Datasets:
jblitzar
/
github-python-corpus

Dataset card Data Studio Files
xet
 Community
text
stringlengths
1
93.6k
# Return list of tag
def GetTags(self, soup):
return soup.find_all('tr')
# Return title string
def GetTitle(self, tag):
return re.sub('[●\n ]', '', tag.find('a').contents[0].string)
# Return URL string
def GetURL(self, tag):
return 'http://kszx.zju.edu.cn/' + tag.find('a')['href']
# Return publish time
def GetPublishTime(self, tag):
return re.sub('[【】]', '', tag.find('font', color='lightgray').string)
# Addditon check, return True if unused
def AdditionCheck(self, tag):
return True
# Return brief string
def GetBrief(self, tag, keywordstring):
return ''
# <FILESEP>
# coding=utf-8
""" Compute TextEmb for classification/regression tasks."""
from __future__ import absolute_import, division, print_function
import argparse
import glob
import logging
import os
import random
import json
import numpy as np
import torch
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
TensorDataset, Subset)
from torch.utils.data.distributed import DistributedSampler
try:
from torch.utils.tensorboard import SummaryWriter
except:
from tensorboardX import SummaryWriter
from tqdm import tqdm, trange
from transformers import (WEIGHTS_NAME, BertConfig, BertModel, BertTokenizer)
from transformers import AdamW, get_linear_schedule_with_warmup
from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_output_modes as output_modes
from transformers import glue_processors as processors
from transformers import glue_convert_examples_to_features as convert_examples_to_features
logger = logging.getLogger(__name__)
ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) for conf in (BertConfig, )), ())
MODEL_CLASSES = {
'bert': (BertConfig, BertModel, BertTokenizer)
}
def set_seed(args):
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if args.n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
def compute_textemb(args, train_dataset, model):
""" Train the model """
tb_writer = SummaryWriter()
args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
train_sampler = SequentialSampler(train_dataset)
train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size)
# multi-gpu training (should be after apex fp16 initialization)
if args.n_gpu > 1:
model = torch.nn.DataParallel(model)
logger.info("***** Compute TextEmb *****")
logger.info("Num examples = %d", len(train_dataset))
logger.info("Batch size = %d", args.train_batch_size)
model.zero_grad()
train_iterator = trange(int(args.num_train_epochs), desc="Epoch", disable=False)
set_seed(args) # Added here for reproductibility (even between python 2 and 3)
total_num_examples = 0
global_feature_dict = {}
for _ in train_iterator:
num_examples = 0