 | 21 | 6 | 19 | 0 | 12 | 6 | 10 | 2 | 1 | 1 | 2 |
234 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/convert_slow_tokenizer.py | transformers.convert_slow_tokenizer.PegasusConverter | from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors
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_se... |
class PegasusConverter(SpmConverter):
def vocab(self, proto):
pass
def unk_id(self, proto):
pass
def pre_tokenizer(self, replacement, add_prefix_space):
pass
def post_processor(self):
pass | 5 | 0 | 9 | 1 | 8 | 0 | 2 | 0 | 1 | 1 | 0 | 0 | 4 | 0 | 4 | 15 | 38 | 6 | 32 | 9 | 27 | 0 | 19 | 9 | 14 | 3 | 2 | 1 | 6 |
235 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/convert_slow_tokenizer.py | transformers.convert_slow_tokenizer.Qwen2Converter | from typing import Optional
from tokenizers.models import BPE, Unigram, WordPiece
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors
class Qwen2Converter(Converter):
def converted(self, vocab: Optional[dict[str, int]]=None, merges: Optional[list[tuple[str, str]]... |
class Qwen2Converter(Converter):
def converted(self, vocab: Optional[dict[str, int]]=None, merges: Optional[list[tuple[str, str]]]=None) -> Tokenizer:
pass | 2 | 0 | 41 | 5 | 36 | 0 | 3 | 0 | 1 | 3 | 0 | 1 | 1 | 1 | 1 | 3 | 42 | 5 | 37 | 4 | 35 | 0 | 12 | 3 | 10 | 3 | 1 | 1 | 3 |
236 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/convert_slow_tokenizer.py | transformers.convert_slow_tokenizer.ReformerConverter | class ReformerConverter(SpmConverter):
pass | class ReformerConverter(SpmConverter):
pass | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 11 | 2 | 0 | 2 | 1 | 1 | 0 | 2 | 1 | 1 | 0 | 2 | 0 | 0 |
237 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/convert_slow_tokenizer.py | transformers.convert_slow_tokenizer.RemBertConverter | from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors
class RemBertConverter(SpmConverter):
def normalizer(self, proto):
list_normalizers = [normalizers.Replace('``', '"'), normalizers.Replace("''", '"'), normalizers.Replace(Regex(' {2,}'), ' ')]
if... |
class RemBertConverter(SpmConverter):
def normalizer(self, proto):
pass
def post_processor(self):
pass | 3 | 0 | 14 | 2 | 12 | 0 | 3 | 0.04 | 1 | 0 | 0 | 0 | 2 | 0 | 2 | 13 | 30 | 4 | 25 | 5 | 22 | 1 | 14 | 5 | 11 | 4 | 2 | 1 | 5 |
238 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/convert_slow_tokenizer.py | transformers.convert_slow_tokenizer.RoFormerConverter | from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import BPE, Unigram, WordPiece
class RoFormerConverter(Converter):
def converted(self) -> Tokenizer:
from .models.roformer.tokenization_utils import JiebaPreTokenizer
vocab... |
class RoFormerConverter(Converter):
def converted(self) -> Tokenizer:
pass | 2 | 0 | 36 | 6 | 30 | 1 | 2 | 0.03 | 1 | 2 | 1 | 0 | 1 | 0 | 1 | 3 | 37 | 6 | 31 | 11 | 28 | 1 | 19 | 11 | 16 | 2 | 1 | 1 | 2 |
239 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/convert_slow_tokenizer.py | transformers.convert_slow_tokenizer.RobertaConverter | from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import BPE, Unigram, WordPiece
class RobertaConverter(Converter):
def converted(self) -> Tokenizer:
ot = self.original_tokenizer
vocab = ot.encoder
merges = list(ot... |
class RobertaConverter(Converter):
def converted(self) -> Tokenizer:
pass | 2 | 0 | 26 | 3 | 23 | 1 | 1 | 0.04 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 3 | 27 | 3 | 24 | 6 | 22 | 1 | 10 | 6 | 8 | 1 | 1 | 0 | 1 |
240 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/convert_slow_tokenizer.py | transformers.convert_slow_tokenizer.SeamlessM4TConverter | from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors
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.pie... |
class SeamlessM4TConverter(SpmConverter):
def vocab(self, proto):
pass
def unk_id(self, proto):
pass
def post_processor(self):
pass | 4 | 0 | 7 | 0 | 7 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 3 | 0 | 3 | 14 | 23 | 2 | 21 | 5 | 17 | 0 | 9 | 5 | 5 | 1 | 2 | 0 | 3 |
241 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/convert_slow_tokenizer.py | transformers.convert_slow_tokenizer.SentencePieceExtractor | from .utils import is_protobuf_available, is_sentencepiece_available, logging, requires_backends
class SentencePieceExtractor:
"""
Extractor implementation for SentencePiece trained models. https://github.com/google/sentencepiece
"""
def __init__(self, model: str):
requires_backends(self, 'sen... |
class SentencePieceExtractor:
'''
Extractor implementation for SentencePiece trained models. https://github.com/google/sentencepiece
'''
def __init__(self, model: str):
pass
def extract(self, vocab_scores=None) -> tuple[dict[str, int], list[tuple]]:
'''
By default will ret... | 3 | 2 | 9 | 2 | 5 | 2 | 1 | 0.64 | 0 | 3 | 0 | 1 | 2 | 1 | 2 | 2 | 23 | 5 | 11 | 8 | 7 | 7 | 11 | 8 | 7 | 1 | 0 | 0 | 2 |
242 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/convert_slow_tokenizer.py | transformers.convert_slow_tokenizer.SplinterConverter | from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import BPE, Unigram, WordPiece
class SplinterConverter(Converter):
def converted(self) -> Tokenizer:
vocab = self.original_tokenizer.vocab
tokenizer = Tokenizer(WordPiece(v... |
class SplinterConverter(Converter):
def converted(self) -> Tokenizer:
pass | 2 | 0 | 47 | 6 | 41 | 1 | 3 | 0.02 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 3 | 48 | 6 | 42 | 16 | 40 | 1 | 27 | 16 | 25 | 3 | 1 | 1 | 3 |
243 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/convert_slow_tokenizer.py | transformers.convert_slow_tokenizer.SpmConverter | from .utils import is_protobuf_available, is_sentencepiece_available, logging, requires_backends
import warnings
from tokenizers.models import BPE, Unigram, WordPiece
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors
class SpmConverter(Converter):
handle_byte_fa... |
class SpmConverter(Converter):
def __init__(self, *args):
pass
def vocab(self, proto):
pass
def unk_id(self, proto):
pass
def tokenizer(self, proto):
pass
def normalizer(self, proto):
pass
def pre_tokenizer(self, replacement, add_prefix_space):
... | 10 | 0 | 13 | 2 | 10 | 1 | 2 | 0.07 | 1 | 3 | 0 | 22 | 9 | 1 | 9 | 11 | 127 | 23 | 98 | 34 | 88 | 7 | 61 | 32 | 51 | 5 | 1 | 1 | 17 |
244 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/convert_slow_tokenizer.py | transformers.convert_slow_tokenizer.T5Converter | from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors
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'<ex... |
class T5Converter(SpmConverter):
def vocab(self, proto):
pass
def post_processor(self):
pass | 3 | 0 | 7 | 0 | 7 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 2 | 0 | 2 | 13 | 15 | 1 | 14 | 5 | 11 | 0 | 8 | 5 | 5 | 1 | 2 | 0 | 2 |
245 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/convert_slow_tokenizer.py | transformers.convert_slow_tokenizer.TikTokenConverter | from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import BPE, Unigram, WordPiece
class TikTokenConverter:
"""
A general tiktoken converter.
"""
def __init__(self, vocab_file=None, pattern="(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n... |
class TikTokenConverter:
'''
A general tiktoken converter.
'''
def __init__(self, vocab_file=None, pattern="(?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, additional_special_tokens=None, *ar... | 6 | 1 | 13 | 1 | 12 | 0 | 2 | 0.05 | 0 | 5 | 0 | 0 | 4 | 4 | 4 | 4 | 72 | 9 | 60 | 30 | 45 | 3 | 45 | 22 | 38 | 6 | 0 | 3 | 11 |
246 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/convert_slow_tokenizer.py | transformers.convert_slow_tokenizer.UdopConverter | from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors
class UdopConverter(SpmConverter):
def post_processor(self):
return processors.TemplateProcessing(single=['$A', '</s>'], pair=['$A', '</s>', '$B', '</s>'], special_tokens=[('</s>', self.original_tokenize... |
class UdopConverter(SpmConverter):
def post_processor(self):
pass | 2 | 0 | 8 | 0 | 8 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 12 | 9 | 0 | 9 | 2 | 7 | 0 | 3 | 2 | 1 | 1 | 2 | 0 | 1 |
247 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/convert_slow_tokenizer.py | transformers.convert_slow_tokenizer.WhisperConverter | from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import BPE, Unigram, WordPiece
class WhisperConverter(Converter):
def converted(self) -> Tokenizer:
vocab = self.original_tokenizer.encoder
merges = list(self.original_toke... |
class WhisperConverter(Converter):
def converted(self) -> Tokenizer:
pass | 2 | 0 | 33 | 4 | 29 | 0 | 1 | 0 | 1 | 2 | 0 | 0 | 1 | 0 | 1 | 3 | 34 | 4 | 30 | 10 | 28 | 0 | 14 | 10 | 12 | 1 | 1 | 0 | 1 |
248 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/convert_slow_tokenizer.py | transformers.convert_slow_tokenizer.XGLMConverter | from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors
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:]... |
class XGLMConverter(SpmConverter):
def vocab(self, proto):
pass
def unk_id(self, proto):
pass
def post_processor(self):
pass | 4 | 0 | 7 | 0 | 7 | 0 | 1 | 0.04 | 1 | 0 | 0 | 0 | 3 | 0 | 3 | 14 | 25 | 2 | 23 | 6 | 19 | 1 | 11 | 6 | 7 | 1 | 2 | 0 | 3 |
249 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/convert_slow_tokenizer.py | transformers.convert_slow_tokenizer.XLMRobertaConverter | from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors
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.piec... |
class XLMRobertaConverter(SpmConverter):
def vocab(self, proto):
pass
def unk_id(self, proto):
pass
def post_processor(self):
pass | 4 | 0 | 7 | 0 | 7 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 3 | 0 | 3 | 14 | 25 | 2 | 23 | 6 | 19 | 0 | 11 | 6 | 7 | 1 | 2 | 0 | 3 |
250 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/convert_slow_tokenizer.py | transformers.convert_slow_tokenizer.XLNetConverter | from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors
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... |
class XLNetConverter(SpmConverter):
def vocab(self, proto):
pass
def normalizer(self, proto):
pass
def post_processor(self):
pass | 4 | 0 | 11 | 1 | 10 | 0 | 2 | 0 | 1 | 0 | 0 | 0 | 3 | 0 | 3 | 14 | 35 | 5 | 30 | 6 | 26 | 0 | 17 | 6 | 13 | 4 | 2 | 1 | 7 |
251 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/data/data_collator.py | transformers.data.data_collator.DataCollatorForLanguageModeling | from typing import Any, Callable, NewType, Optional, Union
import multiprocessing as mp
import numpy as np
from dataclasses import dataclass
from collections.abc import Mapping
from ..tokenization_utils_base import PreTrainedTokenizerBase
@dataclass
class DataCollatorForLanguageModeling(DataCollatorMixin):
"""
... | @dataclass
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`]):
... | 9 | 3 | 30 | 3 | 22 | 5 | 4 | 0.49 | 1 | 6 | 0 | 2 | 7 | 0 | 8 | 9 | 317 | 46 | 184 | 58 | 167 | 90 | 126 | 55 | 112 | 7 | 1 | 2 | 29 |
252 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/data/data_collator.py | transformers.data.data_collator.DataCollatorForPermutationLanguageModeling | from collections.abc import Mapping
from ..tokenization_utils_base import PreTrainedTokenizerBase
from typing import Any, Callable, NewType, Optional, Union
from random import randint
import numpy as np
from dataclasses import dataclass
@dataclass
class DataCollatorForPermutationLanguageModeling(DataCollatorMixin):
... | @dataclass
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
'''... | 6 | 3 | 53 | 7 | 28 | 19 | 5 | 0.71 | 1 | 7 | 0 | 0 | 6 | 0 | 6 | 7 | 336 | 48 | 171 | 61 | 162 | 122 | 135 | 61 | 126 | 7 | 1 | 2 | 27 |
253 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/data/data_collator.py | transformers.data.data_collator.DataCollatorForSOP | import warnings
from dataclasses import dataclass
from typing import Any, Callable, NewType, Optional, Union
@dataclass
class DataCollatorForSOP(DataCollatorForLanguageModeling):
"""
Data collator used for sentence order prediction task.
- collates batches of tensors, honoring their tokenizer's pad_token
... | @dataclass
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 __i... | 5 | 2 | 23 | 3 | 17 | 4 | 2 | 0.31 | 1 | 5 | 0 | 0 | 3 | 1 | 3 | 12 | 80 | 13 | 52 | 22 | 45 | 16 | 37 | 22 | 30 | 4 | 2 | 1 | 6 |
254 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/data/data_collator.py | transformers.data.data_collator.DataCollatorForSeq2Seq | from typing import Any, Callable, NewType, Optional, Union
from ..tokenization_utils_base import PreTrainedTokenizerBase
from dataclasses import dataclass
import numpy as np
from ..utils import PaddingStrategy
@dataclass
class DataCollatorForSeq2Seq:
"""
Data collator that will dynamically pad the inputs recei... | @dataclass
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`], *option... | 3 | 1 | 91 | 9 | 76 | 6 | 17 | 0.4 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 133 | 15 | 84 | 19 | 80 | 34 | 43 | 19 | 39 | 17 | 0 | 3 | 17 |
255 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/data/data_collator.py | transformers.data.data_collator.DataCollatorForTokenClassification | from dataclasses import dataclass
import numpy as np
from typing import Any, Callable, NewType, Optional, Union
from ..tokenization_utils_base import PreTrainedTokenizerBase
from ..utils import PaddingStrategy
@dataclass
class DataCollatorForTokenClassification(DataCollatorMixin):
"""
Data collator that will d... | @dataclass
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.
padd... | 5 | 1 | 26 | 4 | 22 | 1 | 5 | 0.29 | 1 | 2 | 0 | 0 | 3 | 0 | 3 | 4 | 137 | 22 | 89 | 28 | 82 | 26 | 53 | 28 | 46 | 6 | 1 | 1 | 19 |
256 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/data/data_collator.py | transformers.data.data_collator.DataCollatorForWholeWordMask | from collections.abc import Mapping
import warnings
import random
from typing import Any, Callable, NewType, Optional, Union
from ..models.bert import BertTokenizer, BertTokenizerFast
import numpy as np
from dataclasses import dataclass
@dataclass
class DataCollatorForWholeWordMask(DataCollatorForLanguageModeling):
... | @dataclass
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... | 8 | 4 | 35 | 5 | 26 | 6 | 6 | 0.27 | 1 | 11 | 2 | 0 | 7 | 1 | 7 | 16 | 269 | 46 | 180 | 76 | 169 | 49 | 156 | 76 | 145 | 14 | 2 | 4 | 44 |
257 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/data/data_collator.py | transformers.data.data_collator.DataCollatorMixin | class DataCollatorMixin:
def __call__(self, features, return_tensors=None):
if return_tensors is None:
return_tensors = self.return_tensors
if return_tensors == 'pt':
return self.torch_call(features)
elif return_tensors == 'np':
return self.numpy_call(fea... | class DataCollatorMixin:
def __call__(self, features, return_tensors=None):
pass | 2 | 0 | 11 | 0 | 11 | 0 | 5 | 0 | 0 | 1 | 0 | 4 | 1 | 0 | 1 | 1 | 12 | 0 | 12 | 2 | 10 | 0 | 9 | 2 | 7 | 5 | 0 | 1 | 5 |
258 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/data/data_collator.py | transformers.data.data_collator.DataCollatorWithFlattening | import numpy as np
from dataclasses import dataclass
@dataclass
class DataCollatorWithFlattening(DefaultDataCollator):
"""
Data collator used for padding free approach. Does the following:
- concatenates the entire mini batch into single long sequence of shape [1, total_tokens]
- uses `separator_id` t... | @dataclass
class DataCollatorWithFlattening(DefaultDataCollator):
'''
Data collator used for padding free approach. Does the following:
- concatenates the entire mini batch into single long sequence of shape [1, total_tokens]
- uses `separator_id` to separate sequences within the concatenated `labels`, ... | 4 | 1 | 13 | 0 | 13 | 0 | 4 | 0.22 | 1 | 3 | 0 | 0 | 2 | 2 | 2 | 4 | 36 | 3 | 27 | 8 | 24 | 6 | 23 | 8 | 20 | 7 | 2 | 2 | 8 |
259 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/data/data_collator.py | transformers.data.data_collator.DataCollatorWithPadding | from ..tokenization_utils_base import PreTrainedTokenizerBase
from ..utils import PaddingStrategy
from dataclasses import dataclass
from typing import Any, Callable, NewType, Optional, Union
@dataclass
class DataCollatorWithPadding:
"""
Data collator that will dynamically pad the inputs received.
Args:
... | @dataclass
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`], *op... | 3 | 1 | 16 | 0 | 16 | 0 | 3 | 1 | 0 | 2 | 0 | 0 | 1 | 0 | 1 | 1 | 49 | 5 | 22 | 7 | 20 | 22 | 15 | 7 | 13 | 3 | 0 | 1 | 3 |
260 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/data/data_collator.py | transformers.data.data_collator.DefaultDataCollator | from typing import Any, Callable, NewType, Optional, Union
from dataclasses import dataclass
@dataclass
class DefaultDataCollator(DataCollatorMixin):
"""
Very simple data collator that simply collates batches of dict-like objects and performs special handling for
potential keys named:
- `label`: h... | @dataclass
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... | 3 | 1 | 4 | 0 | 4 | 0 | 2 | 2.17 | 1 | 2 | 0 | 1 | 1 | 0 | 1 | 2 | 25 | 6 | 6 | 3 | 4 | 13 | 6 | 3 | 4 | 2 | 1 | 1 | 2 |
261 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/data/datasets/glue.py | transformers.data.datasets.glue.GlueDataTrainingArguments | from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from dataclasses import dataclass, field
@dataclass
class GlueDataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
Using `HfArgumentParser` we ... | @dataclass
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.
'''
def __post_init__(self):
p... | 3 | 1 | 2 | 0 | 2 | 0 | 1 | 0.26 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 27 | 3 | 19 | 6 | 17 | 5 | 7 | 6 | 5 | 1 | 0 | 0 | 1 |
262 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/data/datasets/glue.py | transformers.data.datasets.glue.GlueDataset | from filelock import FileLock
from torch.utils.data import Dataset
import warnings
import torch
from ...utils import check_torch_load_is_safe, logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from typing import Optional, Union
from ...tokenization_utils_base im... |
class GlueDataset(Dataset):
def __init__(self, args: GlueDataTrainingArguments, tokenizer: PreTrainedTokenizerBase, limit_length: Optional[int]=None, mode: Union[str, Split]=Split.train, cache_dir: Optional[str]=None):
pass
def __len__(self):
pass
def __getitem__(self, i) -> InputFeature... | 5 | 0 | 20 | 1 | 18 | 1 | 3 | 0.11 | 1 | 8 | 4 | 0 | 4 | 2 | 4 | 4 | 91 | 7 | 76 | 19 | 64 | 8 | 42 | 12 | 37 | 9 | 1 | 3 | 12 |
263 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/data/datasets/language_modeling.py | transformers.data.datasets.language_modeling.LineByLineTextDataset | import os
import warnings
import torch
from ...tokenization_utils import PreTrainedTokenizer
from torch.utils.data import Dataset
class LineByLineTextDataset(Dataset):
def __init__(self, tokenizer: PreTrainedTokenizer, file_path: str, block_size: int):
warnings.warn(DEPRECATION_WARNING.format('https://git... |
class LineByLineTextDataset(Dataset):
def __init__(self, tokenizer: PreTrainedTokenizer, file_path: str, block_size: int):
pass
def __len__(self):
pass
def __getitem__(self, i) -> dict[str, torch.tensor]:
pass | 4 | 0 | 8 | 1 | 6 | 1 | 1 | 0.3 | 1 | 5 | 1 | 0 | 3 | 1 | 3 | 3 | 31 | 5 | 20 | 8 | 16 | 6 | 15 | 7 | 11 | 2 | 1 | 1 | 4 |
264 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/data/datasets/language_modeling.py | transformers.data.datasets.language_modeling.LineByLineWithRefDataset | from torch.utils.data import Dataset
import os
from ...tokenization_utils import PreTrainedTokenizer
import json
import torch
import warnings
class LineByLineWithRefDataset(Dataset):
def __init__(self, tokenizer: PreTrainedTokenizer, file_path: str, block_size: int, ref_path: str):
warnings.warn(DEPRECATI... |
class LineByLineWithRefDataset(Dataset):
def __init__(self, tokenizer: PreTrainedTokenizer, file_path: str, block_size: int, ref_path: str):
pass
def __len__(self):
pass
def __getitem__(self, i) -> dict[str, torch.tensor]:
pass | 4 | 0 | 13 | 1 | 11 | 2 | 2 | 0.24 | 1 | 6 | 1 | 0 | 3 | 1 | 3 | 3 | 46 | 5 | 34 | 11 | 30 | 8 | 26 | 10 | 22 | 5 | 1 | 1 | 7 |
265 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/data/datasets/language_modeling.py | transformers.data.datasets.language_modeling.LineByLineWithSOPTextDataset | import random
from ...tokenization_utils import PreTrainedTokenizer
from torch.utils.data import Dataset
import os
import torch
import warnings
class LineByLineWithSOPTextDataset(Dataset):
"""
Dataset for sentence order prediction task, prepare sentence pairs for SOP task
"""
def __init__(self, tokeni... |
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):
pass
def create_examples_from_document(self, document, block_size, token... | 6 | 3 | 31 | 2 | 23 | 7 | 6 | 0.36 | 1 | 6 | 1 | 0 | 4 | 1 | 4 | 4 | 150 | 16 | 104 | 33 | 98 | 37 | 87 | 32 | 81 | 14 | 1 | 5 | 30 |
266 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/data/datasets/language_modeling.py | transformers.data.datasets.language_modeling.TextDataset | import torch
import warnings
from filelock import FileLock
import os
from torch.utils.data import Dataset
import time
import pickle
from typing import Optional
from ...tokenization_utils import PreTrainedTokenizer
class TextDataset(Dataset):
def __init__(self, tokenizer: PreTrainedTokenizer, file_path: str, block... |
class TextDataset(Dataset):
def __init__(self, tokenizer: PreTrainedTokenizer, file_path: str, block_size: int, overwrite_cache=False, cache_dir: Optional[str]=None):
pass
def __len__(self):
pass
def __getitem__(self, i) -> torch.Tensor:
pass | 4 | 0 | 21 | 3 | 17 | 2 | 2 | 0.17 | 1 | 7 | 1 | 0 | 3 | 1 | 3 | 3 | 71 | 11 | 52 | 21 | 41 | 9 | 30 | 12 | 26 | 5 | 1 | 3 | 7 |
267 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/data/datasets/language_modeling.py | transformers.data.datasets.language_modeling.TextDatasetForNextSentencePrediction | from ...tokenization_utils import PreTrainedTokenizer
import pickle
import os
from filelock import FileLock
import warnings
import torch
import time
from torch.utils.data import Dataset
import random
class TextDatasetForNextSentencePrediction(Dataset):
def __init__(self, tokenizer: PreTrainedTokenizer, file_path:... |
class TextDatasetForNextSentencePrediction(Dataset):
def __init__(self, tokenizer: PreTrainedTokenizer, file_path: str, block_size: int, overwrite_cache=False, short_seq_probability=0.1, nsp_probability=0.5):
pass
def create_examples_from_document(self, document: list[list[int]], doc_index: int, bloc... | 5 | 1 | 44 | 6 | 30 | 9 | 7 | 0.32 | 1 | 7 | 1 | 0 | 4 | 5 | 4 | 4 | 185 | 29 | 119 | 47 | 106 | 38 | 93 | 37 | 88 | 16 | 1 | 6 | 26 |
268 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/data/datasets/squad.py | transformers.data.datasets.squad.Split | from enum import Enum
class Split(Enum):
train = 'train'
dev = 'dev' |
class Split(Enum):
pass | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 49 | 3 | 0 | 3 | 3 | 2 | 0 | 3 | 3 | 2 | 0 | 4 | 0 | 0 |
269 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/data/datasets/squad.py | transformers.data.datasets.squad.SquadDataTrainingArguments | from dataclasses import dataclass, field
@dataclass
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)})
... | @dataclass
class SquadDataTrainingArguments:
'''
Arguments pertaining to what data we are going to input our model for training and eval.
'''
pass | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0.05 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 64 | 1 | 60 | 13 | 59 | 3 | 13 | 13 | 12 | 0 | 0 | 0 | 0 |
270 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/data/datasets/squad.py | transformers.data.datasets.squad.SquadDataset | from ...utils import check_torch_load_is_safe, logging
from filelock import FileLock
import time
from ..processors.squad import SquadFeatures, SquadV1Processor, SquadV2Processor, squad_convert_examples_to_features
import os
import torch
from ...tokenization_utils import PreTrainedTokenizer
from typing import Optional, ... |
class SquadDataset(Dataset):
def __init__(self, args: SquadDataTrainingArguments, tokenizer: PreTrainedTokenizer, limit_length: Optional[int]=None, mode: Union[str, Split]=Split.train, is_language_sensitive: Optional[bool]=False, cache_dir: Optional[str]=None, dataset_format: Optional[str]='pt'):
pass
... | 4 | 0 | 36 | 4 | 30 | 2 | 5 | 0.1 | 1 | 11 | 5 | 0 | 3 | 4 | 3 | 3 | 121 | 15 | 96 | 31 | 83 | 10 | 59 | 22 | 55 | 9 | 1 | 3 | 16 |
271 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/data/processors/glue.py | transformers.data.processors.glue.ColaProcessor | import warnings
from .utils import DataProcessor, InputExample, InputFeatures
import os
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('pro... |
class ColaProcessor(DataProcessor):
'''Processor for the CoLA data set (GLUE version).'''
def __init__(self, *args, **kwargs):
pass
def get_example_from_tensor_dict(self, tensor_dict):
'''See base class.'''
pass
def get_train_examples(self, data_dir):
'''See base clas... | 8 | 7 | 5 | 0 | 4 | 1 | 2 | 0.23 | 1 | 5 | 1 | 0 | 7 | 0 | 7 | 14 | 45 | 7 | 31 | 15 | 23 | 7 | 26 | 15 | 18 | 5 | 1 | 1 | 11 |
272 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/data/processors/glue.py | transformers.data.processors.glue.MnliMismatchedProcessor | import os
import warnings
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... |
class MnliMismatchedProcessor(MnliProcessor):
'''Processor for the MultiNLI Mismatched data set (GLUE version).'''
def __init__(self, *args, **kwargs):
pass
def get_dev_examples(self, data_dir):
'''See base class.'''
pass
def get_test_examples(self, data_dir):
'''See ... | 4 | 3 | 3 | 0 | 2 | 1 | 1 | 0.38 | 1 | 2 | 0 | 0 | 3 | 0 | 3 | 17 | 14 | 3 | 8 | 4 | 4 | 3 | 8 | 4 | 4 | 1 | 2 | 0 | 3 |
273 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/data/processors/glue.py | transformers.data.processors.glue.MnliProcessor | import warnings
from .utils import DataProcessor, InputExample, InputFeatures
import os
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(... |
class MnliProcessor(DataProcessor):
'''Processor for the MultiNLI data set (GLUE version).'''
def __init__(self, *args, **kwargs):
pass
def get_example_from_tensor_dict(self, tensor_dict):
'''See base class.'''
pass
def get_train_examples(self, data_dir):
'''See base ... | 8 | 7 | 5 | 0 | 4 | 1 | 1 | 0.23 | 1 | 5 | 1 | 1 | 7 | 0 | 7 | 14 | 44 | 7 | 30 | 14 | 22 | 7 | 25 | 14 | 17 | 4 | 1 | 2 | 10 |
274 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/data/processors/glue.py | transformers.data.processors.glue.MrpcProcessor | import os
import warnings
from .utils import DataProcessor, InputExample, InputFeatures
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('pro... |
class MrpcProcessor(DataProcessor):
'''Processor for the MRPC data set (GLUE version).'''
def __init__(self, *args, **kwargs):
pass
def get_example_from_tensor_dict(self, tensor_dict):
'''See base class.'''
pass
def get_train_examples(self, data_dir):
'''See base clas... | 8 | 7 | 5 | 0 | 4 | 1 | 1 | 0.23 | 1 | 5 | 1 | 0 | 7 | 0 | 7 | 14 | 45 | 7 | 31 | 14 | 23 | 7 | 26 | 14 | 18 | 4 | 1 | 2 | 10 |
275 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/data/processors/glue.py | transformers.data.processors.glue.OutputMode | from enum import Enum
class OutputMode(Enum):
classification = 'classification'
regression = 'regression' |
class OutputMode(Enum):
pass | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 49 | 3 | 0 | 3 | 3 | 2 | 0 | 3 | 3 | 2 | 0 | 4 | 0 | 0 |
276 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/data/processors/glue.py | transformers.data.processors.glue.QnliProcessor | import warnings
from .utils import DataProcessor, InputExample, InputFeatures
import os
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('pro... |
class QnliProcessor(DataProcessor):
'''Processor for the QNLI data set (GLUE version).'''
def __init__(self, *args, **kwargs):
pass
def get_example_from_tensor_dict(self, tensor_dict):
'''See base class.'''
pass
def get_train_examples(self, data_dir):
'''See base clas... | 8 | 7 | 5 | 0 | 4 | 1 | 1 | 0.23 | 1 | 5 | 1 | 0 | 7 | 0 | 7 | 14 | 44 | 7 | 30 | 14 | 22 | 7 | 25 | 14 | 17 | 4 | 1 | 2 | 10 |
277 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/data/processors/glue.py | transformers.data.processors.glue.QqpProcessor | from .utils import DataProcessor, InputExample, InputFeatures
import os
import warnings
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('proce... |
class QqpProcessor(DataProcessor):
'''Processor for the QQP data set (GLUE version).'''
def __init__(self, *args, **kwargs):
pass
def get_example_from_tensor_dict(self, tensor_dict):
'''See base class.'''
pass
def get_train_examples(self, data_dir):
'''See base class.... | 8 | 7 | 6 | 0 | 5 | 1 | 2 | 0.19 | 1 | 6 | 1 | 0 | 7 | 0 | 7 | 14 | 50 | 7 | 36 | 17 | 28 | 7 | 31 | 17 | 23 | 7 | 1 | 2 | 13 |
278 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/data/processors/glue.py | transformers.data.processors.glue.RteProcessor | from .utils import DataProcessor, InputExample, InputFeatures
import warnings
import os
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('proce... |
class RteProcessor(DataProcessor):
'''Processor for the RTE data set (GLUE version).'''
def __init__(self, *args, **kwargs):
pass
def get_example_from_tensor_dict(self, tensor_dict):
'''See base class.'''
pass
def get_train_examples(self, data_dir):
'''See base class.... | 8 | 7 | 5 | 0 | 4 | 1 | 1 | 0.23 | 1 | 5 | 1 | 0 | 7 | 0 | 7 | 14 | 44 | 7 | 30 | 14 | 22 | 7 | 25 | 14 | 17 | 4 | 1 | 2 | 10 |
279 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/data/processors/glue.py | transformers.data.processors.glue.Sst2Processor | import warnings
import os
from .utils import DataProcessor, InputExample, InputFeatures
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('pr... |
class Sst2Processor(DataProcessor):
'''Processor for the SST-2 data set (GLUE version).'''
def __init__(self, *args, **kwargs):
pass
def get_example_from_tensor_dict(self, tensor_dict):
'''See base class.'''
pass
def get_train_examples(self, data_dir):
'''See base cla... | 8 | 7 | 5 | 0 | 4 | 1 | 2 | 0.23 | 1 | 5 | 1 | 0 | 7 | 0 | 7 | 14 | 44 | 7 | 30 | 14 | 22 | 7 | 25 | 14 | 17 | 5 | 1 | 2 | 11 |
280 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/data/processors/glue.py | transformers.data.processors.glue.StsbProcessor | from .utils import DataProcessor, InputExample, InputFeatures
import os
import warnings
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('pr... |
class StsbProcessor(DataProcessor):
'''Processor for the STS-B data set (GLUE version).'''
def __init__(self, *args, **kwargs):
pass
def get_example_from_tensor_dict(self, tensor_dict):
'''See base class.'''
pass
def get_train_examples(self, data_dir):
'''See base cla... | 8 | 7 | 5 | 0 | 4 | 1 | 1 | 0.23 | 1 | 5 | 1 | 0 | 7 | 0 | 7 | 14 | 44 | 7 | 30 | 14 | 22 | 7 | 25 | 14 | 17 | 4 | 1 | 2 | 10 |
281 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/data/processors/glue.py | transformers.data.processors.glue.WnliProcessor | import os
from .utils import DataProcessor, InputExample, InputFeatures
import warnings
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('pro... |
class WnliProcessor(DataProcessor):
'''Processor for the WNLI data set (GLUE version).'''
def __init__(self, *args, **kwargs):
pass
def get_example_from_tensor_dict(self, tensor_dict):
'''See base class.'''
pass
def get_train_examples(self, data_dir):
'''See base clas... | 8 | 7 | 5 | 0 | 4 | 1 | 1 | 0.23 | 1 | 5 | 1 | 0 | 7 | 0 | 7 | 14 | 44 | 7 | 30 | 14 | 22 | 7 | 25 | 14 | 17 | 4 | 1 | 2 | 10 |
282 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/data/processors/squad.py | transformers.data.processors.squad.SquadExample | 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 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_character... | 2 | 1 | 46 | 5 | 39 | 2 | 5 | 0.35 | 0 | 0 | 0 | 0 | 1 | 11 | 1 | 1 | 61 | 7 | 40 | 26 | 28 | 14 | 26 | 16 | 24 | 5 | 0 | 3 | 5 |
283 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/data/processors/squad.py | transformers.data.processors.squad.SquadFeatures | from typing import Optional
from ...tokenization_utils_base import BatchEncoding, PreTrainedTokenizerBase, TruncationStrategy
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 ... |
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: ... | 2 | 1 | 38 | 3 | 35 | 0 | 1 | 0.67 | 0 | 2 | 1 | 0 | 1 | 16 | 1 | 1 | 65 | 5 | 36 | 36 | 16 | 24 | 18 | 18 | 16 | 1 | 0 | 0 | 1 |
284 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/data/processors/squad.py | transformers.data.processors.squad.SquadProcessor | from tqdm import tqdm
import json
import os
from .utils import DataProcessor
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_... |
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.
'''
def _get_example_from_tensor_dict(self, tensor_dict, evaluate=False):
pass
def get_exampl... | 6 | 4 | 26 | 4 | 17 | 6 | 4 | 0.37 | 1 | 4 | 1 | 2 | 5 | 0 | 5 | 12 | 144 | 26 | 86 | 32 | 80 | 32 | 59 | 30 | 53 | 6 | 1 | 5 | 19 |
285 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/data/processors/squad.py | transformers.data.processors.squad.SquadResult | 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 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 correspo... | 2 | 1 | 9 | 1 | 8 | 0 | 2 | 0.78 | 0 | 0 | 0 | 0 | 1 | 6 | 1 | 1 | 19 | 3 | 9 | 8 | 7 | 7 | 9 | 8 | 7 | 2 | 0 | 1 | 2 |
286 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/data/processors/squad.py | transformers.data.processors.squad.SquadV1Processor | class SquadV1Processor(SquadProcessor):
train_file = 'train-v1.1.json'
dev_file = 'dev-v1.1.json' | class SquadV1Processor(SquadProcessor):
pass | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 12 | 3 | 0 | 3 | 3 | 2 | 0 | 3 | 3 | 2 | 0 | 2 | 0 | 0 |
287 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/data/processors/squad.py | transformers.data.processors.squad.SquadV2Processor | class SquadV2Processor(SquadProcessor):
train_file = 'train-v2.0.json'
dev_file = 'dev-v2.0.json' | class SquadV2Processor(SquadProcessor):
pass | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 12 | 3 | 0 | 3 | 3 | 2 | 0 | 3 | 3 | 2 | 0 | 2 | 0 | 0 |
288 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/data/processors/utils.py | transformers.data.processors.utils.DataProcessor | import csv
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.
Args:
tensor_dict: Keys and values should match the corresponding Glue
... |
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.
Args:
tensor_dict: Keys and values should match the corresponding Glue
tens... | 9 | 8 | 5 | 0 | 2 | 2 | 1 | 0.84 | 0 | 3 | 0 | 12 | 6 | 0 | 7 | 7 | 43 | 8 | 19 | 10 | 10 | 16 | 18 | 8 | 10 | 2 | 0 | 1 | 8 |
289 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/data/processors/utils.py | transformers.data.processors.utils.InputExample | from dataclasses import dataclass
import json
from typing import Optional, Union
import dataclasses
@dataclass
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... | @dataclass
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... | 3 | 2 | 3 | 0 | 2 | 1 | 1 | 1.71 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 22 | 3 | 7 | 4 | 5 | 12 | 7 | 4 | 5 | 1 | 0 | 0 | 1 |
290 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/data/processors/utils.py | transformers.data.processors.utils.InputFeatures | import json
import dataclasses
from typing import Optional, Union
from dataclasses import dataclass
@dataclass(frozen=True)
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 sequenc... | @dataclass(frozen=True)
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 toke... | 3 | 2 | 3 | 0 | 2 | 1 | 1 | 1.86 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 23 | 3 | 7 | 5 | 5 | 13 | 7 | 5 | 5 | 1 | 0 | 0 | 1 |
291 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/data/processors/utils.py | transformers.data.processors.utils.SingleSentenceClassificationProcessor | from ...utils import is_torch_available, logging
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 e... |
class SingleSentenceClassificationProcessor(DataProcessor):
'''Generic processor for a single sentence classification data set.'''
def __init__(self, labels=None, examples=None, mode='classification', verbose=False):
pass
def __len__(self):
pass
def __getitem__(self, idx):
pa... | 11 | 2 | 24 | 2 | 19 | 2 | 5 | 0.12 | 1 | 13 | 2 | 0 | 6 | 4 | 8 | 15 | 225 | 30 | 174 | 65 | 137 | 21 | 118 | 41 | 105 | 22 | 1 | 2 | 46 |
292 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/data/processors/xnli.py | transformers.data.processors.xnli.XnliProcessor | from .utils import DataProcessor, InputExample
import os
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... |
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):
pass
def get_train_examples(self... | 5 | 4 | 12 | 0 | 11 | 1 | 4 | 0.15 | 1 | 4 | 1 | 0 | 4 | 2 | 4 | 11 | 57 | 4 | 46 | 23 | 41 | 7 | 46 | 23 | 41 | 8 | 1 | 2 | 17 |
293 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/debug_utils.py | transformers.debug_utils.DebugOption | from .utils import ExplicitEnum, is_torch_available, logging
class DebugOption(ExplicitEnum):
UNDERFLOW_OVERFLOW = 'underflow_overflow'
TPU_METRICS_DEBUG = 'tpu_metrics_debug' |
class DebugOption(ExplicitEnum):
pass | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 3 | 3 | 2 | 0 | 3 | 3 | 2 | 0 | 1 | 0 | 0 |
294 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/debug_utils.py | transformers.debug_utils.DebugUnderflowOverflow | import collections
class DebugUnderflowOverflow:
"""
This debug class helps detect and understand where the model starts getting very large or very small, and more
importantly `nan` or `inf` weight and activation elements.
There are 2 working modes:
1. Underflow/overflow detection (default)
2... |
class DebugUnderflowOverflow:
'''
This debug class helps detect and understand where the model starts getting very large or very small, and more
importantly `nan` or `inf` weight and activation elements.
There are 2 working modes:
1. Underflow/overflow detection (default)
2. Specific batch abso... | 15 | 1 | 9 | 1 | 7 | 1 | 2 | 1.06 | 0 | 3 | 0 | 0 | 14 | 10 | 14 | 14 | 264 | 63 | 98 | 30 | 83 | 104 | 87 | 30 | 72 | 9 | 0 | 4 | 33 |
295 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/feature_extraction_sequence_utils.py | transformers.feature_extraction_sequence_utils.SequenceFeatureExtractor | from .utils import PaddingStrategy, TensorType, is_torch_tensor, logging, to_numpy
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from typing import Optional, Union
class SequenceFeatureExtractor(FeatureExtractionMixin):
"""
This is a general feature extraction cl... |
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... | 6 | 5 | 65 | 9 | 36 | 20 | 10 | 0.62 | 1 | 12 | 1 | 15 | 5 | 5 | 5 | 17 | 343 | 52 | 180 | 58 | 146 | 112 | 107 | 30 | 101 | 22 | 2 | 3 | 48 |
296 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/feature_extraction_utils.py | transformers.feature_extraction_utils.BatchFeature | import numpy as np
from typing import TYPE_CHECKING, Any, Optional, TypeVar, Union
from collections import UserDict
from .utils import FEATURE_EXTRACTOR_NAME, PROCESSOR_NAME, PushToHubMixin, TensorType, copy_func, download_url, is_numpy_array, is_offline_mode, is_remote_url, is_torch_available, is_torch_device, is_torc... |
class BatchFeature(UserDict):
'''
Holds the output of the [`~SequenceFeatureExtractor.pad`] and feature extractor specific `__call__` methods.
This class is derived from a python dictionary and can be used as a dictionary.
Args:
data (`dict`, *optional*):
Dictionary of lists/arrays/... | 12 | 4 | 14 | 1 | 10 | 3 | 3 | 0.46 | 1 | 12 | 0 | 1 | 11 | 1 | 11 | 66 | 189 | 29 | 112 | 29 | 94 | 52 | 93 | 29 | 75 | 9 | 8 | 3 | 41 |
297 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/feature_extraction_utils.py | transformers.feature_extraction_utils.FeatureExtractionMixin | from .dynamic_module_utils import custom_object_save
from .utils.hub import cached_file
from typing import TYPE_CHECKING, Any, Optional, TypeVar, Union
import os
import copy
import json
import warnings
from .utils import FEATURE_EXTRACTOR_NAME, PROCESSOR_NAME, PushToHubMixin, TensorType, copy_func, download_url, is_num... |
class FeatureExtractionMixin(PushToHubMixin):
'''
This is a feature extraction mixin used to provide saving/loading functionality for sequential and image feature
extractors.
'''
def __init__(self, **kwargs):
'''Set elements of `kwargs` as attributes.'''
pass
def _set_processo... | 18 | 12 | 35 | 5 | 18 | 13 | 4 | 0.72 | 1 | 10 | 0 | 2 | 7 | 1 | 12 | 12 | 443 | 68 | 218 | 71 | 188 | 157 | 152 | 51 | 138 | 15 | 1 | 2 | 48 |
298 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/generation/beam_constraints.py | transformers.generation.beam_constraints.Constraint | from abc import ABC, abstractmethod
class Constraint(ABC):
"""Abstract base class for all constraints that can be applied during generation.
It must define how the constraint can be satisfied.
All classes that inherit Constraint must follow the requirement that
```py
completed = False
while n... |
class Constraint(ABC):
'''Abstract base class for all constraints that can be applied during generation.
It must define how the constraint can be satisfied.
All classes that inherit Constraint must follow the requirement that
```py
completed = False
while not completed:
_, completed = c... | 15 | 8 | 12 | 1 | 5 | 6 | 2 | 1.12 | 1 | 3 | 0 | 2 | 8 | 0 | 8 | 28 | 125 | 19 | 50 | 19 | 35 | 56 | 30 | 13 | 21 | 6 | 4 | 2 | 13 |
299 | huggingface/pytorch-pretrained-BERT | huggingface_pytorch-pretrained-BERT/src/transformers/generation/beam_constraints.py | transformers.generation.beam_constraints.ConstraintListState | from typing import Optional
class ConstraintListState:
"""
A class for beam scorers to track its progress through a list of constraints.
Args:
constraints (`list[Constraint]`):
A list of [`Constraint`] objects that must be fulfilled by the beam scorer.
"""
def __init__(self, c... |
class ConstraintListState:
'''
A class for beam scorers to track its progress through a list of constraints.
Args:
constraints (`list[Constraint]`):
A list of [`Constraint`] objects that must be fulfilled by the beam scorer.
'''
def __init__(self, constraints: list[Constraint])... | 8 | 3 | 22 | 5 | 13 | 5 | 5 | 0.46 | 0 | 6 | 1 | 0 | 7 | 7 | 7 | 7 | 171 | 40 | 92 | 25 | 84 | 42 | 80 | 25 | 72 | 14 | 0 | 5 | 33 |
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