id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
164,917 | import json
import pdb
from map_subword_serialize import schema_subword_matrix
import argparse
from transformers import AutoTokenizer
import pickle
def schema_subword_matrix(db_sep, init_idx, tables, tokenizer, table_items=None, column_items=None):
def schema_subword_dataset(seq2seq_dataset, tokenizer, tables, output... | null |
164,918 | import json
import pdb
from map_subword_serialize import schema_linking_subword
import argparse
from transformers import AutoTokenizer
import pickle
def schema_linking_subword(question_subword_dict: dict, schema_2_ids: dict, schema_linking: tuple, question_subword_len: int, schema_subword_len: int):
# assert dim m... | null |
164,919 | import os, json, pickle, argparse, sys, time
import pdb
import torch
from collections import defaultdict
import numpy as np
import re
from transformers import AutoModel, AutoTokenizer
The provided code snippet includes necessary dependencies for implementing the `quote_normalization` function. Write a Python function ... | Normalize all usage of quotation marks into a separate \" |
164,920 | import os, json, pickle, argparse, sys, time
import pdb
import torch
from collections import defaultdict
import numpy as np
import re
from transformers import AutoModel, AutoTokenizer
def subword_dict(input_ids):
word_subword_mapping = defaultdict()
for sub_idx, word_idx in enumerate(input_ids):
if word... | null |
164,921 | import datetime
import hashlib
import math
import json
import os
import shutil
import time
import codecs
import numpy as np
import pickle
import tensorflow as tf
from data import data_provider_bert
from model import bert
from model import dse_cl_bert
from config_bert import get_parser
import wrapper
The provided code ... | 多卡梯度求平均 |
164,922 | import argparse
The provided code snippet includes necessary dependencies for implementing the `get_parser` function. Write a Python function `def get_parser()` to solve the following problem:
从命令行获取parser
Here is the function:
def get_parser():
"""
从命令行获取parser
"""
parser = argparse.ArgumentParser()... | 从命令行获取parser |
164,923 | import argparse
The provided code snippet includes necessary dependencies for implementing the `get_parser` function. Write a Python function `def get_parser()` to solve the following problem:
从命令行获取parser
Here is the function:
def get_parser():
"""
从命令行获取parser
"""
parser = argparse.ArgumentParser()... | 从命令行获取parser |
164,924 | import datetime
import hashlib
import math
import json
import os
import shutil
import time
import codecs
import numpy as np
import pickle
import tensorflow as tf
from common import dump_script
from data import data_provider
from config import get_parser
The provided code snippet includes necessary dependencies for imp... | 多卡梯度求平均 |
164,925 |
The provided code snippet includes necessary dependencies for implementing the `add_path_suffix` function. Write a Python function `def add_path_suffix(path_variable, suffix)` to solve the following problem:
增加分区后缀
Here is the function:
def add_path_suffix(path_variable, suffix):
"""
增加分区后缀
"""
retu... | 增加分区后缀 |
164,926 | import codecs
The provided code snippet includes necessary dependencies for implementing the `dump_script` function. Write a Python function `def dump_script(module)` to solve the following problem:
将当前文件打印, 供模型分析. pyc文件无法正常打印，打印对应的python文件
Here is the function:
def dump_script(module):
"""
将当前文件打印, 供模型分析. p... | 将当前文件打印, 供模型分析. pyc文件无法正常打印，打印对应的python文件 |
164,927 | import os
The provided code snippet includes necessary dependencies for implementing the `line_statistics` function. Write a Python function `def line_statistics(file_name)` to solve the following problem:
统计文件行数
Here is the function:
def line_statistics(file_name):
"""
统计文件行数
"""
if file_name is Non... | 统计文件行数 |
164,928 | import collections
import copy
import json
import math
import re
import numpy as np
import six
import tensorflow as tf
def gelu(x):
"""Gaussian Error Linear Unit.
This is a smoother version of the RELU.
Original paper: https://arxiv.org/abs/1606.08415
Args:
x: float Tensor to perform activation.
... | 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... |
164,929 | import collections
import copy
import json
import math
import re
import numpy as np
import six
import tensorflow as tf
def create_initializer(initializer_range=0.02):
"""Creates a `truncated_normal_initializer` with the given range."""
return tf.truncated_normal_initializer(stddev=initializer_range)
def get_sha... | Looks up words embeddings for id tensor. Args: input_ids: int32 Tensor of shape [batch_size, seq_length] containing word ids. vocab_size: int. Size of the embedding vocabulary. embedding_size: int. Width of the word embeddings. initializer_range: float. Embedding initialization range. word_embedding_name: string. Name ... |
164,930 | import collections
import copy
import json
import math
import re
import numpy as np
import six
import tensorflow as tf
def layer_norm_and_dropout(input_tensor, dropout_prob, name=None):
"""Runs layer normalization followed by dropout."""
output_tensor = layer_norm(input_tensor, name)
output_tensor = dropout... | 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... |
164,931 | import collections
import copy
import json
import math
import re
import numpy as np
import six
import tensorflow as tf
def get_shape_list(tensor, expected_rank=None, name=None):
"""Returns a list of the shape of tensor, preferring static dimensions.
Args:
tensor: A tf.Tensor object to find the shape of.
... | 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]. |
164,932 | import collections
import copy
import json
import math
import re
import numpy as np
import six
import tensorflow as tf
def gelu(x):
"""Gaussian Error Linear Unit.
This is a smoother version of the RELU.
Original paper: https://arxiv.org/abs/1606.08415
Args:
x: float Tensor to perform activation.
... | 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... |
164,933 | import tensorflow as tf
from model import operations
The provided code snippet includes necessary dependencies for implementing the `attentive_pooling` function. Write a Python function `def attentive_pooling(inputs, attention_size, sequence_mask=None, return_alphas=False, scope="", temperature=1.0)` to solve the foll... | Attention mechanism layer which reduces RNN/Bi-RNN outputs with Attention vector. |
164,934 | import tensorflow as tf
from model import operations
The provided code snippet includes necessary dependencies for implementing the `average_pooling` function. Write a Python function `def average_pooling(inputs, sequence_mask=None)` to solve the following problem:
Attention mechanism layer which reduces RNN/Bi-RNN ou... | Attention mechanism layer which reduces RNN/Bi-RNN outputs with Attention vector. |
164,935 | import tensorflow as tf
from model import bert as modeling
from util.bert import tokenization
The provided code snippet includes necessary dependencies for implementing the `create_model` function. Write a Python function `def create_model(bert_config, is_training, input_ids, input_mask, segment_ids, scope="", init_ch... | Creates a classification model. |
164,936 | import tensorflow as tf
from model import bert as modeling
from util.bert import tokenization
def convert_single_example(ex_index, example, label_list, max_seq_length, tokenizer):
"""Converts a single `InputExample` into a single `InputFeatures`."""
"""
label_map = {}
for (i, label) in enumerate(label_l... | Convert a set of `InputExample`s to a list of `InputFeatures`. |
164,937 | import re
import tensorflow as tf
class AdamWeightDecayOptimizer(tf.train.Optimizer):
"""A basic Adam optimizer that includes "correct" L2 weight decay."""
def __init__(self,
learning_rate,
weight_decay_rate=0.0,
beta_1=0.9,
beta_2=0.999,
... | Creates an optimizer training op. |
164,938 | import collections
import re
import unicodedata
import six
import tensorflow as tf
The provided code snippet includes necessary dependencies for implementing the `validate_case_matches_checkpoint` function. Write a Python function `def validate_case_matches_checkpoint(do_lower_case, init_checkpoint)` to solve the foll... | Checks whether the casing config is consistent with the checkpoint name. |
164,939 | import collections
import re
import unicodedata
import six
import tensorflow as tf
The provided code snippet includes necessary dependencies for implementing the `printable_text` function. Write a Python function `def printable_text(text)` to solve the following problem:
Returns text encoded in a way suitable for prin... | Returns text encoded in a way suitable for print or `tf.logging`. |
164,940 | import collections
import re
import unicodedata
import six
import tensorflow as tf
def convert_to_unicode(text):
"""Converts `text` to Unicode (if it's not already), assuming utf-8 input."""
if six.PY3:
if isinstance(text, str):
return text
elif isinstance(text, bytes):
return text.decode("utf-8... | Loads a vocabulary file into a dictionary. |
164,941 | import collections
import re
import unicodedata
import six
import tensorflow as tf
def convert_by_vocab(vocab, items):
"""Converts a sequence of [tokens|ids] using the vocab."""
output = []
for item in items:
output.append(vocab[item])
return output
def convert_ids_to_tokens(inv_vocab, ids):
return conve... | null |
164,942 | import collections
import re
import unicodedata
import six
import tensorflow as tf
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:
Runs basic whitespace cleaning and sp... | Runs basic whitespace cleaning and splitting on a piece of text. |
164,943 | import collections
import re
import unicodedata
import six
import tensorflow as tf
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 whether `chars` is a whitespace characte... | Checks whether `chars` is a whitespace character. |
164,944 | 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 `def _is_control(char)` to solve the following problem:
Checks whether `chars` is a control character.
Here ... | Checks whether `chars` is a control character. |
164,945 | import collections
import re
import unicodedata
import six
import tensorflow as tf
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 whether `chars` is a punctuation chara... | Checks whether `chars` is a punctuation character. |
164,946 | import os
import codecs
import math
import numpy as np
import argparse
from tqdm import tqdm
from model import bert
from eval import bert_dse_server
The provided code snippet includes necessary dependencies for implementing the `compute_kernel_bias` function. Write a Python function `def compute_kernel_bias(vecs)` to ... | 计算kernel和bias vecs.shape = [num_samples, embedding_size]， 最后的变换：y = (x + bias).dot(kernel) |
164,947 | import os
import codecs
import math
import numpy as np
import argparse
from collections import OrderedDict
from tqdm import tqdm
import bert_dse_server
The provided code snippet includes necessary dependencies for implementing the `cos_similarity` function. Write a Python function `def cos_similarity(vec1, vec2)` to s... | :param matrix: (n,d) :param vec: (d) :return: (n) |
164,948 | import os
import codecs
import numpy as np
import argparse
from scipy import stats
def pearson_r(x, y):
assert x.ndim == y.ndim == 1
corr_mat = np.corrcoef(x, y)
return corr_mat[0, 1] | null |
164,949 | import os
import codecs
import numpy as np
import argparse
from scipy import stats
def spearman_r(x, y):
assert x.ndim == y.ndim == 1
return stats.spearmanr(x, y).correlation | null |
164,950 | import os
import codecs
import numpy as np
import argparse
from scipy import stats
def get_args():
parser = argparse.ArgumentParser(description="Semantic Textual Similarity")
parser.add_argument("--dataset", "-g", help=r"指定的数据集目录，包括语义相似度数据和其结果数据", default='')
return parser.parse_args() | null |
164,951 | import os
import codecs
import numpy as np
import pandas as pd
import argparse
from tqdm import tqdm
from gensim import corpora
from gensim.summarization.bm25 import BM25
import jieba
from typing import *
def check_rank_matrix(rank_matrix):
assert rank_matrix.ndim == 2, rank_matrix.shape
assert rank_matrix.dtyp... | null |
164,952 | import os
import codecs
import numpy as np
import pandas as pd
import argparse
from tqdm import tqdm
from gensim import corpora
from gensim.summarization.bm25 import BM25
import jieba
from typing import *
def check_rank_matrix(rank_matrix):
assert rank_matrix.ndim == 2, rank_matrix.shape
assert rank_matrix.dtyp... | null |
164,953 | import os
import codecs
import numpy as np
import pandas as pd
import argparse
from tqdm import tqdm
from gensim import corpora
from gensim.summarization.bm25 import BM25
import jieba
from typing import *
def check_rank_matrix(rank_matrix):
def precision_at_k(rank_matrix, k):
check_rank_matrix(rank_matrix)
pre... | null |
164,954 | import os
import codecs
import numpy as np
import pandas as pd
import argparse
from tqdm import tqdm
from gensim import corpora
from gensim.summarization.bm25 import BM25
import jieba
from typing import *
def check_rank_matrix(rank_matrix):
assert rank_matrix.ndim == 2, rank_matrix.shape
assert rank_matrix.dtyp... | null |
164,955 | import os
import codecs
import numpy as np
import pandas as pd
import argparse
from tqdm import tqdm
from gensim import corpora
from gensim.summarization.bm25 import BM25
import jieba
from typing import *
The provided code snippet includes necessary dependencies for implementing the `cos_similarity` function. Write a ... | :param matrix: (n,d) :param vec: (d) :return: (n) |
164,956 | import os
import codecs
import numpy as np
import pandas as pd
import argparse
from tqdm import tqdm
from gensim import corpora
from gensim.summarization.bm25 import BM25
import jieba
from typing import *
def pad_rank_label_list(rank_label_list):
lens = [len(_) for _ in rank_label_list]
if len(set(lens)) == 1:... | null |
164,957 | import os
import codecs
import numpy as np
import pandas as pd
import argparse
from tqdm import tqdm
from gensim import corpora
from gensim.summarization.bm25 import BM25
import jieba
from typing import *
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument("--dataset", help="数据集", default=""... | null |
164,958 | import random, re, os
from data.prompt_dataset import *
from data.plot_dataset import *
from data.arxiv_dataset import *
from data.yelp_dataset import *
import torch
import torch.utils.data as data
from torch.utils.data.distributed import DistributedSampler
from unidecode import unidecode
import functools
from rake_nlt... | Executes a list of functions in order |
164,959 | import random, re, os
from data.prompt_dataset import *
from data.plot_dataset import *
from data.arxiv_dataset import *
from data.yelp_dataset import *
import torch
import torch.utils.data as data
from torch.utils.data.distributed import DistributedSampler
from unidecode import unidecode
import functools
from rake_nlt... | truncates text to the prefix window size |
164,960 | import random, re, os
from data.prompt_dataset import *
from data.plot_dataset import *
from data.arxiv_dataset import *
from data.yelp_dataset import *
import torch
import torch.utils.data as data
from torch.utils.data.distributed import DistributedSampler
from unidecode import unidecode
import functools
from rake_nlt... | null |
164,961 | import random, re, os
from data.prompt_dataset import *
from data.plot_dataset import *
from data.arxiv_dataset import *
from data.yelp_dataset import *
import torch
import torch.utils.data as data
from torch.utils.data.distributed import DistributedSampler
from unidecode import unidecode
import functools
from rake_nlt... | null |
164,962 | import random, re, os
from data.prompt_dataset import *
from data.plot_dataset import *
from data.arxiv_dataset import *
from data.yelp_dataset import *
import torch
import torch.utils.data as data
from torch.utils.data.distributed import DistributedSampler
from unidecode import unidecode
import functools
from rake_nlt... | null |
164,963 | import random, re, os
from data.prompt_dataset import *
from data.plot_dataset import *
from data.arxiv_dataset import *
from data.yelp_dataset import *
import torch
import torch.utils.data as data
from torch.utils.data.distributed import DistributedSampler
from unidecode import unidecode
import functools
from rake_nlt... | null |
164,964 | import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
from dataset import PadBatchSeq, pad_seq
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os, time, gc, json, pickle, argparse, math, re
impo... | null |
164,965 | import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
from dataset import PadBatchSeq, pad_seq
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os, time, gc, json, pickle, argparse, math, re
impo... | null |
164,966 | import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
from dataset import PadBatchSeq, pad_seq
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os, time, gc, json, pickle, argparse, math, re
impo... | null |
164,967 | import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
from dataset import PadBatchSeq, pad_seq
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os, time, gc, json, pickle, argparse, math, re
impo... | null |
164,968 | import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
from dataset import PadBatchSeq, pad_seq
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os, time, gc, json, pickle, argparse, math, re
impo... | Apply LR multiplier before iteration "switch" |
164,969 | import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
from dataset import PadBatchSeq, pad_seq
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os, time, gc, json, pickle, argparse, math, re
impo... | null |
164,970 | import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
from dataset import PadBatchSeq, pad_seq
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os, time, gc, json, pickle, argparse, math, re
impo... | null |
164,971 | import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
from dataset import PadBatchSeq, pad_seq
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os, time, gc, json, pickle, argparse, math, re
impo... | null |
164,972 | import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
from dataset import PadBatchSeq, pad_seq
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os, time, gc, json, pickle, argparse, math, re
impo... | null |
164,973 | import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
from dataset import PadBatchSeq, pad_seq
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os, time, gc, json, pickle, argparse, math, re
impo... | collect tensors from all processes |
164,974 | import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
from dataset import PadBatchSeq, pad_seq
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os, time, gc, json, pickle, argparse, math, re
impo... | null |
164,975 | import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
from dataset import PadBatchSeq, pad_seq
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os, time, gc, json, pickle, argparse, math, re
impo... | null |
164,976 | import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
from dataset import PadBatchSeq, pad_seq
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os, time, gc, json, pickle, argparse, math, re
impo... | pred_slots, true_slots are like [['from_location:10-11', 'leaving_date:12-13']] |
164,977 | import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
from dataset import PadBatchSeq, pad_seq
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os, time, gc, json, pickle, argparse, math, re
impo... | null |
164,978 | import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
from dataset import PadBatchSeq, pad_seq
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os, time, gc, json, pickle, argparse, math, re
impo... | null |
164,979 | import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
from dataset import PadBatchSeq, pad_seq
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os, time, gc, json, pickle, argparse, math, re
impo... | null |
164,980 | import json
import numpy as np
import os
def get_slot_list(data_path):
# data_dir = '/'.join(data_path.split('/')[:-1])
data_type = ['train.json','valid.json','test.json']
slot_list = []
for tp in data_type:
datapp = os.path.join(data_path,tp)
with open(datapp, 'r') as f:
d... | null |
164,981 | import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
from dataset import PadBatchSeq, pad_seq
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os, time, gc, json, pickle, argparse, math, re
imp... | null |
164,982 | import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
from dataset import PadBatchSeq, pad_seq
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os, time, gc, json, pickle, argparse, math, re
imp... | null |
164,983 | import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
from dataset import PadBatchSeq, pad_seq
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os, time, gc, json, pickle, argparse, math, re
imp... | null |
164,984 | import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
from dataset import PadBatchSeq, pad_seq
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os, time, gc, json, pickle, argparse, math, re
imp... | Apply LR multiplier before iteration "switch" |
164,985 | import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
from dataset import PadBatchSeq, pad_seq
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os, time, gc, json, pickle, argparse, math, re
imp... | null |
164,986 | import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
from dataset import PadBatchSeq, pad_seq
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os, time, gc, json, pickle, argparse, math, re
imp... | null |
164,987 | import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
from dataset import PadBatchSeq, pad_seq
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os, time, gc, json, pickle, argparse, math, re
imp... | null |
164,988 | import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
from dataset import PadBatchSeq, pad_seq
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os, time, gc, json, pickle, argparse, math, re
imp... | null |
164,989 | import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
from dataset import PadBatchSeq, pad_seq
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os, time, gc, json, pickle, argparse, math, re
imp... | collect tensors from all processes |
164,990 | import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
from dataset import PadBatchSeq, pad_seq
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os, time, gc, json, pickle, argparse, math, re
imp... | null |
164,991 | import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
from dataset import PadBatchSeq, pad_seq
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os, time, gc, json, pickle, argparse, math, re
imp... | null |
164,992 | import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
from dataset import PadBatchSeq, pad_seq
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os, time, gc, json, pickle, argparse, math, re
imp... | pred_slots, true_slots are like [['from_location:10-11', 'leaving_date:12-13']] |
164,993 | import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
from dataset import PadBatchSeq, pad_seq
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os, time, gc, json, pickle, argparse, math, re
imp... | null |
164,994 | import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
from dataset import PadBatchSeq, pad_seq
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os, time, gc, json, pickle, argparse, math, re
imp... | null |
164,995 | import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
from dataset import PadBatchSeq, pad_seq
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os, time, gc, json, pickle, argparse, math, re
imp... | null |
164,996 | import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
from dataset import PadBatchSeq, pad_seq
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os, time, gc, json, pickle, argparse, math, re
imp... | null |
164,997 | import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
from dataset import PadBatchSeq, pad_seq
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os, time, gc, json, pickle, argparse, math, re
imp... | null |
164,998 | import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
from dataset import PadBatchSeq, pad_seq
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os, time, gc, json, pickle, argparse, math, re
imp... | null |
164,999 | import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
from dataset import PadBatchSeq, pad_seq
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os, time, gc, json, pickle, argparse, math, re
imp... | null |
165,000 | from mycvae.utils import *
from mycvae.model import *
import threading
import torch
import os, shutil
from torch.utils.data import DataLoader
from dataset import PadBatchSeq, TASK2INFO, MixedCLSDataset, MixedSlotTaggingDataset, PromptCLSDataset, PromptSlotTaggingDataset
from tqdm import tqdm
import json
import torch.di... | null |
165,001 | from mycvae.utils import *
from mycvae.model import *
import threading
import torch
import os, shutil
from torch.utils.data import DataLoader
from dataset import PadBatchSeq, TASK2INFO, MixedCLSDataset, MixedSlotTaggingDataset, PromptCLSDataset, PromptSlotTaggingDataset
from tqdm import tqdm
import json
import torch.di... | null |
165,002 | import os
import argparse
import torch
import shutil
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--eval_during_train',type=bool,default=False)
parser.add_argument('--test_all',type=bool,default=True)
parser.add_argument('--z_dim',type=int, default=768, help='Dimension of t... | null |
165,003 | import torch
import csv
import os
import re
import json
import numpy as np
from settings import parse_args
class PseudoCLSDataset(PromptCLSDataset):
def __init__(self, taskname, data, tokz, ctx_max_len=100):
self.ctx_max_len = ctx_max_len
self.tokz = tokz
self.max_ans_len = 0
self.da... | task2data : {'task_name': [data1, data2, data3, ...]} |
165,004 | import torch
import csv
import os
import re
import json
import numpy as np
from settings import parse_args
def rolling_window(a, window):
shape = a.shape[:-1] + (a.shape[-1] - window + 1, window)
strides = a.strides + (a.strides[-1],)
c = np.lib.stride_tricks.as_strided(a, shape=shape, strides=strides)
... | null |
165,005 | from mycvae.utils import *
from mycvae.model import *
import pickle
import os
import math
import torch
import torch.nn.functional as F
from torch.nn import DataParallel
import numpy as np
import argparse
from transformers import GPT2Tokenizer, GPT2LMHeadModel, GPT2Config
from tqdm import tqdm
from tqdm import trange
im... | null |
165,006 | from mycvae.utils import *
from mycvae.model import *
import pickle
import os
import math
import torch
import torch.nn.functional as F
from torch.nn import DataParallel
import numpy as np
import argparse
from transformers import GPT2Tokenizer, GPT2LMHeadModel, GPT2Config
from tqdm import tqdm
from tqdm import trange
im... | null |
165,007 | import sys
def uniq_n_gram_length(words, n):
ngrams = make_n_gram(words, n)
return len(list(set(ngrams)))
def distinct0(words):
dis = []
dis.append(uniq_n_gram_length(words, 1))
dis.append(uniq_n_gram_length(words, 2))
dis.append(uniq_n_gram_length(words, 3))
dis.append(uniq_n_gram_length(... | null |
165,008 | import sys
def readinputall():
datas = []
for line in sys.stdin:
#words = line.strip().split()
words = line.strip("\n").replace(' </s>', '').split()
#print("words:", words, "words-1:", words[-1], file=sys.stderr)
datas.append(words)
return datas | null |
165,009 | import sys
def readinput(k):
datas = []
count = 0
for line in sys.stdin:
#words = line.strip().split()
words = line.strip("\n").replace(' </s>', '').split()
if len(words) == 0:
count = 0
continue
if count >= k:
continue
count += 1... | null |
165,010 | import sys
def round_for_list(x, precision):
return [round(data, precision) for data in x]
def make_all_n_gram(datas):
all_words = len(datas)
all_ngram = [[] for i in range(4)]
all_ngram_len = []
all_ngram_score = []
all_ngram[0] += make_n_gram(datas, 1)
all_ngram[1] += make_n_gram(datas, 2)... | null |
165,011 | import json
import logging
import os
import sys
import time
from dataclasses import asdict, dataclass, field
from datasets import load_dataset, load_metric
from enum import Enum
from itertools import chain
from pathlib import Path
from typing import Dict, List, Optional
import numpy as np
from datasets import load_data... | This function is copy of `random_spans_helper <https://github.com/google-research/text-to-text-transfer-transformer/blob/84f8bcc14b5f2c03de51bd3587609ba8f6bbd1cd/t5/data/preprocessors.py#L2466>`__ . Training parameters to avoid padding with random_spans_noise_mask. When training a model with random_spans_noise_mask, we... |
165,012 | import json
import logging
import os
import sys
import time
from dataclasses import asdict, dataclass, field
from datasets import load_dataset, load_metric
from enum import Enum
from itertools import chain
from pathlib import Path
from typing import Dict, List, Optional
import numpy as np
from datasets import load_data... | null |
165,013 | import json
import logging
import os
import sys
import time
from dataclasses import asdict, dataclass, field
from datasets import load_dataset, load_metric
from enum import Enum
from itertools import chain
from pathlib import Path
from typing import Dict, List, Optional
import numpy as np
from datasets import load_data... | null |
165,014 | import json
import logging
import os
import sys
import time
from dataclasses import asdict, dataclass, field
from datasets import load_dataset, load_metric
from enum import Enum
from itertools import chain
from pathlib import Path
from typing import Dict, List, Optional
import numpy as np
from datasets import load_data... | null |
165,015 | import nltk
import re
import random
from random import shuffle
random.seed(1)
def get_only_chars(line):
clean_line = ""
line = line.replace("’", "")
line = line.replace("'", "")
line = line.replace("-", " ") # replace hyphens with spaces
line = line.replace("\t", " ")
line = line.replace("\n", ... | null |
165,016 | from unifymodel.dataset import PadBatchSeq, pad_seq, get_unlabel_data
import logging
import random
import torch
import numpy as np
from torch.utils.data import DataLoader
import json
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score
import torch.distributed as dist
import os, time, gc, json, pi... | null |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.