The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
The information about the size of the dataset is not coherent.
Error code: UnexpectedError
Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
code string | code_codestyle int64 | style_context string | style_context_codestyle int64 | label int64 |
|---|---|---|---|---|
'''simple docstring'''
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spect... | 0 |
'''simple docstring'''
import sys
UpperCamelCase__ : int = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"6... | 0 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, r... | 0 |
'''simple docstring'''
UpperCamelCase__ : Dict = {
"a": "AAAAA",
"b": "AAAAB",
"c": "AAABA",
"d": "AAABB",
"e": "AABAA",
"f": "AABAB",
"g": "AABBA",
"h": "AABBB",
"i": "ABAAA",
"j": "BBBAA",
"k": "ABAAB",
"l": "ABABA",
"m": "ABABB",
"n": "A... | 0 | 1 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _a (_lowerCamelCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = ['image_processor', 'tokenizer']
SCREAMING_SNAKE_CASE ... | 0 |
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Any:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = [
"""encoder.version""",
... | 0 | 1 |
'''simple docstring'''
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class _a :
"""simple docstring"""
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE ... | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase__ : str = {
"configuration_canine": ["CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP", "CanineConfig"],
"tokenizat... | 0 | 1 |
'''simple docstring'''
import os
# Precomputes a list of the 100 first triangular numbers
UpperCamelCase__ : List[str] = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def lowerCAmelCase_ ( ) -> str:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = os.path.dirname... | 0 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _a (_lowerCamelCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = ['image_processor', 'tokenizer']
SCREAMING_SNAKE_CASE ... | 0 | 1 |
'''simple docstring'''
import itertools
import math
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> bool:
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even ... | 0 |
'''simple docstring'''
from sklearn.metrics import matthews_corrcoef
import datasets
UpperCamelCase__ : List[str] = "\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass c... | 0 | 1 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
UpperCamelCase__ : Optional[int] = True
except (ImportError, ModuleNotFoundError):
UpperCamelCase__ : Any = False
if NLTK_AVAILABLE:
with FileLock(".lock") as lock:
... | 0 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Tuple:
"""simple docstring"""
print(F"Vertex\tShortest Distance from vertex {src}" )
for i, d in enumerate(SCREAMING_SNAKE_CASE_ ):
... | 0 | 1 |
'''simple docstring'''
import numpy as np
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> np.ndarray:
"""simple docstring"""
return 1 / (1 + np.exp(-vector ))
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> np.ndarray:
"""simple docstring"""
return ve... | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, r... | 0 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ : Optional[Any] = logging.get_logger(__name__)
UpperCamelCase__ : List[str] = {
"studio-ousia/luke-base": "https://huggingface.co/studio-ousia/luke-... | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
UpperCamelCase__ : int = {"tokenization_herbert": ["HerbertTokenizer"]}
try:
if not is_tokenizers_available():
raise OptionalDepend... | 0 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase__ : Optional[Any] = {"configuration_mmbt": ["MMBTConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNot... | 0 |
'''simple docstring'''
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVision... | 0 | 1 |
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common ... | 0 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class _a (_lowerCamelCase):
"""simple docstring"""
def __init__( self , A__ , A__ ) -> Any:
_SCREAMING_SNAKE_CASE ... | 0 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase__ : List[Any] = logging.get_logger(__name__)
UpperC... | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase__ : List[Any] = logging.get_logger(__name__)
UpperC... | 0 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
UpperCamelCase__ : List[Any] = logging.get_logger(__name__)
class _a (_lowerCamelCase):
"""simple docstring"""
def __init__( self ,... | 0 |
'''simple docstring'''
import os
import unittest
from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transfo... | 0 | 1 |
'''simple docstring'''
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
UpperCamelCase__ : Optional[Any] = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
" (KHTML, like Gecko) Chrome/70.0.3538... | 0 |
'''simple docstring'''
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
UpperCamelCase__ : Tuple = logging.getLogger(__name__)
i... | 0 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ : Tuple = logging.get_logger(__name__)
UpperCamelCase__ : List[str] = {
"RWKV/rwkv-4-169m-pile": "https://huggingface.co/RWKV/rwkv-4-169m-pile/resol... | 0 |
'''simple docstring'''
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> str:
"""simple docstring"""
return "".join([hex(SCREAMING_SNAKE_CASE_ )[2:].zfill(2 ).upper() for byte in list(SCREAMING_SNAKE_CASE_ )] )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) ... | 0 | 1 |
'''simple docstring'''
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class _a :
"""simple docstring"""
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
def lowerCAmelCase_ ( ... | 0 |
'''simple docstring'''
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def lowerCAmelCase_ ( ) -> List[Any]:
"""simple docstring"""
with offline(Offli... | 0 | 1 |
End of preview.
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.