id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
19,268 | import logging
import math
import os
import random
import sys
import numpy as np
import torch
from fairseq import (
checkpoint_utils,
distributed_utils,
options,
quantization_utils,
tasks,
utils,
)
from fairseq.data import iterators
from fairseq.logging import meters, metrics, progress_bar
from ... | Train the model for one epoch and return validation losses. |
19,269 | import logging
import math
import os
import random
import sys
import numpy as np
import torch
from fairseq import (
checkpoint_utils,
distributed_utils,
options,
quantization_utils,
tasks,
utils,
)
from fairseq.data import iterators
from fairseq.logging import meters, metrics, progress_bar
from ... | null |
19,270 | from collections import namedtuple
import fileinput
import logging
import math
import sys
import os
import torch
from fairseq import checkpoint_utils, options, tasks, utils
from fairseq.data import encoders
def buffered_read(input, buffer_size):
buffer = []
with fileinput.input(files=[input], openhook=fileinpu... | null |
19,271 | from collections import namedtuple
import fileinput
import logging
import math
import sys
import os
import torch
from fairseq import checkpoint_utils, options, tasks, utils
from fairseq.data import encoders
Batch = namedtuple('Batch', 'ids src_tokens src_lengths')
def make_batches(lines, args, task, max_positions, enc... | null |
19,272 | from collections import namedtuple
import fileinput
import logging
import math
import sys
import os
import torch
from fairseq import checkpoint_utils, options, tasks, utils
from fairseq.data import encoders
def main(args):
utils.import_user_module(args)
if args.buffer_size < 1:
args.buffer_size = 1
... | null |
19,273 | from itertools import chain
import logging
import sys
import torch
from fairseq import checkpoint_utils, distributed_utils, options, utils
from fairseq.logging import metrics, progress_bar
def main(args, override_args=None):
utils.import_user_module(args)
assert args.max_tokens is not None or args.max_sentences... | null |
19,274 | from collections import Counter
from itertools import zip_longest
import logging
from multiprocessing import Pool
import os
import shutil
import sys
from fairseq import options, tasks, utils
from fairseq.data import indexed_dataset
from fairseq.binarizer import Binarizer
def dataset_dest_file(args, output_prefix, lang,... | null |
19,275 | from collections import Counter
from itertools import zip_longest
import logging
from multiprocessing import Pool
import os
import shutil
import sys
from fairseq import options, tasks, utils
from fairseq.data import indexed_dataset
from fairseq.binarizer import Binarizer
def dataset_dest_file(args, output_prefix, lang,... | null |
19,276 | from collections import Counter
from itertools import zip_longest
import logging
from multiprocessing import Pool
import os
import shutil
import sys
from fairseq import options, tasks, utils
from fairseq.data import indexed_dataset
from fairseq.binarizer import Binarizer
class Binarizer:
def binarize(
file... | null |
19,277 | from collections import Counter
from itertools import zip_longest
import logging
from multiprocessing import Pool
import os
import shutil
import sys
from fairseq import options, tasks, utils
from fairseq.data import indexed_dataset
from fairseq.binarizer import Binarizer
def main(args):
utils.import_user_module(arg... | null |
19,278 | import argparse
import os
import sys
from fairseq import bleu
from fairseq.data import dictionary
def get_parser():
parser = argparse.ArgumentParser(description='Command-line script for BLEU scoring.')
# fmt: off
parser.add_argument('-s', '--sys', default='-', help='system output')
parser.add_argument('... | null |
19,279 | import logging
import math
import os
import torch
from fairseq import checkpoint_utils, options, tasks, utils
from fairseq.data import LMContextWindowDataset
from fairseq.logging import progress_bar
from fairseq.logging.meters import StopwatchMeter, TimeMeter
from fairseq.sequence_scorer import SequenceScorer
from fair... | null |
19,280 | import logging
import math
import os
import sys
import torch
from fairseq import bleu, checkpoint_utils, options, tasks, utils
from fairseq.logging import progress_bar
from fairseq.logging.meters import StopwatchMeter, TimeMeter
from fairseq.data import encoders
def progress_bar(
iterator,
log_format: Optional... | null |
19,281 | import logging
import math
import os
import sys
import torch
from fairseq import bleu, checkpoint_utils, options, tasks, utils
from fairseq.logging import progress_bar
from fairseq.logging.meters import StopwatchMeter, TimeMeter
from fairseq.data import encoders
def main(args):
assert args.path is not None, '--path... | null |
19,282 | from __future__ import absolute_import, division, print_function
import argparse
from transformers import BertTokenizer, XLMTokenizer, XLMRobertaTokenizer
import os
from collections import defaultdict
import csv
import random
import os
import shutil
import json
TOKENIZERS = {
'bert': BertTokenizer,
'xlm': XLMTokeni... | null |
19,283 | from __future__ import absolute_import, division, print_function
import argparse
from transformers import BertTokenizer, XLMTokenizer, XLMRobertaTokenizer
import os
from collections import defaultdict
import csv
import random
import os
import shutil
import json
def panx_preprocess(args):
def _process_one_file(infile... | null |
19,284 | from __future__ import absolute_import, division, print_function
import argparse
from transformers import BertTokenizer, XLMTokenizer, XLMRobertaTokenizer
import os
from collections import defaultdict
import csv
import random
import os
import shutil
import json
TOKENIZERS = {
'bert': BertTokenizer,
'xlm': XLMTokeni... | null |
19,285 | from __future__ import absolute_import, division, print_function
import argparse
from transformers import BertTokenizer, XLMTokenizer, XLMRobertaTokenizer
import os
from collections import defaultdict
import csv
import random
import os
import shutil
import json
def udpos_preprocess(args):
def _read_one_file(file):
... | null |
19,286 | from __future__ import absolute_import, division, print_function
import argparse
from transformers import BertTokenizer, XLMTokenizer, XLMRobertaTokenizer
import os
from collections import defaultdict
import csv
import random
import os
import shutil
import json
def pawsx_preprocess(args):
def _preprocess_one_file(in... | null |
19,287 | from __future__ import absolute_import, division, print_function
import argparse
from transformers import BertTokenizer, XLMTokenizer, XLMRobertaTokenizer
import os
from collections import defaultdict
import csv
import random
import os
import shutil
import json
def xnli_preprocess(args):
def _preprocess_file(infile,... | null |
19,288 | from __future__ import absolute_import, division, print_function
import argparse
from transformers import BertTokenizer, XLMTokenizer, XLMRobertaTokenizer
import os
from collections import defaultdict
import csv
import random
import os
import shutil
import json
def tatoeba_preprocess(args):
lang3_dict = {
'afr':... | null |
19,289 | from __future__ import absolute_import, division, print_function
import argparse
from transformers import BertTokenizer, XLMTokenizer, XLMRobertaTokenizer
import os
from collections import defaultdict
import csv
import random
import os
import shutil
import json
def remove_qa_test_annotations(test_dir):
assert os.path... | null |
19,290 | from __future__ import absolute_import, division, print_function
import argparse
from transformers import BertTokenizer, XLMTokenizer, XLMRobertaTokenizer
import os
from collections import defaultdict
import csv
import random
import os
import shutil
import json
def remove_qa_test_annotations(test_dir):
assert os.path... | null |
19,291 | from __future__ import absolute_import, division, print_function
import argparse
from transformers import BertTokenizer, XLMTokenizer, XLMRobertaTokenizer
import os
from collections import defaultdict
import csv
import random
import os
import shutil
import json
def remove_qa_test_annotations(test_dir):
assert os.path... | null |
19,292 | import argparse
import glob
import logging
import os
import random
import timeit
import shutil,json
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm, trange
from evaluate_squad impo... | Train the model |
19,293 | import os
import sys
import faiss
import tempfile
import numpy as np
import faiss
def knn(x, y, k, use_gpu, dist='cosine'):
return knnGPU(x, y, k) if use_gpu else knnCPU(x, y, k, dist)
def score(x, y, fwd_mean, bwd_mean, margin, dist='cosine'):
if dist == 'cosine':
return margin(x.dot(y), (fwd_mean + bwd_mean) ... | null |
19,294 | import os
import sys
import faiss
import tempfile
import numpy as np
import faiss
def bucc_optimize(candidate2score, gold):
items = sorted(candidate2score.items(), key=lambda x: -x[1])
ngold = len(gold)
nextract = ncorrect = 0
threshold = 0
best_f1 = 0
for i in range(len(items)):
nextract += 1
if '\... | null |
19,295 | import os
import sys
import faiss
import tempfile
import numpy as np
import faiss
def similarity_search(x, y, dim, normalize=False):
num = x.shape[0]
idx = faiss.IndexFlatL2(dim)
if normalize:
faiss.normalize_L2(x)
faiss.normalize_L2(y)
idx.add(x)
scores, prediction = idx.search(y, 1)
return predic... | null |
19,296 | import argparse
import glob
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, TensorDataset
from torch.utils.data import RandomSampler, SequentialSampler
from tqdm import tqdm, trange
from transformers import (
XLMRobertaConfig,
XLMRobertaTokenizer,
XL... | null |
19,297 | import argparse
import glob
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, TensorDataset
from torch.utils.data import RandomSampler, SequentialSampler
from tqdm import tqdm, trange
from transformers import (
XLMRobertaConfig,
XLMRobertaTokenizer,
XL... | null |
19,298 | from __future__ import print_function
from collections import Counter
import string
import re
import argparse
import json
import sys
import unicodedata
def f1_score(prediction, ground_truth, lang):
def exact_match_score(prediction, ground_truth, lang):
def metric_max_over_ground_truths(metric_fn, prediction, ground_tru... | null |
19,299 | import json
import logging
import os
from functools import partial
from multiprocessing import Pool, cpu_count
import numpy as np
from tqdm import tqdm
from transformers.file_utils import is_tf_available, is_torch_available
from transformers.tokenization_bert import whitespace_tokenize
from transformers import DataProc... | Check if this is the 'max context' doc span for the token. |
19,300 | import json
import logging
import os
from functools import partial
from multiprocessing import Pool, cpu_count
import numpy as np
from tqdm import tqdm
from transformers.file_utils import is_tf_available, is_torch_available
from transformers.tokenization_bert import whitespace_tokenize
from transformers import DataProc... | null |
19,301 | import argparse
import glob
import logging
import os
import random
import shutil, pickle
import numpy as np
import torch
from torch.utils.data import DataLoader, TensorDataset
from torch.utils.data import RandomSampler, SequentialSampler
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm,... | Train the model. |
19,302 | import argparse
from seqeval.metrics import precision_score, recall_score, f1_score
import sys
import os
from collections import defaultdict
import json
from third_party.evaluate_squad import evaluate as squad_eval
from third_party.evaluate_mlqa import evaluate as mlqa_eval
def read_tag(file):
labels = []
example ... | null |
19,303 | import argparse
from seqeval.metrics import precision_score, recall_score, f1_score
import sys
import os
from collections import defaultdict
import json
from third_party.evaluate_squad import evaluate as squad_eval
from third_party.evaluate_mlqa import evaluate as mlqa_eval
def read_label(file):
with open(file, 'r')... | null |
19,304 | import argparse
from seqeval.metrics import precision_score, recall_score, f1_score
import sys
import os
from collections import defaultdict
import json
from third_party.evaluate_squad import evaluate as squad_eval
from third_party.evaluate_mlqa import evaluate as mlqa_eval
def read_squad(file):
expected_version = '... | null |
19,305 | import argparse
from seqeval.metrics import precision_score, recall_score, f1_score
import sys
import os
from collections import defaultdict
import json
from third_party.evaluate_squad import evaluate as squad_eval
from third_party.evaluate_mlqa import evaluate as mlqa_eval
def accuracy(labels, predictions, language=N... | null |
19,306 | import argparse
from seqeval.metrics import precision_score, recall_score, f1_score
import sys
import os
from collections import defaultdict
import json
from third_party.evaluate_squad import evaluate as squad_eval
from third_party.evaluate_mlqa import evaluate as mlqa_eval
def f1(labels, predictions, language=None):
... | null |
19,307 | import argparse
from seqeval.metrics import precision_score, recall_score, f1_score
import sys
import os
from collections import defaultdict
import json
from third_party.evaluate_squad import evaluate as squad_eval
from third_party.evaluate_mlqa import evaluate as mlqa_eval
def squad_em_f1(labels, predictions, languag... | null |
19,308 | import argparse
from seqeval.metrics import precision_score, recall_score, f1_score
import sys
import os
from collections import defaultdict
import json
from third_party.evaluate_squad import evaluate as squad_eval
from third_party.evaluate_mlqa import evaluate as mlqa_eval
def mlqa_em_f1(labels, predictions, language... | null |
19,309 | import argparse
from seqeval.metrics import precision_score, recall_score, f1_score
import sys
import os
from collections import defaultdict
import json
from third_party.evaluate_squad import evaluate as squad_eval
from third_party.evaluate_mlqa import evaluate as mlqa_eval
GROUP2TASK = {
"classification": ["pawsx", ... | Evaluate on all tasks if available. Args: prediction_folder (string): prediction folder that contains each task's prediction in each subfolder. label_file (string): label folder that contains each task's ground-truth label in each subfolder. Return: overall_scores (dict): a dictionary with sub-group scores. key: group ... |
19,310 | import warnings
import torch
from torch.nn import Module, Parameter, Linear
from torch.nn.init import xavier_normal_, xavier_uniform_, constant_
from torch.nn.functional import linear, softmax, dropout
The provided code snippet includes necessary dependencies for implementing the `multi_head_attention_forward` functio... | r""" Args: query, key, value: map a query and a set of key-value pairs to an output. See "Attention Is All You Need" for more details. embed_dim_to_check: total dimension of the model. num_heads: parallel attention heads. in_proj_weight, in_proj_bias: input projection weight and bias. bias_k, bias_v: bias of the key an... |
19,311 | import json
import torch
import logging
logger = logging.getLogger(__name__)
class AnswerTable:
ANS_CONVERT = {
"a man": "man",
"the man": "man",
"a woman": "woman",
"the woman": "woman",
'one': '1',
'two': '2',
'three': '3',
'four': '4',
'five... | Load model weights from pre-training model. The answers in the fine-tuned QA task (indicated by label2ans) would also be properly initialized with pre-trained QA heads. :param path: Path to model snapshot. :param model: LXRT model instance. :param label2ans: The label2ans dict of fine-tuned QA datasets, like {0: 'cat',... |
19,312 | import json
import logging
import os
import shutil
import tempfile
from functools import wraps
from hashlib import sha256
import sys
from io import open
import boto3
import requests
from botocore.exceptions import ClientError
from tqdm import tqdm
The provided code snippet includes necessary dependencies for implement... | Return the url and etag (which may be ``None``) stored for `filename`. Raise ``EnvironmentError`` if `filename` or its stored metadata do not exist. |
19,313 | import json
import logging
import os
import shutil
import tempfile
from functools import wraps
from hashlib import sha256
import sys
from io import open
import boto3
import requests
from botocore.exceptions import ClientError
from tqdm import tqdm
def get_from_cache(url, cache_dir=None):
"""
Given a URL, look f... | 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. |
19,314 | import json
import logging
import os
import shutil
import tempfile
from functools import wraps
from hashlib import sha256
import sys
from io import open
import boto3
import requests
from botocore.exceptions import ClientError
from tqdm import tqdm
The provided code snippet includes necessary dependencies for implement... | Wrapper function for s3 requests in order to create more helpful error messages. |
19,315 | import json
import logging
import os
import shutil
import tempfile
from functools import wraps
from hashlib import sha256
import sys
from io import open
import boto3
import requests
from botocore.exceptions import ClientError
from tqdm import tqdm
The provided code snippet includes necessary dependencies for implement... | Extract a de-duped collection (set) of text from a file. Expected file format is one item per line. |
19,316 | import json
import logging
import os
import shutil
import tempfile
from functools import wraps
from hashlib import sha256
import sys
from io import open
import boto3
import requests
from botocore.exceptions import ClientError
from tqdm import tqdm
def get_file_extension(path, dot=True, lower=True):
ext = os.path.s... | null |
19,317 | import math
import torch
from torch.optim import Optimizer
from torch.optim.optimizer import required
import logging
def warmup_cosine(x, warmup=0.002):
if x < warmup:
return x/warmup
return 0.5 * (1.0 + torch.cos(math.pi * x)) | null |
19,318 | import math
import torch
from torch.optim import Optimizer
from torch.optim.optimizer import required
import logging
The provided code snippet includes necessary dependencies for implementing the `warmup_constant` function. Write a Python function `def warmup_constant(x, warmup=0.002)` to solve the following problem:
... | Linearly increases learning rate over `warmup`*`t_total` (as provided to BertAdam) training steps. Learning rate is 1. afterwards. |
19,319 | import math
import torch
from torch.optim import Optimizer
from torch.optim.optimizer import required
import logging
The provided code snippet includes necessary dependencies for implementing the `warmup_linear` function. Write a Python function `def warmup_linear(x, warmup=0.002)` to solve the following problem:
Spec... | Specifies a triangular learning rate schedule where peak is reached at `warmup`*`t_total`-th (as provided to BertAdam) training step. After `t_total`-th training step, learning rate is zero. |
19,320 | import collections
import logging
import os
import unicodedata
from io import open
from .file_utils import cached_path
The provided code snippet includes necessary dependencies for implementing the `load_vocab` function. Write a Python function `def load_vocab(vocab_file)` to solve the following problem:
Loads a vocab... | Loads a vocabulary file into a dictionary. |
19,321 | import collections
import logging
import os
import unicodedata
from io import open
from .file_utils import cached_path
The provided code snippet includes necessary dependencies for implementing the `whitespace_tokenize` function. Write a Python function `def whitespace_tokenize(text)` to solve the following problem:
R... | Runs basic whitespace cleaning and splitting on a piece of text. |
19,322 | import collections
import logging
import os
import unicodedata
from io import open
from .file_utils import cached_path
The provided code snippet includes necessary dependencies for implementing the `_is_whitespace` function. Write a Python function `def _is_whitespace(char)` to solve the following problem:
Checks whet... | Checks whether `chars` is a whitespace character. |
19,323 | import collections
import logging
import os
import unicodedata
from io import open
from .file_utils import cached_path
The provided code snippet includes necessary dependencies for implementing the `_is_control` function. Write a Python function `def _is_control(char)` to solve the following problem:
Checks whether `c... | Checks whether `chars` is a control character. |
19,324 | import collections
import logging
import os
import unicodedata
from io import open
from .file_utils import cached_path
The provided code snippet includes necessary dependencies for implementing the `_is_punctuation` function. Write a Python function `def _is_punctuation(char)` to solve the following problem:
Checks wh... | Checks whether `chars` is a punctuation character. |
19,325 | import copy
import json
import logging
import math
import os
import shutil
import tarfile
import tempfile
import sys
from io import open
from torch.nn import functional as F
import numpy as np
from param import args
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, SmoothL1Loss
from .file_utils i... | null |
19,326 | import copy
import json
import logging
import math
import os
import shutil
import tarfile
import tempfile
import sys
from io import open
from torch.nn import functional as F
import numpy as np
from param import args
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, SmoothL1Loss
from .file_utils i... | Load tf checkpoints in a pytorch model |
19,327 | import copy
import json
import logging
import math
import os
import shutil
import tarfile
import tempfile
import sys
from io import open
from torch.nn import functional as F
import numpy as np
from param import args
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, SmoothL1Loss
from .file_utils i... | 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)))) Also see https://arxiv.org/abs/1606.08415 |
19,328 | import copy
import json
import logging
import math
import os
import shutil
import tarfile
import tempfile
import sys
from io import open
from torch.nn import functional as F
import numpy as np
from param import args
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, SmoothL1Loss
from .file_utils i... | null |
19,329 | import sys
import csv
import base64
import time
import logging
import numpy as np
csv.field_size_limit(sys.maxsize)
FIELDNAMES = ["img_id", "img_h", "img_w", "objects_id", "objects_conf",
"attrs_id", "attrs_conf", "num_boxes", "boxes", "features"]
logger = logging.getLogger(__name__)
The provided code sn... | Load object features from tsv file. :param fname: The path to the tsv file. :param topk: Only load features for top K images (lines) in the tsv file. Will load all the features if topk is either -1 or None. :return: A list of image object features where each feature is a dict. See FILENAMES above for the keys in the fe... |
19,330 | import torch.nn as nn
import os
from param import args
import torch
from lxrt.tokenization import BertTokenizer
from lxrt.modeling import LXRTFeatureExtraction, VISUAL_CONFIG, BertConfig, BertLayerNorm, GeLU
import logging
class InputFeatures(object):
"""A single set of features of data."""
def __init__(self, i... | Loads a data file into a list of `InputBatch`s. |
19,331 | import torch.nn as nn
import os
from param import args
import torch
from lxrt.tokenization import BertTokenizer
from lxrt.modeling import LXRTFeatureExtraction, VISUAL_CONFIG, BertConfig, BertLayerNorm, GeLU
import logging
VISUAL_CONFIG = VisualConfig()
def set_visual_config(params):
VISUAL_CONFIG.l_layers = para... | null |
19,332 | import os
import collections
import torch
import torch.nn as nn
import logging
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdm
from param import args
from lxrt.qa_answer_table import load_lxmert_qa
from tasks.vqa_model import VQAModel
from tasks.vqa_data import VQADataset, VQATorchDataset, VQAE... | null |
19,333 | import os
import collections
import torch
import torch.nn as nn
import logging
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdm
from param import args
from lxrt.qa_answer_table import load_lxmert_qa
from tasks.vqa_model import VQAModel
from tasks.vqa_data import VQADataset, VQATorchDataset, VQAE... | null |
19,334 | import torch.nn as nn
import os
import torch
import logging
from lxrt.tokenization import BertTokenizer
from lxrt.modeling import LXRTFeatureExtraction, VISUAL_CONFIG, BertConfig, BertLayerNorm, GeLU
from param import args
class InputFeatures(object):
"""A single set of features of data."""
def __init__(self, i... | Loads a data file into a list of `InputBatch`s. |
19,335 | import torch.nn as nn
import os
import torch
import logging
from lxrt.tokenization import BertTokenizer
from lxrt.modeling import LXRTFeatureExtraction, VISUAL_CONFIG, BertConfig, BertLayerNorm, GeLU
from param import args
VISUAL_CONFIG = VisualConfig()
def set_visual_config(params):
VISUAL_CONFIG.l_layers = para... | null |
19,336 | import os
import collections
from tqdm import tqdm
import torch
import torch.nn as nn
from torch.utils.data.dataloader import DataLoader
import logging
from param import args
from tasks.nlvr2_model import NLVR2Model
from tasks.nlvr2_data import NLVR2Dataset, NLVR2TorchDataset, NLVR2Evaluator
DataTuple = collections.nam... | null |
19,337 | import argparse
import random
import logging
import numpy as np
import torch
def get_optimizer(optim):
# Bind the optimizer
if optim == 'rms':
# print("Optimizer: Using RMSProp")
logger.info("Optimizer: Using RMSProp")
optimizer = torch.optim.RMSprop
elif optim == 'adam':
# p... | null |
19,338 | import os, sys, argparse, re, json
import time
import torch
import random as python_random
from uer.utils.tokenizer import *
from uer.utils.vocab import Vocab
from sqlova.utils.utils_wikisql import *
from sqlova.model.nl2sql.wikisql_models import *
from tableModel import TableTextPretraining
import comp_sql
import pand... | null |
19,339 | import os, sys, argparse, re, json
import time
import torch
import random as python_random
from uer.utils.tokenizer import *
from uer.utils.vocab import Vocab
from sqlova.utils.utils_wikisql import *
from sqlova.model.nl2sql.wikisql_models import *
from tableModel import TableTextPretraining
import comp_sql
import pand... | null |
19,340 | import os, sys, argparse, re, json
import time
import torch
import random as python_random
from uer.utils.tokenizer import *
from uer.utils.vocab import Vocab
from sqlova.utils.utils_wikisql import *
from sqlova.model.nl2sql.wikisql_models import *
from tableModel import TableTextPretraining
import comp_sql
import pand... | null |
19,341 | import os, sys, argparse, re, json
import time
import torch
import random as python_random
from uer.utils.tokenizer import *
from uer.utils.vocab import Vocab
from sqlova.utils.utils_wikisql import *
from sqlova.model.nl2sql.wikisql_models import *
from tableModel import TableTextPretraining
import comp_sql
import pand... | null |
19,342 | import os, sys, argparse, re, json
import time
import torch
import random as python_random
from uer.utils.tokenizer import *
from uer.utils.vocab import Vocab
from sqlova.utils.utils_wikisql import *
from sqlova.model.nl2sql.wikisql_models import *
from tableModel import TableTextPretraining
import comp_sql
import pand... | null |
19,343 | import os, sys, argparse, re, json
import time
import torch
import random as python_random
from uer.utils.tokenizer import *
from uer.utils.vocab import Vocab
from sqlova.utils.utils_wikisql import *
from sqlova.model.nl2sql.wikisql_models import *
from tableModel import TableTextPretraining
import comp_sql
import pand... | null |
19,344 | import os, sys, argparse, re, json
import time
import torch
import random as python_random
from uer.utils.tokenizer import *
from uer.utils.vocab import Vocab
from sqlova.utils.utils_wikisql import *
from sqlova.model.nl2sql.wikisql_models import *
from tableModel import TableTextPretraining
import comp_sql
import pand... | null |
19,345 | import os, sys, argparse, re, json
import time
import torch
import random as python_random
from uer.utils.tokenizer import *
from uer.utils.vocab import Vocab
from sqlova.utils.utils_wikisql import *
from sqlova.model.nl2sql.wikisql_models import *
from tableModel import TableTextPretraining
import comp_sql
import pand... | null |
19,346 | import os, sys, argparse, re, json
import time
import torch
import random as python_random
from uer.utils.tokenizer import *
from uer.utils.vocab import Vocab
from sqlova.utils.utils_wikisql import *
from sqlova.model.nl2sql.wikisql_models import *
from tableModel import TableTextPretraining
import comp_sql
import pand... | null |
19,347 | import os, sys, argparse, re, json
import time
import torch
import random as python_random
from uer.utils.tokenizer import *
from uer.utils.vocab import Vocab
from sqlova.utils.utils_wikisql import *
from sqlova.model.nl2sql.wikisql_models import *
from tableModel import TableTextPretraining
import comp_sql
import pand... | null |
19,348 | import os, json
from copy import deepcopy
from matplotlib.pylab import *
import torch
import torch.nn as nn
import torch.nn.functional as F
from sqlova.utils.utils import topk_multi_dim
from sqlova.utils.utils_wikisql import *
def Loss_slen(s_slen, g_slen):
loss = F.cross_entropy(s_slen, torch.tensor(g_slen).to(dev... | :param s_wv: score [ B, n_conds, T, score] :param g_wn: [ B ] :param g_wvi: [B, conds, pnt], e.g. [[[0, 6, 7, 8, 15], [0, 1, 2, 3, 4, 15]], [[0, 1, 2, 3, 16], [0, 7, 8, 9, 16]]] :return: |
19,349 | import os, json
from copy import deepcopy
from matplotlib.pylab import *
import torch
import torch.nn as nn
import torch.nn.functional as F
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
from sqlova.utils.utils import topk_multi_dim
from sqlova.utils.utils_wikisql import *
def Loss_sc(s_sc, g_sc... | null |
19,350 | import os, json
from copy import deepcopy
from matplotlib.pylab import *
import torch
import torch.nn as nn
import torch.nn.functional as F
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
from sqlova.utils.utils import topk_multi_dim
from sqlova.utils.utils_wikisql import *
def Loss_sa(s_sa, g_sa... | null |
19,351 | import os, json
from copy import deepcopy
from matplotlib.pylab import *
import torch
import torch.nn as nn
import torch.nn.functional as F
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
from sqlova.utils.utils import topk_multi_dim
from sqlova.utils.utils_wikisql import *
The provided code snip... | score = [B, T, max_seq_length] |
19,352 | import os, sys, json
from matplotlib.pylab import *
def get_qas(path_q, tid):
qas = []
with open(path_q, 'r') as f_q:
qnum = -1
for j, q1 in enumerate(f_q):
q1 = json.loads(q1)
tid_q = q1['table_id']
if tid_q != tid:
continue
else:
... | null |
19,353 | import os
from matplotlib.pylab import *
The provided code snippet includes necessary dependencies for implementing the `ensure_dir` function. Write a Python function `def ensure_dir(my_path)` to solve the following problem:
Generate directory if not exists
Here is the function:
def ensure_dir(my_path):
""" Gene... | Generate directory if not exists |
19,354 | import os
from matplotlib.pylab import *
def topk_multi_dim(tensor, n_topk=1, batch_exist=True):
if batch_exist:
idxs = []
for b, tensor1 in enumerate(tensor):
idxs1 = []
tensor1_1d = tensor1.reshape(-1)
values_1d, idxs_1d = tensor1_1d.topk(k=n_topk)
... | null |
19,355 | 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 |
19,356 | 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 |
19,357 | 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 |
19,358 | 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 |
19,359 | 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 |
19,360 | 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... | Zero-padded when word is not available (teated as <UNK>) Treat each "header tokens" as if they are NL-utterance tokens. |
19,361 | 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 |
19,362 | 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... | for backward compatibility, separated with get_g |
19,363 | 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 |
19,364 | 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 |
19,365 | 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 subset of TAPI from english-to-korean dict of table headers etc.. update_w2i_wemb. It uses wv, w2i, wemb, idx_w2i as global variables. To do 1. What should we do with the numeric? Current version do not treat them specially. But this would be modified later so that we can use tags. |
19,366 | 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 |
19,367 | 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 |
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