id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
19,368 | import os, json
import random as rd
from copy import deepcopy
import difflib
import re
import torch
import torchvision.datasets as dsets
import torch.nn as nn
import torch.nn.functional as F
from matplotlib.pylab import *
from torch.autograd import Variable
from .utils import generate_perm_inv
from .utils import json_d... | s2s version. Treat SQL-tokens as pseudo-headers sql_vocab = ("sql select", "sql where", "sql and", "sql equal", "sql greater than", "sql less than") e.g.) Q: What is the name of the player with score greater than 15? H: Name of the player, score Input: [CLS], what, is, ..., [SEP], name, of, the, player, [SEP], score, [... |
19,369 | import os, json
import random as rd
from copy import deepcopy
import difflib
import re
import torch
import torchvision.datasets as dsets
import torch.nn as nn
import torch.nn.functional as F
from matplotlib.pylab import *
from torch.autograd import Variable
from .utils import generate_perm_inv
from .utils import json_d... | null |
19,370 | import os, json
import random as rd
from copy import deepcopy
import difflib
import re
import torch
import torchvision.datasets as dsets
import torch.nn as nn
import torch.nn.functional as F
from matplotlib.pylab import *
from torch.autograd import Variable
from .utils import generate_perm_inv
from .utils import json_d... | Generate one-hot idx indicating vectors with their lenghts. :param g_wvi: e.g. [[[0, 6, 7, 8, 15], [0, 1, 2, 3, 4, 15]], [[0, 1, 2, 3, 16], [0, 7, 8, 9, 16]]] where_val idx in nlu_t. 0 = <BEG>, -1 = <END>. :param mL_w: 4 :param mL_nt: 200 :return: |
19,371 | import os, json
import random as rd
from copy import deepcopy
import difflib
import re
import torch
import torchvision.datasets as dsets
import torch.nn as nn
import torch.nn.functional as F
from matplotlib.pylab import *
from torch.autograd import Variable
from .utils import generate_perm_inv
from .utils import json_d... | return: [ pr_wc1_i, pr_wc2_i, ...] |
19,372 | import os, json
import random as rd
from copy import deepcopy
import difflib
import re
import torch
import torchvision.datasets as dsets
import torch.nn as nn
import torch.nn.functional as F
from matplotlib.pylab import *
from torch.autograd import Variable
from .utils import generate_perm_inv
from .utils import json_d... | return: [ pr_wc1_i, pr_wc2_i, ...] |
19,373 | import os, json
import random as rd
from copy import deepcopy
import difflib
import re
import torch
import torchvision.datasets as dsets
import torch.nn as nn
import torch.nn.functional as F
from matplotlib.pylab import *
from torch.autograd import Variable
from .utils import generate_perm_inv
from .utils import json_d... | return: [ pr_wc1_i, pr_wc2_i, ...] |
19,374 | import os, json
import random as rd
from copy import deepcopy
import difflib
import re
import torch
import torchvision.datasets as dsets
import torch.nn as nn
import torch.nn.functional as F
from matplotlib.pylab import *
from torch.autograd import Variable
from .utils import generate_perm_inv
from .utils import json_d... | return: [ pr_wc1_i, pr_wc2_i, ...] |
19,375 | import os, json
import random as rd
from copy import deepcopy
import difflib
import re
import torch
import torchvision.datasets as dsets
import torch.nn as nn
import torch.nn.functional as F
from matplotlib.pylab import *
from torch.autograd import Variable
from .utils import generate_perm_inv
from .utils import json_d... | return: [ pr_wc1_i, pr_wc2_i, ...] |
19,376 | import os, json
import random as rd
from copy import deepcopy
import difflib
import re
import torch
import torchvision.datasets as dsets
import torch.nn as nn
import torch.nn.functional as F
from matplotlib.pylab import *
from torch.autograd import Variable
from .utils import generate_perm_inv
from .utils import json_d... | return: [ pr_wc1_i, pr_wc2_i, ...] |
19,377 | import os, json
import random as rd
from copy import deepcopy
import difflib
import re
import torch
import torchvision.datasets as dsets
import torch.nn as nn
import torch.nn.functional as F
from matplotlib.pylab import *
from torch.autograd import Variable
from .utils import generate_perm_inv
from .utils import json_d... | return: [ pr_wc1_i, pr_wc2_i, ...] |
19,378 | import os, json
import random as rd
from copy import deepcopy
import difflib
import re
import torch
import torchvision.datasets as dsets
import torch.nn as nn
import torch.nn.functional as F
from matplotlib.pylab import *
from torch.autograd import Variable
from .utils import generate_perm_inv
from .utils import json_d... | return: [ pr_wc1_i, pr_wc2_i, ...] ! Returned index is sorted! |
19,379 | import os, json
import random as rd
from copy import deepcopy
import difflib
import re
import torch
import torchvision.datasets as dsets
import torch.nn as nn
import torch.nn.functional as F
from matplotlib.pylab import *
from torch.autograd import Variable
from .utils import generate_perm_inv
from .utils import json_d... | return: [ pr_wc1_i, pr_wc2_i, ...] ! Returned index is sorted! |
19,380 | import os, json
import random as rd
from copy import deepcopy
import difflib
import re
import torch
import torchvision.datasets as dsets
import torch.nn as nn
import torch.nn.functional as F
from matplotlib.pylab import *
from torch.autograd import Variable
from .utils import generate_perm_inv
from .utils import json_d... | return: [ pr_wc1_i, pr_wc2_i, ...] ! Returned index is sorted! |
19,381 | import os, json
import random as rd
from copy import deepcopy
import difflib
import re
import torch
import torchvision.datasets as dsets
import torch.nn as nn
import torch.nn.functional as F
from matplotlib.pylab import *
from torch.autograd import Variable
from .utils import generate_perm_inv
from .utils import json_d... | return: [ pr_wc1_i, pr_wc2_i, ...] ! Returned index is sorted by prob. All colume-indexes are returned here. |
19,382 | import os, json
import random as rd
from copy import deepcopy
import difflib
import re
import torch
import torchvision.datasets as dsets
import torch.nn as nn
import torch.nn.functional as F
from matplotlib.pylab import *
from torch.autograd import Variable
from .utils import generate_perm_inv
from .utils import json_d... | return: [ pr_wc1_i, pr_wc2_i, ...] |
19,383 | import os, json
import random as rd
from copy import deepcopy
import difflib
import re
import torch
import torchvision.datasets as dsets
import torch.nn as nn
import torch.nn.functional as F
from matplotlib.pylab import *
from torch.autograd import Variable
from .utils import generate_perm_inv
from .utils import json_d... | s_wv: [B, 4, mL, 2] - predict best st-idx & ed-idx |
19,384 | import os, json
import random as rd
from copy import deepcopy
import difflib
import re
import torch
import torchvision.datasets as dsets
import torch.nn as nn
import torch.nn.functional as F
from matplotlib.pylab import *
from torch.autograd import Variable
from .utils import generate_perm_inv
from .utils import json_d... | s_wv: [B, 4, mL, 2] - predict best st-idx & ed-idx output: pr_wvi_beam = [B, max_wn, n_pairs, 2]. 2 means [st, ed]. prob_wvi_beam = [B, max_wn, n_pairs] |
19,385 | import os, json
import random as rd
from copy import deepcopy
import difflib
import re
import torch
import torchvision.datasets as dsets
import torch.nn as nn
import torch.nn.functional as F
from matplotlib.pylab import *
from torch.autograd import Variable
from .utils import generate_perm_inv
from .utils import json_d... | null |
19,386 | import os, json
import random as rd
from copy import deepcopy
import difflib
import re
import torch
import torchvision.datasets as dsets
import torch.nn as nn
import torch.nn.functional as F
from matplotlib.pylab import *
from torch.autograd import Variable
from .utils import generate_perm_inv
from .utils import json_d... | - Convert to the string in whilte-space-separated tokens - Add-hoc addition. |
19,387 | import os, json
import random as rd
from copy import deepcopy
import difflib
import re
import torch
import torchvision.datasets as dsets
import torch.nn as nn
import torch.nn.functional as F
from matplotlib.pylab import *
from torch.autograd import Variable
from .utils import generate_perm_inv
from .utils import json_d... | null |
19,388 | import os, json
import random as rd
from copy import deepcopy
import difflib
import re
import torch
import torchvision.datasets as dsets
import torch.nn as nn
import torch.nn.functional as F
from matplotlib.pylab import *
from torch.autograd import Variable
from .utils import generate_perm_inv
from .utils import json_d... | Generate SQuAD style start and end index of wv in nlu. Index is for of after WordPiece tokenization. Assumption: where_str always presents in the nlu. |
19,389 | import os, json
import random as rd
from copy import deepcopy
import difflib
import re
import torch
import torchvision.datasets as dsets
import torch.nn as nn
import torch.nn.functional as F
from matplotlib.pylab import *
from torch.autograd import Variable
from .utils import generate_perm_inv
from .utils import json_d... | null |
19,390 | import os, json
import random as rd
from copy import deepcopy
import difflib
import re
import torch
import torchvision.datasets as dsets
import torch.nn as nn
import torch.nn.functional as F
from matplotlib.pylab import *
from torch.autograd import Variable
from .utils import generate_perm_inv
from .utils import json_d... | Generate SQuAD style start and end index of wv in nlu. Index is for of after WordPiece tokenization. Assumption: where_str always presents in the nlu. |
19,391 | import os, json
import random as rd
from copy import deepcopy
import difflib
import re
import torch
import torchvision.datasets as dsets
import torch.nn as nn
import torch.nn.functional as F
from matplotlib.pylab import *
from torch.autograd import Variable
from .utils import generate_perm_inv
from .utils import json_d... | Generate SQuAD style start and end index of wv in nlu. Index is for of after WordPiece tokenization. Assumption: where_str always presents in the nlu. |
19,392 | import os, json
import random as rd
from copy import deepcopy
import difflib
import re
import torch
import torchvision.datasets as dsets
import torch.nn as nn
import torch.nn.functional as F
from matplotlib.pylab import *
from torch.autograd import Variable
from .utils import generate_perm_inv
from .utils import json_d... | usalbe only when g_wc was used to find pr_wv |
19,393 | import os, json
import random as rd
from copy import deepcopy
import difflib
import re
import torch
import torchvision.datasets as dsets
import torch.nn as nn
import torch.nn.functional as F
from matplotlib.pylab import *
from torch.autograd import Variable
from .utils import generate_perm_inv
from .utils import json_d... | usalbe only when g_wc was used to find pr_wv |
19,394 | import os, json
import random as rd
from copy import deepcopy
import difflib
import re
import torch
import torchvision.datasets as dsets
import torch.nn as nn
import torch.nn.functional as F
from matplotlib.pylab import *
from torch.autograd import Variable
from .utils import generate_perm_inv
from .utils import json_d... | null |
19,395 | import os, json
import random as rd
from copy import deepcopy
import difflib
import re
import torch
import torchvision.datasets as dsets
import torch.nn as nn
import torch.nn.functional as F
from matplotlib.pylab import *
from torch.autograd import Variable
from .utils import generate_perm_inv
from .utils import json_d... | null |
19,396 | import os, json
import random as rd
from copy import deepcopy
import difflib
import re
import torch
import torchvision.datasets as dsets
import torch.nn as nn
import torch.nn.functional as F
from matplotlib.pylab import *
from torch.autograd import Variable
from .utils import generate_perm_inv
from .utils import json_d... | Get list of mean, std of grad of each parameters Code based on web searched result.. |
19,397 | import os, json
import random as rd
from copy import deepcopy
import difflib
import re
import torch
import torchvision.datasets as dsets
import torch.nn as nn
import torch.nn.functional as F
from matplotlib.pylab import *
from torch.autograd import Variable
from .utils import generate_perm_inv
from .utils import json_d... | null |
19,398 | import os, json
import random as rd
from copy import deepcopy
import difflib
import re
import torch
import torchvision.datasets as dsets
import torch.nn as nn
import torch.nn.functional as F
from matplotlib.pylab import *
from torch.autograd import Variable
from .utils import generate_perm_inv
from .utils import json_d... | null |
19,399 | import os, json
import random as rd
from copy import deepcopy
import difflib
import re
import torch
import torchvision.datasets as dsets
import torch.nn as nn
import torch.nn.functional as F
from matplotlib.pylab import *
from torch.autograd import Variable
from .utils import generate_perm_inv
from .utils import json_d... | null |
19,400 | import os, json
import random as rd
from copy import deepcopy
import difflib
import re
import torch
import torchvision.datasets as dsets
import torch.nn as nn
import torch.nn.functional as F
from matplotlib.pylab import *
from torch.autograd import Variable
from .utils import generate_perm_inv
from .utils import json_d... | null |
19,401 | import os, json
import random as rd
from copy import deepcopy
import difflib
import re
import torch
import torchvision.datasets as dsets
import torch.nn as nn
import torch.nn.functional as F
from matplotlib.pylab import *
from torch.autograd import Variable
from .utils import generate_perm_inv
from .utils import json_d... | null |
19,402 | import os, json
import random as rd
from copy import deepcopy
import difflib
import re
import torch
import torchvision.datasets as dsets
import torch.nn as nn
import torch.nn.functional as F
from matplotlib.pylab import *
from torch.autograd import Variable
from .utils import generate_perm_inv
from .utils import json_d... | null |
19,403 | import os, json
import random as rd
from copy import deepcopy
import difflib
import re
import torch
import torchvision.datasets as dsets
import torch.nn as nn
import torch.nn.functional as F
from matplotlib.pylab import *
from torch.autograd import Variable
from .utils import generate_perm_inv
from .utils import json_d... | null |
19,404 | import os, json
import random as rd
from copy import deepcopy
import difflib
import re
import torch
import torchvision.datasets as dsets
import torch.nn as nn
import torch.nn.functional as F
from matplotlib.pylab import *
from torch.autograd import Variable
from .utils import generate_perm_inv
from .utils import json_d... | Check whether pr_sc, pr_sa are allowed pairs or not. agg_ops = ['', 'MAX', 'MIN', 'COUNT', 'SUM', 'AVG'] |
19,405 | import os, json
import random as rd
from copy import deepcopy
import difflib
import re
import torch
import torchvision.datasets as dsets
import torch.nn as nn
import torch.nn.functional as F
from matplotlib.pylab import *
from torch.autograd import Variable
from .utils import generate_perm_inv
from .utils import json_d... | null |
19,406 | import os, json
import random as rd
from copy import deepcopy
import difflib
import re
import torch
import torchvision.datasets as dsets
import torch.nn as nn
import torch.nn.functional as F
from matplotlib.pylab import *
from torch.autograd import Variable
from .utils import generate_perm_inv
from .utils import json_d... | null |
19,407 | import os, json
import random as rd
from copy import deepcopy
import difflib
import re
import torch
import torchvision.datasets as dsets
import torch.nn as nn
import torch.nn.functional as F
from matplotlib.pylab import *
from torch.autograd import Variable
from .utils import generate_perm_inv
from .utils import json_d... | null |
19,408 | import os, json
import random as rd
from copy import deepcopy
import difflib
import re
import torch
import torchvision.datasets as dsets
import torch.nn as nn
import torch.nn.functional as F
from matplotlib.pylab import *
from torch.autograd import Variable
from .utils import generate_perm_inv
from .utils import json_d... | sql_vocab = ( 0.. "sql none", "sql max", "sql min", "sql count", "sql sum", "sql average", ..5 6.. "sql select", "sql where", "sql and", .. 8 9.. "sql equal", "sql greater than", "sql less than", .. 11 12.. "sql start", "sql end" .. 13 ) |
19,409 | import os, json
import random as rd
from copy import deepcopy
import difflib
import re
import torch
import torchvision.datasets as dsets
import torch.nn as nn
import torch.nn.functional as F
from matplotlib.pylab import *
from torch.autograd import Variable
from .utils import generate_perm_inv
from .utils import json_d... | null |
19,410 | import os, json
import random as rd
from copy import deepcopy
import difflib
import re
import torch
import torchvision.datasets as dsets
import torch.nn as nn
import torch.nn.functional as F
from matplotlib.pylab import *
from torch.autograd import Variable
from .utils import generate_perm_inv
from .utils import json_d... | null |
19,411 | import os, json
import random as rd
from copy import deepcopy
import difflib
import re
import torch
import torchvision.datasets as dsets
import torch.nn as nn
import torch.nn.functional as F
from matplotlib.pylab import *
from torch.autograd import Variable
from .utils import generate_perm_inv
from .utils import json_d... | null |
19,412 | import os, json
import random as rd
from copy import deepcopy
import difflib
import re
import torch
import torchvision.datasets as dsets
import torch.nn as nn
import torch.nn.functional as F
from matplotlib.pylab import *
from torch.autograd import Variable
from .utils import generate_perm_inv
from .utils import json_d... | ( "none", "max", "min", "count", "sum", "average", "select", "where", "and", "equal", "greater than", "less than", "start", "end" ), |
19,413 | import os, json
import random as rd
from copy import deepcopy
import difflib
import re
import torch
import torchvision.datasets as dsets
import torch.nn as nn
import torch.nn.functional as F
from matplotlib.pylab import *
from torch.autograd import Variable
from .utils import generate_perm_inv
from .utils import json_d... | null |
19,414 | import os, json
import random as rd
from copy import deepcopy
import difflib
import re
import torch
import torchvision.datasets as dsets
import torch.nn as nn
import torch.nn.functional as F
from matplotlib.pylab import *
from torch.autograd import Variable
from .utils import generate_perm_inv
from .utils import json_d... | As if [ [table-1-col-1-tok1, t1-c1-t2, ...], [t1-c2-t1, t1-c2-t2, ...]. ... [t2-c1-t1, ...,] ] # i_hds = [ [ Batch 1 ] [ Batch 2 ] ] # [Batch 1] = [ (col1_st_idx, col1_ed_idx), (col2_st_idx, col2_ed_idx), ...] # i_hds = [[(11, 14), (15, 19), (20, 21), (22, 24), (25, 27), (28, 29)], # [(16, 19), (20, 24), (25, 26), (27,... |
19,415 | import os, json
import random as rd
from copy import deepcopy
import difflib
import re
import torch
import torchvision.datasets as dsets
import torch.nn as nn
import torch.nn.functional as F
from matplotlib.pylab import *
from torch.autograd import Variable
from .utils import generate_perm_inv
from .utils import json_d... | :param s_sc: [B, l_h] :param s_sa: [B, l_a] # 16 :param s_wn: [B, 5] :param s_wc: [B, l_h] :param s_wo: [B, 4, l_o] # :param s_wv: [B, 4, 22] :return: |
19,416 | import os, json
import random as rd
from copy import deepcopy
import difflib
import re
import torch
import torchvision.datasets as dsets
import torch.nn as nn
import torch.nn.functional as F
from matplotlib.pylab import *
from torch.autograd import Variable
from .utils import generate_perm_inv
from .utils import json_d... | Input: list pr_wc = [B, n_conds] g_wc = [B, n_conds] Return: list pr_wc_sorted = [B, n_conds] |
19,417 | import os, json
import random as rd
from copy import deepcopy
import difflib
import re
import torch
import torchvision.datasets as dsets
import torch.nn as nn
import torch.nn.functional as F
from matplotlib.pylab import *
from torch.autograd import Variable
from .utils import generate_perm_inv
from .utils import json_d... | null |
19,418 | import os
import sys
import time
import math
import torch
import torch.nn as nn
import torch.distributed as dist
import torch.multiprocessing as mp
from torch.nn.parallel import DistributedDataParallel
from uer.model_loader import load_model
from uer.model_saver import save_model
from uer.model_builder import build_mod... | null |
19,419 | import os
import sys
import time
import math
import torch
import torch.nn as nn
import torch.distributed as dist
import torch.multiprocessing as mp
from torch.nn.parallel import DistributedDataParallel
from uer.model_loader import load_model
from uer.model_saver import save_model
from uer.model_builder import build_mod... | null |
19,420 | import os
import sys
import time
import math
import torch
import torch.nn as nn
import torch.distributed as dist
import torch.multiprocessing as mp
from torch.nn.parallel import DistributedDataParallel
from uer.model_loader import load_model
from uer.model_saver import save_model
from uer.model_builder import build_mod... | null |
19,421 | import os
import sys
import time
import math
import torch
import torch.nn as nn
import torch.distributed as dist
import torch.multiprocessing as mp
from torch.nn.parallel import DistributedDataParallel
from uer.model_loader import load_model
from uer.model_saver import save_model
from uer.model_builder import build_mod... | null |
19,422 | import os
import sys
import time
import math
import torch
import torch.nn as nn
import torch.distributed as dist
import torch.multiprocessing as mp
from torch.nn.parallel import DistributedDataParallel
from uer.model_loader import load_model
from uer.model_saver import save_model
from uer.model_builder import build_mod... | null |
19,423 | import os
import sys
import time
import math
import torch
import torch.nn as nn
import torch.distributed as dist
import torch.multiprocessing as mp
from torch.nn.parallel import DistributedDataParallel
from uer.model_loader import load_model
from uer.model_saver import save_model
from uer.model_builder import build_mod... | null |
19,424 | from __future__ import absolute_import, division, print_function, unicode_literals
from uer.utils.constants import *
from uer.utils.vocab import Vocab
import collections
import unicodedata
The provided code snippet includes necessary dependencies for implementing the `whitespace_tokenize` function. Write a Python func... | Runs basic whitespace cleaning and splitting on a piece of text. |
19,425 | from __future__ import absolute_import, division, print_function, unicode_literals
from uer.utils.constants import *
from uer.utils.vocab import Vocab
import collections
import unicodedata
The provided code snippet includes necessary dependencies for implementing the `_is_whitespace` function. Write a Python function ... | Checks whether `chars` is a whitespace character. |
19,426 | from __future__ import absolute_import, division, print_function, unicode_literals
from uer.utils.constants import *
from uer.utils.vocab import Vocab
import collections
import unicodedata
The provided code snippet includes necessary dependencies for implementing the `_is_control` function. Write a Python function `de... | Checks whether `chars` is a control character. |
19,427 | from __future__ import absolute_import, division, print_function, unicode_literals
from uer.utils.constants import *
from uer.utils.vocab import Vocab
import collections
import unicodedata
The provided code snippet includes necessary dependencies for implementing the `_is_punctuation` function. Write a Python function... | Checks whether `chars` is a punctuation character. |
19,428 | import os
import torch
import codecs
import random
import pickle
from multiprocessing import Pool
from uer.utils.constants import *
from uer.utils.misc import count_lines
from uer.utils.seed import set_seed
The provided code snippet includes necessary dependencies for implementing the `mask_seq` function. Write a Pyth... | mask input sequence for MLM task args: src: a list of tokens vocab_size: the vocabulary size |
19,429 | import os
import torch
import codecs
import random
import pickle
from multiprocessing import Pool
from uer.utils.constants import *
from uer.utils.misc import count_lines
from uer.utils.seed import set_seed
def merge_dataset(dataset_path, workers_num):
# Merge datasets.
f_writer = open(dataset_path, "w... | null |
19,430 | import json
def load_hyperparam(args):
with open(args.config_path, mode="r", encoding="utf-8") as f:
param = json.load(f)
args.emb_size = param.get("emb_size", 768)
args.hidden_size = param.get("hidden_size", 768)
args.kernel_size = param.get("kernel_size", 3)
args.block_size = param.get("b... | null |
19,431 | import torch
import torch.nn as nn
from uer.utils.constants import *
The provided code snippet includes necessary dependencies for implementing the `word2sub` function. Write a Python function `def word2sub(word_ids, vocab, sub_vocab, subword_type)` to solve the following problem:
word_ids: batch_size, seq_length
Her... | word_ids: batch_size, seq_length |
19,432 | import torch
import torch.nn as nn
def count_lines(file_path):
lines_num = 0
with open(file_path, 'rb') as f:
while True:
data = f.read(2^20)
if not data:
break
lines_num += data.count(b'\n')
return lines_num | null |
19,433 | import torch
import torch.nn as nn
def flip(x, dim):
indices = [slice(None)] * x.dim()
indices[dim] = torch.arange(x.size(dim) - 1, -1, -1,
dtype=torch.long, device=x.device)
return x[tuple(indices)] | null |
19,434 | import math
import torch
def gelu(x):
return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0))) | null |
19,435 | import numpy as np
import os
import pandas as pd
import pickle
import copy
from collections import Counter
def com_sels(gold_sql, pr_sql, tablei, table_words):
# print('tablei:', tablei)
gold_sels = gold_sql['sel'].tolist()
gold_aggs = gold_sql['agg'].tolist()
pre_sels = pr_sql['sel']
pre_aggs = pr... | null |
19,436 | import numpy as np
import os
import pandas as pd
import pickle
import copy
from collections import Counter
def col_val_syn(table, table_words, col_index, val):
# table = self.tables[tableId]
val = str(val)
headers = table['header']
cond_col = headers[col_index]
cond_value_synonmys = []
cond_valu... | null |
19,437 | import numpy as np
import os
import pandas as pd
import pickle
import copy
from collections import Counter
def com_sels_final(gold_sql, pr_sql, tablei, table_words):
# print('tablei:', tablei)
gold_sels = gold_sql['sel'].tolist()
gold_aggs = gold_sql['agg'].tolist()
pre_sels = pr_sql['sel']
pre_ag... | null |
19,438 | import numpy as np
import os
import pandas as pd
import pickle
import copy
from collections import Counter
def com_sels_with_split_final(gold_sql_sc, gold_sql_sa, pr_sql, tablei, table_words):
# print('tablei:', tablei)
gold_sels = gold_sql_sc
gold_aggs = gold_sql_sa
pre_sels = pr_sql['sel']
pre_a... | null |
19,439 | import torch.nn.functional as F
import torch.optim.lr_scheduler
import numpy as np
from uer.models.model import Model
from uer.model_builder import build_model
from uer.layers.layer_norm import LayerNorm
from uer.utils.act_fun import gelu
import torch.nn as nn
from torch.autograd import Variable
from matplotlib.pylab i... | adopted from Timothy Dozat https://github.com/tdozat/Parser/blob/master/lib/linalg.py |
19,440 | import torch.nn.functional as F
import torch.optim.lr_scheduler
import numpy as np
from uer.models.model import Model
from uer.model_builder import build_model
from uer.layers.layer_norm import LayerNorm
from uer.utils.act_fun import gelu
import torch.nn as nn
from torch.autograd import Variable
from matplotlib.pylab i... | null |
19,441 | import torch.nn.functional as F
import torch.optim.lr_scheduler
import numpy as np
from uer.models.model import Model
from uer.model_builder import build_model
from uer.layers.layer_norm import LayerNorm
from uer.utils.act_fun import gelu
import torch.nn as nn
from torch.autograd import Variable
from matplotlib.pylab i... | null |
19,442 | import torch.nn.functional as F
import torch.optim.lr_scheduler
import numpy as np
from uer.models.model import Model
from uer.model_builder import build_model
from uer.layers.layer_norm import LayerNorm
from uer.utils.act_fun import gelu
import torch.nn as nn
from torch.autograd import Variable
from matplotlib.pylab i... | [batch_size, max token length, dim_emb] |
19,443 | import torch.nn.functional as F
import torch.optim.lr_scheduler
import numpy as np
from uer.models.model import Model
from uer.model_builder import build_model
from uer.layers.layer_norm import LayerNorm
from uer.utils.act_fun import gelu
import torch.nn as nn
from torch.autograd import Variable
from matplotlib.pylab i... | null |
19,444 | import torch.nn.functional as F
import torch.optim.lr_scheduler
import numpy as np
from uer.models.model import Model
from uer.model_builder import build_model
from uer.layers.layer_norm import LayerNorm
from uer.utils.act_fun import gelu
import torch.nn as nn
from torch.autograd import Variable
from matplotlib.pylab i... | null |
19,445 | import tensorflow as tf
import numpy as np
def float32_variable_storage_getter(getter, name, shape=None, dtype=None,
initializer=None, regularizer=None,
trainable=True,
*args, **kwargs):
"""Custom variable ge... | null |
19,446 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tfdeterminism import patch
import collections
import json
import math
import os
import random
from transformers import RobertaTokenizer
import modeling
import optimization
import numpy as np
from os.path im... | Read a SQuAD json file into a list of SquadExample. |
19,447 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tfdeterminism import patch
import collections
import json
import math
import os
import random
from transformers import RobertaTokenizer
import modeling
import optimization
import numpy as np
from os.path im... | Loads a data file into a list of `InputBatch`s. |
19,448 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tfdeterminism import patch
import collections
import json
import math
import os
import random
from transformers import RobertaTokenizer
import modeling
import optimization
import numpy as np
from os.path im... | Returns `model_fn` closure for TPUEstimator. |
19,449 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tfdeterminism import patch
import collections
import json
import math
import os
import random
from transformers import RobertaTokenizer
import modeling
import optimization
import numpy as np
from os.path im... | Creates an `input_fn` closure to be passed to TPUEstimator. |
19,450 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tfdeterminism import patch
import collections
import json
import math
import os
import random
from transformers import RobertaTokenizer
import modeling
import optimization
import numpy as np
from os.path im... | Compute softmax probability over raw logits. |
19,451 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tfdeterminism import patch
import collections
import json
import math
import os
import random
from transformers import RobertaTokenizer
import modeling
import optimization
import numpy as np
from os.path im... | Validate the input FLAGS or throw an exception. |
19,452 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tfdeterminism import patch
import collections
import json
import math
import os
import random
from transformers import RobertaTokenizer
import modeling
import optimization
import numpy as np
from os.path im... | null |
19,457 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import re
import unicodedata
import six
import tensorflow as tf
The provided code snippet includes necessary dependencies for implementing the `_is_control` function. Write a Python function ... | Checks whether `chars` is a control character. |
19,459 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tfdeterminism import patch
import collections
import copy
import json
import math
import re
import os
import numpy as np
import six
import tensorflow as tf
tf.set_random_seed(SEED)
tf.random.set_random_seed... | Maps a string to a Python function, e.g., "relu" => `tf.nn.relu`. Args: activation_string: String name of the activation function. Returns: A Python function corresponding to the activation function. If `activation_string` is None, empty, or "linear", this will return None. If `activation_string` is not a string, it wi... |
19,460 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tfdeterminism import patch
import collections
import copy
import json
import math
import re
import os
import numpy as np
import six
import tensorflow as tf
tf.set_random_seed(SEED)
tf.random.set_random_seed... | Performs various post-processing on a word embedding tensor. Args: input_tensor: float Tensor of shape [batch_size, seq_length, embedding_size]. use_token_type: bool. Whether to add embeddings for `token_type_ids`. token_type_ids: (optional) int32 Tensor of shape [batch_size, seq_length]. Must be specified if `use_toke... |
19,461 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tfdeterminism import patch
import collections
import copy
import json
import math
import re
import os
import numpy as np
import six
import tensorflow as tf
tf.set_random_seed(SEED)
tf.random.set_random_seed... | Create 3D attention mask from a 2D tensor mask. Args: from_tensor: 2D or 3D Tensor of shape [batch_size, from_seq_length, ...]. to_mask: int32 Tensor of shape [batch_size, to_seq_length]. Returns: float Tensor of shape [batch_size, from_seq_length, to_seq_length]. |
19,462 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tfdeterminism import patch
import collections
import copy
import json
import math
import re
import os
import numpy as np
import six
import tensorflow as tf
tf.set_random_seed(SEED)
tf.random.set_random_seed... | Multi-headed, multi-layer Transformer from "Attention is All You Need". This is almost an exact implementation of the original Transformer encoder. See the original paper: https://arxiv.org/abs/1706.03762 Also see: https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/models/transformer.py Args: input_t... |
19,463 | import argparse
import time
import math
import os, sys
import json
import itertools
from typing import Callable, Dict, Iterable, List, Optional, Tuple
import torch
from torch import Tensor, device, dtype, nn
from torch.nn import CrossEntropyLoss
from torch.nn import functional as F
from torch.utils.data import DataLoad... | null |
19,464 | import argparse
import time
import math
import os, sys
import json
import itertools
from typing import Callable, Dict, Iterable, List, Optional, Tuple
import torch
from torch import Tensor, device, dtype, nn
from torch.nn import CrossEntropyLoss
from torch.nn import functional as F
from torch.utils.data import DataLoad... | null |
19,465 | import json
import numpy as np
import argparse
import os
import sys
import re
import json
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data
import encoder
def stardard_tokenize(sent):
def post_process(sent, is_tokenize, ... | null |
19,466 | import os, sys
import glob
import random
from collections import Counter, OrderedDict
import numpy as np
import torch
import json
import torch
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
class Corpus(object):
def __init__(self, path):
def get_lm_corpus(data):
print('Producing ... | null |
19,467 | import os, sys
import glob
import random
from collections import Counter, OrderedDict
import numpy as np
import torch
import json
import torch
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
def padding_tokens(tokens, max_seq_length, pad_token, direct, max_context_length=0):
if max_co... | null |
19,468 | import argparse
import time
import math
import os, sys
import numpy as np
import itertools
import torch
import random
from torch.utils.data import DataLoader
from gpu import (
add_gpu_params,
parse_gpu,
distributed_opt,
distributed_gather,
distributed_sync,
cleanup
)
from optimizer import (... | null |
19,469 | import argparse
import time
import math
import os, sys
import numpy as np
import itertools
import torch
import random
from torch.utils.data import DataLoader
torch.set_printoptions(threshold=100000)
from gpu import (
add_gpu_params,
parse_gpu,
distributed_opt,
distributed_gather,
distributed_syn... | null |
19,470 | import logging
import math
import os
from collections import OrderedDict
import copy
import math
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
import torch.nn.functional as F
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from torch.nn.parameter impor... | null |
19,471 | import logging
import math
import os
from collections import OrderedDict
import copy
import math
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
import torch.nn.functional as F
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from torch.nn.parameter impor... | null |
19,472 | import logging
import math
import os
from collections import OrderedDict
import copy
import math
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
import torch.nn.functional as F
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from torch.nn.parameter impor... | Implementation of the gelu activation function currently in Google Bert repo (identical to OpenAI GPT). Also see https://arxiv.org/abs/1606.08415 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.