id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
185,097 | import itertools
import logging
import os
import sys
from typing import Any, List, Optional, Union
import numpy as np
import torch
import torch.nn.functional as F
import librosa
from fairseq.data.audio.speech_to_text_dataset import get_features_or_waveform
from fairseq.data import data_utils
from fairseq.data.fairseq_d... | null |
185,098 | import itertools
import logging
import os
import sys
from typing import Any, List, Optional, Union
import numpy as np
import torch
import torch.nn.functional as F
import librosa
from fairseq.data.audio.speech_to_text_dataset import get_features_or_waveform
from fairseq.data import data_utils
from fairseq.data.fairseq_d... | null |
185,099 | import itertools
import logging
import os
import sys
from typing import Any, List, Optional, Union
import numpy as np
import torch
import torch.nn.functional as F
import librosa
from fairseq.data.audio.speech_to_text_dataset import get_features_or_waveform
from fairseq.data import data_utils
from fairseq.data.fairseq_d... | Compute log-Mel filterbank feature. (https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/parallel_wavegan/bin/preprocess.py) Args: audio (ndarray): Audio signal (T,). sampling_rate (int): Sampling rate. fft_size (int): FFT size. hop_size (int): Hop size. win_length (int): Window length. If set to None, it will b... |
185,100 | import math
import numpy as np
import torch
from fairseq.data import FairseqDataset, data_utils
def collate(
samples,
pad_idx,
eos_idx,
vocab,
left_pad_source=False,
left_pad_target=False,
input_feeding=True,
pad_to_length=None,
):
assert input_feeding
if len(samples) == 0:
... | null |
185,101 | import itertools
import logging
import os
from typing import Any, List, Optional
import numpy as np
import torch
import torch.nn.functional as F
import librosa
from fairseq.data.audio.speech_to_text_dataset import get_features_or_waveform
from fairseq.data import data_utils, Dictionary
from fairseq.data.fairseq_dataset... | Convert a list of 2D frames into a padded 3D tensor Args: frames (list): list of 2D frames of size L[i]*f_dim. Where L[i] is length of i-th frame and f_dim is static dimension of features Returns: 3D tensor of size len(frames)*len_max*f_dim where len_max is max of L[i] |
185,102 | import itertools
import logging
import os
from typing import Any, List, Optional
import numpy as np
import torch
import torch.nn.functional as F
import librosa
from fairseq.data.audio.speech_to_text_dataset import get_features_or_waveform
from fairseq.data import data_utils, Dictionary
from fairseq.data.fairseq_dataset... | null |
185,103 | import itertools
import logging
import os
from typing import Any, List, Optional
import numpy as np
import torch
import torch.nn.functional as F
import librosa
from fairseq.data.audio.speech_to_text_dataset import get_features_or_waveform
from fairseq.data import data_utils, Dictionary
from fairseq.data.fairseq_dataset... | null |
185,104 | import itertools
import logging
import os
from typing import Any, List, Optional
import numpy as np
import torch
import torch.nn.functional as F
import librosa
from fairseq.data.audio.speech_to_text_dataset import get_features_or_waveform
from fairseq.data import data_utils, Dictionary
from fairseq.data.fairseq_dataset... | null |
185,105 | import itertools
import logging
import os
from typing import Any, List, Optional
import numpy as np
import torch
import torch.nn.functional as F
import librosa
from fairseq.data.audio.speech_to_text_dataset import get_features_or_waveform
from fairseq.data import data_utils, Dictionary
from fairseq.data.fairseq_dataset... | Compute log-Mel filterbank feature. (https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/parallel_wavegan/bin/preprocess.py) Args: audio (ndarray): Audio signal (T,). sampling_rate (int): Sampling rate. fft_size (int): FFT size. hop_size (int): Hop size. win_length (int): Window length. If set to None, it will b... |
185,106 | import math
from argparse import Namespace
from dataclasses import dataclass, field
from omegaconf import II
from typing import Optional
import torch
import torch.nn.functional as F
from fairseq import metrics, utils
from fairseq.criterions import FairseqCriterion, register_criterion
from fairseq.dataclass import Fairs... | null |
185,107 | from typing import Dict, List
import numpy as np
import torch
import torch.nn as nn
import contextlib
from fairseq import utils
from fairseq.models import (
FairseqEncoder,
)
from fairseq.modules import (
FairseqDropout,
LayerNorm,
TransformerEncoderLayer,
)
from torch import Tensor
from .transformer_la... | null |
185,108 | from fairseq.models import (
register_model_architecture,
)
from fairseq.models.transformer_lm import base_lm_architecture
def base_lm_architecture(args):
def transformer_lm_t5(args):
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 1280)
args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_... | null |
185,109 | import logging
from ast import literal_eval
from typing import Dict, List, Optional, Tuple
import torch
import torch.nn.functional as F
from fairseq import utils
from fairseq.models import (
FairseqEncoderDecoderModel,
FairseqIncrementalDecoder,
register_model,
register_model_architecture,
)
from .modul... | null |
185,110 | import logging
from ast import literal_eval
from typing import Dict, List, Optional, Tuple
import torch
import torch.nn.functional as F
from fairseq import utils
from fairseq.models import (
FairseqEncoderDecoderModel,
FairseqIncrementalDecoder,
register_model,
register_model_architecture,
)
from .modul... | null |
185,111 | import logging
from ast import literal_eval
from typing import Dict, List, Optional, Tuple
import torch
import torch.nn.functional as F
from fairseq import utils
from fairseq.models import (
FairseqEncoderDecoderModel,
FairseqIncrementalDecoder,
register_model,
register_model_architecture,
)
from .modul... | null |
185,112 | import ast
import logging
import os
import os.path as op
import sys
from argparse import Namespace
import numpy as np
import torch
from fairseq import checkpoint_utils, options, tasks, utils
from fairseq.dataclass.utils import convert_namespace_to_omegaconf
from fairseq.logging import progress_bar
from omegaconf import... | null |
185,113 | import ast
import logging
import os
import os.path as op
import sys
from argparse import Namespace
import numpy as np
import torch
from fairseq import checkpoint_utils, options, tasks, utils
from fairseq.dataclass.utils import convert_namespace_to_omegaconf
from fairseq.logging import progress_bar
from omegaconf import... | null |
185,137 | import datetime
import io
import os
import math
import time
import json
import argparse
import numpy as np
from pathlib import Path
from collections import defaultdict, deque
from timm.utils import get_state_dict
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
from to... | Parse boolean arguments from the command line. |
185,138 | import datetime
import io
import os
import math
import time
import json
import argparse
import numpy as np
from pathlib import Path
from collections import defaultdict, deque
from timm.utils import get_state_dict
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
from to... | null |
185,139 | import datetime
import io
import os
import math
import time
import json
import argparse
import numpy as np
from pathlib import Path
from collections import defaultdict, deque
from timm.utils import get_state_dict
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
from to... | null |
185,140 | import datetime
import io
import os
import math
import time
import json
import argparse
import numpy as np
from pathlib import Path
from collections import defaultdict, deque
from timm.utils import get_state_dict
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
from to... | null |
185,141 | import datetime
import io
import os
import math
import time
import json
import argparse
import numpy as np
from pathlib import Path
from collections import defaultdict, deque
from timm.utils import get_state_dict
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
from to... | null |
185,142 | import datetime
import io
import os
import math
import time
import json
import argparse
import numpy as np
from pathlib import Path
from collections import defaultdict, deque
from timm.utils import get_state_dict
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
from to... | null |
185,143 | import datetime
import io
import os
import math
import time
import json
import argparse
import numpy as np
from pathlib import Path
from collections import defaultdict, deque
from timm.utils import get_state_dict
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
from to... | null |
185,144 | import datetime
import io
import os
import math
import time
import json
import argparse
import numpy as np
from pathlib import Path
from collections import defaultdict, deque
from timm.utils import get_state_dict
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
from to... | null |
185,145 | import datetime
import io
import os
import math
import time
import json
import argparse
import numpy as np
from pathlib import Path
from collections import defaultdict, deque
from timm.utils import get_state_dict
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
from to... | null |
185,146 | import datetime
import io
import os
import math
import time
import json
import argparse
import numpy as np
from pathlib import Path
from collections import defaultdict, deque
from timm.utils import get_state_dict
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
from to... | null |
185,147 | import datetime
import io
import os
import math
import time
import json
import argparse
import numpy as np
from pathlib import Path
from collections import defaultdict, deque
from timm.utils import get_state_dict
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
from to... | null |
185,148 | import datetime
import io
import os
import math
import time
import json
import argparse
import numpy as np
from pathlib import Path
from collections import defaultdict, deque
from timm.utils import get_state_dict
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
from to... | null |
185,149 | import datetime
import io
import os
import math
import time
import json
import argparse
import numpy as np
from pathlib import Path
from collections import defaultdict, deque
from timm.utils import get_state_dict
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
from to... | null |
185,150 | from torch import optim as optim
from timm.optim.lookahead import Lookahead
import json
def get_num_layer_for_vit(var_name, num_max_layer):
if "embed" in var_name:
return 0
elif var_name in (
"cls_token", "mask_token", "pos_embed", "language_pos_embed",
"word_embeddings.weight", "visio... | null |
185,153 | import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.registry import register_model
import numpy as np
import utils
from modeling_utils import BEiT3Wrapper, _get_base_config, _get_large_config
class BEiT3ForImageClassification(BEiT3Wrapper):
def __init__(
self,
... | null |
185,154 | import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.registry import register_model
import numpy as np
import utils
from modeling_utils import BEiT3Wrapper, _get_base_config, _get_large_config
class BEiT3ForImageClassification(BEiT3Wrapper):
def __init__(
self,
... | null |
185,155 | import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.registry import register_model
import numpy as np
import utils
from modeling_utils import BEiT3Wrapper, _get_base_config, _get_large_config
class BEiT3ForVisualReasoning(BEiT3Wrapper):
def __init__(
self,
... | null |
185,156 | import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.registry import register_model
import numpy as np
import utils
from modeling_utils import BEiT3Wrapper, _get_base_config, _get_large_config
class BEiT3ForVisualReasoning(BEiT3Wrapper):
def __init__(
self,
ar... | null |
185,157 | import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.registry import register_model
import numpy as np
import utils
from modeling_utils import BEiT3Wrapper, _get_base_config, _get_large_config
class BEiT3ForVisualQuestionAnswering(BEiT3Wrapper):
def __init__(
self,
... | null |
185,158 | import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.registry import register_model
import numpy as np
import utils
from modeling_utils import BEiT3Wrapper, _get_base_config, _get_large_config
class BEiT3ForVisualQuestionAnswering(BEiT3Wrapper):
def __init__(
self,
... | null |
185,159 | import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.registry import register_model
import numpy as np
import utils
from modeling_utils import BEiT3Wrapper, _get_base_config, _get_large_config
class BEiT3ForVisualQuestionAnswering(BEiT3Wrapper):
def __init__(
self,
... | null |
185,160 | import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.registry import register_model
import numpy as np
import utils
from modeling_utils import BEiT3Wrapper, _get_base_config, _get_large_config
class BEiT3ForVisualQuestionAnswering(BEiT3Wrapper):
def __init__(
self,
... | null |
185,161 | import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.registry import register_model
import numpy as np
import utils
from modeling_utils import BEiT3Wrapper, _get_base_config, _get_large_config
class BEiT3ForVisualQuestionAnswering(BEiT3Wrapper):
def __init__(
self,
... | null |
185,162 | import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.registry import register_model
import numpy as np
import utils
from modeling_utils import BEiT3Wrapper, _get_base_config, _get_large_config
class BEiT3ForCaptioning(BEiT3Wrapper):
def __init__(
self,
args,
... | null |
185,163 | import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.registry import register_model
import numpy as np
import utils
from modeling_utils import BEiT3Wrapper, _get_base_config, _get_large_config
class BEiT3ForCaptioning(BEiT3Wrapper):
def __init__(
self,
args,
... | null |
185,164 | import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.registry import register_model
import numpy as np
import utils
from modeling_utils import BEiT3Wrapper, _get_base_config, _get_large_config
class BEiT3ForCaptioning(BEiT3Wrapper):
def __init__(
self,
... | null |
185,165 | import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.registry import register_model
import numpy as np
import utils
from modeling_utils import BEiT3Wrapper, _get_base_config, _get_large_config
class BEiT3ForRetrieval(BEiT3Wrapper):
def __init__(
self,
args,
... | null |
185,166 | import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.registry import register_model
import numpy as np
import utils
from modeling_utils import BEiT3Wrapper, _get_base_config, _get_large_config
class BEiT3ForRetrieval(BEiT3Wrapper):
def __init__(
self,
args,
... | null |
185,167 | import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.registry import register_model
import numpy as np
import utils
from modeling_utils import BEiT3Wrapper, _get_base_config, _get_large_config
class BEiT3ForRetrieval(BEiT3Wrapper):
def __init__(
self,
args,
... | null |
185,168 | import re
contractions = {
"aint": "ain't",
"arent": "aren't",
"cant": "can't",
"couldve": "could've",
"couldnt": "couldn't",
"couldn'tve": "couldn't've",
"couldnt've": "couldn't've",
"didnt": "didn't",
"doesnt": "doesn't",
"dont": "don't",
"hadnt": "hadn't",
"hadnt've": ... | null |
185,169 | import os
import json
import random
import torch
import glob
from collections import defaultdict, Counter
from torchvision import transforms
from torchvision.datasets.folder import default_loader
from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD... | null |
185,170 | import os
import json
import random
import torch
import glob
from collections import defaultdict, Counter
from torchvision import transforms
from torchvision.datasets.folder import default_loader
from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD... | null |
185,171 | import os
import json
import random
import torch
import glob
from collections import defaultdict, Counter
from torchvision import transforms
from torchvision.datasets.folder import default_loader
from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD... | null |
185,172 | import os
import json
import random
import torch
import glob
from collections import defaultdict, Counter
from torchvision import transforms
from torchvision.datasets.folder import default_loader
from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD... | null |
185,173 | import math
import sys
import json
from typing import Iterable, Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.utils import ModelEma
from timm.utils import accuracy, ModelEma
from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy
from datasets import get_sentence... | null |
185,174 | import math
import sys
import json
from typing import Iterable, Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.utils import ModelEma
from timm.utils import accuracy, ModelEma
from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy
from datasets import get_sentence... | null |
185,175 | import math
import sys
import json
from typing import Iterable, Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.utils import ModelEma
from timm.utils import accuracy, ModelEma
from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy
from datasets import get_sentence... | null |
185,176 | import math
import torch
import torch.nn as nn
from timm.models.layers import trunc_normal_ as __call_trunc_normal_
from torchscale.model.BEiT3 import BEiT3
from torchscale.architecture.config import EncoderConfig
def trunc_normal_(tensor, mean=0., std=1.):
__call_trunc_normal_(tensor, mean=mean, std=std, a=-std, ... | null |
185,177 | import argparse
import datetime
import numpy as np
import time
import torch
import torch.backends.cudnn as cudnn
import json
import os
from pathlib import Path
from timm.data.mixup import Mixup
from timm.models import create_model
from timm.utils import ModelEma
from optim_factory import create_optimizer, get_parameter... | null |
185,178 | from __future__ import absolute_import, division, print_function
import argparse
import glob
import logging
import os
import random
import json
import numpy as np
import torch
from seqeval.metrics import f1_score, precision_score, recall_score
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
... | Train the model |
185,179 | from __future__ import absolute_import, division, print_function
import argparse
import glob
import logging
import os
import random
import json
import time
import numpy as np
import torch
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
TensorDataset)
from torch.... | Train the model |
185,180 | from __future__ import absolute_import, division, print_function
import argparse
import glob
import logging
import os
import random
import json
import numpy as np
import torch
from sklearn.metrics import matthews_corrcoef, f1_score
from sklearn.metrics import cohen_kappa_score, precision_score, recall_score, precision_... | Train the model |
185,181 | import logging
import os
from tqdm import *
def get_labels(path):
if path:
with open(path, "r") as f:
labels = f.read().splitlines()
if "O" not in labels:
labels = ["O"] + labels
return labels
else:
return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-OR... | null |
185,182 | import logging
import os
import csv
import sys
import copy
import json
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import matthews_corrcoef, f1_score
from sklearn.preprocessing import MultiLabelBinarizer
logger = logging.getLogger(__name__)
class InputFeatures(object):
"""
A single set of f... | Loads a data file into a list of ``InputFeatures`` Args: examples: List of ``InputExamples`` or ``tf.data.Dataset`` containing the examples. tokenizer: Instance of a tokenizer that will tokenize the examples max_length: Maximum example length task: GLUE task label_list: List of labels. Can be obtained from the processo... |
185,183 | import logging
import os
import csv
import sys
import copy
import json
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import matthews_corrcoef, f1_score
from sklearn.preprocessing import MultiLabelBinarizer
def simple_accuracy(preds, labels):
return (preds == labels).mean()
def acc_and_f1(preds, l... | null |
185,184 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import sys
import unicodedata
import six
import logging
from six.moves import range
import time
import glob
_ALPHANUMERIC_CHAR_SET = set(
six.unichr(i) for i in range(sys.maxunicode)
... | Decode a list of tokens to a unicode string. Args: tokens: a list of Unicode strings Returns: a unicode string |
185,185 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import sys
import unicodedata
import six
import logging
from six.moves import range
import time
import glob
_native_to_unicode = (lambda s: s.decode("utf-8")) if six.PY2 else (lambda s: s)
lo... | Read a vocab file and return a dictionary of token counts. Reads a two-column CSV file of tokens and their frequency in a dataset. The tokens are presumed to be generated by encode() or the equivalent. Args: text_filepattern: A pattern matching one or more files. max_lines: An integer; maximum total lines to read. Retu... |
185,186 | from __future__ import absolute_import
from __future__ import division
from numpy.core.fromnumeric import argsort
from text_encoder import SubwordTextEncoder
import tokenizer
import tempfile
import argparse
from transformers import BertTokenizer
import random
import math
import numpy as np
def merge_output_file_with_be... | @description : The function to get the incremental vocabulary for @param : @Returns : |
185,187 | from __future__ import absolute_import
from __future__ import division
from numpy.core.fromnumeric import argsort
from text_encoder import SubwordTextEncoder
import tokenizer
import tempfile
import argparse
from transformers import BertTokenizer
import random
import math
import numpy as np
def get_args():
parser =... | null |
185,188 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
from itertools import chain
import re
import time
import logging
import six
from six.moves import range
logger = logging.getLogger(__name__)
def is_unicode(s):
return isinstance(s, six.text... | null |
185,189 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
from itertools import chain
import re
import time
import logging
import six
from six.moves import range
if six.PY2:
RESERVED_TOKENS_BYTES = RESERVED_TOKENS
else:
RESERVED_TOKENS_BYTES = [... | null |
185,190 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
from itertools import chain
import re
import time
import logging
import six
from six.moves import range
if six.PY2:
RESERVED_TOKENS_BYTES = RESERVED_TOKENS
else:
RESERVED_TOKENS_BYTES = [... | Escape away underscores and OOV characters and append '_'. This allows the token to be expressed as the concatenation of a list of subtokens from the vocabulary. The underscore acts as a sentinel which allows us to invertibly concatenate multiple such lists. Args: token: A unicode string to be escaped. alphabet: A set ... |
185,191 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
from itertools import chain
import re
import time
import logging
import six
from six.moves import range
if six.PY2:
RESERVED_TOKENS_BYTES = RESERVED_TOKENS
else:
RESERVED_TOKENS_BYTES = [... | null |
185,192 | from __future__ import absolute_import
from __future__ import division
from text_encoder import SubwordTextEncoder
import tokenizer
import os
import tempfile
import tensorflow as tf
def merge_output_file_with_bert_vocab(output_filename, bert_vocab, temp_path):
writer = open(output_filename, 'w', encoding='utf-8')
... | null |
185,193 | import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
from datasets import ClassLabel, load_dataset, load_metric
import layoutlmft.data.datasets.funsd
import transformers
from layoutlmft.data import DataCollatorForKeyValueExtraction
from layoutlmft.d... | null |
185,194 | import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
from datasets import ClassLabel, load_dataset, load_metric
import layoutlmft.data.datasets.xfun
import transformers
from layoutlmft.data import DataCollatorForKeyValueExtraction
from layoutlmft.da... | null |
185,195 | import logging
import os
import sys
import numpy as np
from datasets import ClassLabel, load_dataset
import layoutlmft.data.datasets.xfun
import transformers
from layoutlmft import AutoModelForRelationExtraction
from layoutlmft.data.data_args import XFUNDataTrainingArguments
from layoutlmft.data.data_collator import Da... | null |
185,205 | from __future__ import print_function
from bleu.bleu import Bleu
from meteor.meteor import Meteor
from rouge.rouge import Rouge
from cider.cider import Cider
from collections import defaultdict
from argparse import ArgumentParser
import string
import sys
_tok_dict = {"(": "-lrb-", ")": "-rrb-",
"[": "-lsb-... | null |
185,206 | from __future__ import print_function
from bleu.bleu import Bleu
from meteor.meteor import Meteor
from rouge.rouge import Rouge
from cider.cider import Cider
from collections import defaultdict
from argparse import ArgumentParser
import string
import sys
def detokenize(tk_list):
r_list = []
for tk in tk_list:
... | Given a filename, calculate the metric scores for that prediction file isDin: boolean value to check whether input file is DirectIn.txt |
185,207 | from __future__ import print_function
from bleu.bleu import Bleu
from meteor.meteor import Meteor
from rouge.rouge import Rouge
from cider.cider import Cider
from collections import defaultdict
from argparse import ArgumentParser
import string
import sys
def fix_tokenization(text):
input_tokens = text.split()
o... | Given a filename, calculate the metric scores for that prediction file isDin: boolean value to check whether input file is DirectIn.txt |
185,208 | import torch
from torch.nn import DataParallel
from torch.cuda._utils import _get_device_index
from torch.nn.parallel._functions import Scatter
from itertools import chain
def scatter_imbalance(inputs, target_gpus, dim=0):
r"""
Slices tensors into approximately equal chunks and
distributes them across given... | r"""Scatter with support for kwargs dictionary |
185,209 | import os
import logging
import shutil
import tempfile
import json
from urllib.parse import urlparse
from pathlib import Path
from typing import Optional, Tuple, Union, IO, Callable, Set
from hashlib import sha256
from functools import wraps
from tqdm import tqdm
import boto3
from botocore.exceptions import ClientError... | Return the url and etag (which may be ``None``) stored for `filename`. Raise ``FileNotFoundError`` if `filename` or its stored metadata do not exist. |
185,210 | import os
import logging
import shutil
import tempfile
import json
from urllib.parse import urlparse
from pathlib import Path
from typing import Optional, Tuple, Union, IO, Callable, Set
from hashlib import sha256
from functools import wraps
from tqdm import tqdm
import boto3
from botocore.exceptions import ClientError... | Given something that might be a URL (or might be a local path), determine which. If it's a URL, download the file and cache it, and return the path to the cached file. If it's already a local path, make sure the file exists and then return the path. |
185,214 | import math
import torch
from torch.optim import Optimizer
from torch.optim.optimizer import required
from torch.nn.utils import clip_grad_norm_
from collections import defaultdict
from torch._six import container_abcs
from copy import deepcopy
from itertools import chain
def warmup_cosine(x, warmup=0.002):
if x <... | null |
185,215 | import math
import torch
from torch.optim import Optimizer
from torch.optim.optimizer import required
from torch.nn.utils import clip_grad_norm_
from collections import defaultdict
from torch._six import container_abcs
from copy import deepcopy
from itertools import chain
def warmup_constant(x, warmup=0.002):
if x... | null |
185,216 | import math
import torch
from torch.optim import Optimizer
from torch.optim.optimizer import required
from torch.nn.utils import clip_grad_norm_
from collections import defaultdict
from torch._six import container_abcs
from copy import deepcopy
from itertools import chain
def warmup_linear(x, warmup=0.002):
if x <... | null |
185,217 | import math
import torch
from torch.optim import Optimizer
from torch.optim.optimizer import required
from torch.nn.utils import clip_grad_norm_
from collections import defaultdict
from torch._six import container_abcs
from copy import deepcopy
from itertools import chain
def find_state_dict_subset_finetune(org_state_... | null |
185,218 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import unicodedata
import os
import logging
from .file_utils import cached_path
The provided code snippet includes necessary dependencies for implementing the `load_vocab` function. Write a P... | Loads a vocabulary file into a dictionary. |
185,223 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import copy
import json
import math
import logging
import tarfile
import tempfile
import shutil
import numpy as np
from scipy.stats import truncnorm
import torch
from torch import nn
from torch.nn impo... | Implementation of the gelu activation function. For information: OpenAI GPT's gelu is slightly different (and gives slightly different results): 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3)))) |
185,224 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import copy
import json
import math
import logging
import tarfile
import tempfile
import shutil
import numpy as np
from scipy.stats import truncnorm
import torch
from torch import nn
from torch.nn impo... | null |
185,225 | from random import randint, shuffle, choice
from random import random as rand
import math
import torch
from biunilm.loader_utils import get_random_word, batch_list_to_batch_tensors, Pipeline
def truncate_tokens_pair(tokens_a, tokens_b, max_len, max_len_a=0, max_len_b=0, trunc_seg=None, always_truncate_tail=False):
... | null |
185,226 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import logging
import glob
import argparse
import math
from tqdm import tqdm, trange
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler
from torch.utils.data.distrib... | null |
185,227 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import logging
import glob
import argparse
import math
from tqdm import tqdm, trange
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler
from torch.utils.data.distrib... | null |
185,228 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import logging
import glob
import math
import json
import argparse
import random
from pathlib import Path
from tqdm import tqdm, trange
import numpy as np
import torch
from torch.utils.data import Rand... | null |
185,229 | from random import randint, shuffle
from random import random as rand
import numpy as np
import torch
import torch.utils.data
def get_random_word(vocab_words):
i = randint(0, len(vocab_words)-1)
return vocab_words[i] | null |
185,230 | from random import randint, shuffle
from random import random as rand
import numpy as np
import torch
import torch.utils.data
def batch_list_to_batch_tensors(batch):
batch_tensors = []
for x in zip(*batch):
if x[0] is None:
batch_tensors.append(None)
elif isinstance(x[0], torch.Tens... | null |
185,231 | from random import randint, shuffle
from random import random as rand
import numpy as np
import torch
import torch.utils.data
def _get_word_split_index(tokens, st, end):
split_idx = []
i = st
while i < end:
if (not tokens[i].startswith('##')) or (i == st):
split_idx.append(i)
i ... | null |
185,232 | from random import randint, shuffle
from random import random as rand
import numpy as np
import torch
import torch.utils.data
def _expand_whole_word(tokens, st, end):
new_st, new_end = st, end
while (new_st >= 0) and tokens[new_st].startswith('##'):
new_st -= 1
while (new_end < len(tokens)) and tok... | null |
185,233 | import pickle
import math
import argparse
import glob
from pathlib import Path
from tqdm import tqdm
import unicodedata
from pytorch_pretrained_bert.tokenization import BertTokenizer
def read_traces_from_file(file_name):
with open(file_name, "rb") as fin:
meta = pickle.load(fin)
num_samples = meta[... | null |
185,234 | import pickle
import math
import argparse
import glob
from pathlib import Path
from tqdm import tqdm
import unicodedata
from pytorch_pretrained_bert.tokenization import BertTokenizer
def get_best_sequence(sample, eos_id, pad_id, length_penalty=None, alpha=None, expect=None, min_len=None):
# if not any((length_pena... | null |
185,235 | import pickle
import math
import argparse
import glob
from pathlib import Path
from tqdm import tqdm
import unicodedata
from pytorch_pretrained_bert.tokenization import BertTokenizer
def detokenize(tk_list):
r_list = []
for tk in tk_list:
if tk.startswith('##') and len(r_list) > 0:
r_list[-... | null |
185,236 | import pickle
import math
import argparse
import glob
from pathlib import Path
from tqdm import tqdm
import unicodedata
from pytorch_pretrained_bert.tokenization import BertTokenizer
def simple_postprocess(tk_list):
# truncate duplicate punctuations
while tk_list and len(tk_list) > 4 and len(tk_list[-1]) == 1 ... | null |
185,238 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import logging
import glob
import json
import argparse
import math
import string
from multiprocessing import Pool, cpu_count
from tqdm import tqdm, trange
from pathlib import Path
import numpy as np
im... | null |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.