id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
20,694 | import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, text_segmentate, ListDataset, seed_everything, get_pool_emb
import torch.nn as nn
import torch
import torch.optim as optim
from torch.utils.data i... | 单条样本推理 |
20,695 | from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, text_segmentate, ListDataset, seed_everything, get_pool_emb
import torch.nn as nn
import torch
import torch.optim as op... | null |
20,696 | from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, text_segmentate, ListDataset, seed_everything, get_pool_emb
import torch.nn as nn
import torch
import torch.optim as op... | null |
20,697 | import torch
import torch.nn as nn
import numpy as np
from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model
from torch.optim import Adam
import torch.nn.functional as F
from bert4torch.snippets import sequence_padding, ListDataset
from bert4torch.callbacks import Callback
fro... | null |
20,698 | import torch
import torch.nn as nn
import numpy as np
from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model
from torch.optim import Adam
import torch.nn.functional as F
from bert4torch.snippets import sequence_padding, ListDataset
from bert4torch.callbacks import Callback
fro... | 对输入进行随机mask |
20,699 | ers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, text_segmentate, ListDataset
import torch.nn as nn
import torch
import torch.optim as optim
from torch.utils.data import DataLoader, Dataset
m... | null |
20,700 | ers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, text_segmentate, ListDataset
import torch.nn as nn
import torch
import torch.optim as optim
from torch.utils.data import DataLoader, Dataset
m... | null |
20,701 | import numpy as np
from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, text_segmentate, ListDataset, seed_everything, get_pool_emb
import torch.nn as nn
import torch
impor... | null |
20,702 | import numpy as np
from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, text_segmentate, ListDataset, seed_everything, get_pool_emb
import torch.nn as nn
import torch
impor... | null |
20,703 | from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, text_segmentate, ListDataset, seed_everything, get_pool_emb
import torch.nn as nn
import torch
import torch.optim as op... | null |
20,704 | from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, text_segmentate, ListDataset, seed_everything, get_pool_emb
import torch.nn as nn
import torch
import torch.optim as op... | null |
20,705 | import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from bert4torch.models import build_transformer_model
from bert4torch.generation import AutoRegressiveDecoder
from bert4torch.snippets import ListDataset, sequence_padding
from bert4torch.callbacks import Callback
fro... | [ ['哈哈', '哦', '你是猪', '不是'] ] [CLS]text1[SEP]text2[SEP] |
20,706 | import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from bert4torch.models import build_transformer_model
from bert4torch.generation import AutoRegressiveDecoder
from bert4torch.snippets import ListDataset, sequence_padding
from bert4torch.callbacks import Callback
fro... | null |
20,707 | import torch.optim as optim
import json
from torch.utils.data import DataLoader
from bert4torch.models import build_transformer_model
from bert4torch.tokenizers import SpTokenizer
from bert4torch.generation import AutoRegressiveDecoder
from bert4torch.snippets import sequence_padding, ListDataset
from bert4torch.callba... | 格式为: |
20,708 | import torch.optim as optim
import json
from torch.utils.data import DataLoader
from bert4torch.models import build_transformer_model
from bert4torch.tokenizers import SpTokenizer
from bert4torch.generation import AutoRegressiveDecoder
from bert4torch.snippets import sequence_padding, ListDataset
from bert4torch.callba... | null |
20,709 |
The provided code snippet includes necessary dependencies for implementing the `collate_fn` function. Write a Python function `def collate_fn(batch)` to solve the following problem:
单条样本格式：content：[CLS]文章[SEP] tgt: [CLS]标题[SEP]
Here is the function:
def collate_fn(batch):
"""单条样本格式：content：[CLS]文章[SEP] tgt: [C... | 单条样本格式：content：[CLS]文章[SEP] tgt: [CLS]标题[SEP] |
20,710 |
def just_show():
s1 = u'抽象了一种基于中心的战术应用场景与业务,并将网络编码技术应用于此类场景的实时数据多播业务中。在分析基于中心网络与Many-to-all业务模式特性的基础上,提出了仅在中心节点进行编码操作的传输策略以及相应的贪心算法。分析了网络编码多播策略的理论增益上界,仿真试验表明该贪心算法能够获得与理论相近的性能增益。最后的分析与仿真试验表明,在这种有中心网络的实时数据多播应用中,所提出的多播策略的实时性能要明显优于传统传输策略。'
s2 = u'普适计算环境中未知移动节点的位置信息是定位服务要解决的关键技术。在普适计算二维空间定位过程中,通过对三角形定位单元区域的误差分析,提出... | null |
20,711 | h.models import build_transformer_model, BaseModel
from bert4torch.tokenizers import Tokenizer, load_vocab
from bert4torch.snippets import sequence_padding, seed_everything, ListDataset
from bert4torch.generation import AutoRegressiveDecoder
from bert4torch.callbacks import Callback
import torch
import torch.nn as nn
i... | 单条样本格式：content：[CLS]文章[SEP] tgt: [CLS]标题[SEP] |
20,712 | h.models import build_transformer_model, BaseModel
from bert4torch.tokenizers import Tokenizer, load_vocab
from bert4torch.snippets import sequence_padding, seed_everything, ListDataset
from bert4torch.generation import AutoRegressiveDecoder
from bert4torch.callbacks import Callback
import torch
import torch.nn as nn
i... | null |
20,713 |
The provided code snippet includes necessary dependencies for implementing the `collate_fn` function. Write a Python function `def collate_fn(batch)` to solve the following problem:
单条样本格式：[CLS]文章[SEP]标题[SEP]
Here is the function:
def collate_fn(batch):
"""单条样本格式：[CLS]文章[SEP]标题[SEP]
"""
batch_token_ids,... | 单条样本格式：[CLS]文章[SEP]标题[SEP] |
20,715 | os
from bert4torch.models import build_transformer_model
from bert4torch.tokenizers import Tokenizer, load_vocab
from bert4torch.snippets import sequence_padding
from bert4torch.snippets import ListDataset
from bert4torch.callbacks import Callback
from tqdm import tqdm
import torch
from torchinfo import summary
import ... | null |
20,716 | from bert4torch.models import build_transformer_model
from bert4torch.tokenizers import Tokenizer, load_vocab
from bert4torch.snippets import sequence_padding
from bert4torch.snippets import ListDataset
from bert4torch.callbacks import Callback
from tqdm import tqdm
import torch
from torchinfo import summary
import tor... | 单条样本格式为 输入: [CLS][MASK][MASK][SEP]问题[SEP]篇章[SEP] 输出: 答案 |
20,717 | from bert4torch.models import build_transformer_model
from bert4torch.tokenizers import Tokenizer, load_vocab
from bert4torch.snippets import sequence_padding
from bert4torch.snippets import ListDataset
from bert4torch.callbacks import Callback
from tqdm import tqdm
import torch
from torchinfo import summary
import tor... | 将预测结果输出到文件，方便评估 |
20,718 | t4torch.models import build_transformer_model
from bert4torch.tokenizers import Tokenizer, load_vocab
from bert4torch.snippets import sequence_padding, text_segmentate, ListDataset
from bert4torch.generation import AutoRegressiveDecoder
from bert4torch.callbacks import Callback
import torch
from torchinfo import summar... | 单条样本格式：[CLS]篇章[SEP]答案[SEP]问题[SEP] |
20,719 | t4torch.models import build_transformer_model
from bert4torch.tokenizers import Tokenizer, load_vocab
from bert4torch.snippets import sequence_padding, text_segmentate, ListDataset
from bert4torch.generation import AutoRegressiveDecoder
from bert4torch.callbacks import Callback
import torch
from torchinfo import summar... | null |
20,720 | import numpy as np
import torch
from torch import nn, optim
from torch.utils.data import DataLoader
import torch.nn.functional as F
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.snippets import sequence_padding, ListDataset, text_segmentate, get_pool_emb
from bert4torch.generation imp... | null |
20,721 | import numpy as np
import torch
from torch import nn, optim
from torch.utils.data import DataLoader
import torch.nn.functional as F
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.snippets import sequence_padding, ListDataset, text_segmentate, get_pool_emb
from bert4torch.generation imp... | 随机观察一些样本的效果 |
20,722 | import division
import json, re
from tqdm import tqdm
from bert4torch.models import build_transformer_model
from bert4torch.tokenizers import Tokenizer, load_vocab
from torch import nn, optim
import torch
from torch.utils.data import DataLoader
from bert4torch.callbacks import Callback
from bert4torch.snippets import s... | 去掉冗余的括号 |
20,723 | import division
import json, re
from tqdm import tqdm
from bert4torch.models import build_transformer_model
from bert4torch.tokenizers import Tokenizer, load_vocab
from torch import nn, optim
import torch
from torch.utils.data import DataLoader
from bert4torch.callbacks import Callback
from bert4torch.snippets import s... | null |
20,724 | import numpy as np
from bert4torch.models import build_transformer_model
from bert4torch.tokenizers import Tokenizer
import torch.optim as optim
import torch.nn as nn
import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader
from bert4torch.snippets import ListDataset, sequence_padding
from b... | 最长公共子序列（source和target的最长非连续子序列） 返回：子序列长度, 映射关系（映射对组成的list） 注意：最长公共子序列可能不止一个，所返回的映射只代表其中一个。 |
20,725 | s, json
import numpy as np
from bert4torch.models import build_transformer_model
from bert4torch.tokenizers import Tokenizer
import torch.optim as optim
import torch.nn as nn
import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader
from bert4torch.snippets import ListDataset, sequence_paddin... | 读取数据集 |
20,726 | import numpy as np
from bert4torch.models import build_transformer_model
from bert4torch.tokenizers import Tokenizer
import torch.optim as optim
import torch.nn as nn
import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader
from bert4torch.snippets import ListDataset, sequence_padding
from b... | 数据生成器 单条样本：[CLS] Q [SEP] S [SEP] P [SEP] M [SEP] |
20,727 | s, json
import numpy as np
from bert4torch.models import build_transformer_model
from bert4torch.tokenizers import Tokenizer
import torch.optim as optim
import torch.nn as nn
import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader
from bert4torch.snippets import ListDataset, sequence_paddin... | 输出测试结果到文件 结果文件可以提交到 https://www.cluebenchmarks.com 评测。 |
20,730 | os
from bert4torch.models import build_transformer_model
from bert4torch.tokenizers import Tokenizer, load_vocab
from bert4torch.snippets import sequence_padding, text_segmentate, ListDataset
from bert4torch.generation import AutoRegressiveDecoder
from bert4torch.callbacks import Callback
from tqdm import tqdm
import t... | null |
20,731 | from bert4torch.models import build_transformer_model
from bert4torch.tokenizers import Tokenizer, load_vocab
from bert4torch.snippets import sequence_padding, text_segmentate, ListDataset
from bert4torch.generation import AutoRegressiveDecoder
from bert4torch.callbacks import Callback
from tqdm import tqdm
import torc... | 单条样本格式: [CLS]篇章[SEP]问题[SEP]答案[SEP] |
20,732 | from bert4torch.models import build_transformer_model
from bert4torch.tokenizers import Tokenizer, load_vocab
from bert4torch.snippets import sequence_padding, text_segmentate, ListDataset
from bert4torch.generation import AutoRegressiveDecoder
from bert4torch.callbacks import Callback
from tqdm import tqdm
import torc... | 将预测结果输出到文件，方便评估 |
20,733 | t4torch.models import build_transformer_model
from bert4torch.tokenizers import Tokenizer, load_vocab
from bert4torch.snippets import sequence_padding, text_segmentate, ListDataset
from bert4torch.generation import AutoRegressiveDecoder
from bert4torch.callbacks import Callback
import torch
from torchinfo import summar... | 单条样本格式：[CLS]篇章[SEP]答案[SEP]问题[SEP] |
20,734 | t4torch.models import build_transformer_model
from bert4torch.tokenizers import Tokenizer, load_vocab
from bert4torch.snippets import sequence_padding, text_segmentate, ListDataset
from bert4torch.generation import AutoRegressiveDecoder
from bert4torch.callbacks import Callback
import torch
from torchinfo import summar... | null |
20,735 | json, os
from bert4torch.models import build_transformer_model
from bert4torch.tokenizers import Tokenizer, load_vocab
from bert4torch.snippets import sequence_padding, text_segmentate, ListDataset
from bert4torch.generation import AutoRegressiveDecoder
from bert4torch.callbacks import Callback
from tqdm import tqdm
i... | null |
20,736 | from bert4torch.models import build_transformer_model
from bert4torch.tokenizers import Tokenizer, load_vocab
from bert4torch.snippets import sequence_padding, text_segmentate, ListDataset
from bert4torch.generation import AutoRegressiveDecoder
from bert4torch.callbacks import Callback
from tqdm import tqdm
import tor... | 单条样本格式：[CLS]篇章[SEP]答案[SEP]问题[SEP] |
20,737 | from bert4torch.models import build_transformer_model
from bert4torch.tokenizers import Tokenizer, load_vocab
from bert4torch.snippets import sequence_padding, text_segmentate, ListDataset
from bert4torch.generation import AutoRegressiveDecoder
from bert4torch.callbacks import Callback
from tqdm import tqdm
import tor... | 将预测结果输出到文件，方便评估 |
20,738 |
The provided code snippet includes necessary dependencies for implementing the `collate_fn` function. Write a Python function `def collate_fn(batch)` to solve the following problem:
单条样本格式：content：[CLS]文章[SEP] tgt: [CLS]标题[SEP]
Here is the function:
def collate_fn(batch):
"""单条样本格式：content：[CLS]文章[SEP] tgt: [C... | 单条样本格式：content：[CLS]文章[SEP] tgt: [CLS]标题[SEP] |
20,740 | import torch
from torch.utils.data import DataLoader
from model import uie_model, tokenizer, custom_model
from bert4torch.snippets import seed_everything, sequence_padding
from bert4torch.callbacks import Callback
from torch import nn
from torch.utils.data import Dataset
import json
from utils import get_bool_ids_great... | example: {title, prompt, content, result_list} |
20,741 | import contextlib
import functools
import json
import logging
import math
import random
import re
import shutil
import threading
import time
from functools import partial
import colorlog
import numpy as np
import torch
from colorama import Back, Fore
from tqdm import tqdm
The provided code snippet includes necessary d... | Get span set from position start and end list. Args: start_ids (List[int]/List[tuple]): The start index list. end_ids (List[int]/List[tuple]): The end index list. with_prob (bool): If True, each element for start_ids and end_ids is a tuple aslike: (index, probability). Returns: set: The span set without overlapping, ev... |
20,742 | import contextlib
import functools
import json
import logging
import math
import random
import re
import shutil
import threading
import time
from functools import partial
import colorlog
import numpy as np
import torch
from colorama import Back, Fore
from tqdm import tqdm
The provided code snippet includes necessary d... | Get idx of the last dimension in probability arrays, which is greater than a limitation. Args: probs (List[List[float]]): The input probability arrays. limit (float): The limitation for probability. return_prob (bool): Whether to return the probability Returns: List[List[int]]: The index of the last dimension meet the ... |
20,743 | import contextlib
import functools
import json
import logging
import math
import random
import re
import shutil
import threading
import time
from functools import partial
import colorlog
import numpy as np
import torch
from colorama import Back, Fore
from tqdm import tqdm
def get_id_and_prob(spans, offset_map):
pr... | null |
20,744 | import contextlib
import functools
import json
import logging
import math
import random
import re
import shutil
import threading
import time
from functools import partial
import colorlog
import numpy as np
import torch
from colorama import Back, Fore
from tqdm import tqdm
The provided code snippet includes necessary d... | Cut the Chinese sentences more precisely, reference to "https://blog.csdn.net/blmoistawinde/article/details/82379256". |
20,745 | import contextlib
import functools
import json
import logging
import math
import random
import re
import shutil
import threading
import time
from functools import partial
import colorlog
import numpy as np
import torch
from colorama import Back, Fore
from tqdm import tqdm
def dbc2sbc(s):
rs = ""
for char in s:... | null |
20,746 | import contextlib
import functools
import json
import logging
import math
import random
import re
import shutil
import threading
import time
from functools import partial
import colorlog
import numpy as np
import torch
from colorama import Back, Fore
from tqdm import tqdm
logger = Logger()
tqdm = partial(tqdm, bar_form... | Download from given url to root_dir. if file or directory specified by url is exists under root_dir, return the path directly, otherwise download from url and decompress it, return the path. Args: url (str): download url root_dir (str): root dir for downloading, it should be WEIGHTS_HOME or DATASET_HOME decompress (boo... |
20,747 | import re
import json
en2ch = {
'ORG':'机构',
'PER':'人名',
'LOC':'籍贯'
}
def preprocess(input_path, save_path, mode):
if not os.path.exists(save_path):
os.makedirs(save_path)
data_path = os.path.join(save_path, mode + ".json")
result = []
tmp = {}
tmp['id'] = 0
tmp['text'] = ''
... | null |
20,748 | import time
import argparse
import json
from decimal import Decimal
import numpy as np
from bert4torch.snippets import seed_everything
from utils import convert_ext_examples, convert_cls_examples, logger
logger = Logger()
def convert_cls_examples(raw_examples, prompt_prefix, options):
examples = []
... | null |
20,749 | import numpy as np
from bert4torch.models import build_transformer_model, BaseModel
import torch
from torch.utils.data import DataLoader
import torch.nn as nn
import torch.optim as optim
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, seed_everything
from bert4to... | null |
20,750 | import numpy as np
from bert4torch.models import build_transformer_model, BaseModel
import torch
from torch.utils.data import DataLoader
import torch.nn as nn
import torch.optim as optim
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, seed_everything
from bert4to... | null |
20,751 | import numpy as np
from bert4torch.models import build_transformer_model, BaseModel
import torch
from torch.utils.data import DataLoader
import torch.optim as optim
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, seed_everything
from bert4torch.tokenizers import ... | null |
20,752 | import numpy as np
from bert4torch.models import build_transformer_model, BaseModel
import torch
from torch.utils.data import DataLoader
import torch.optim as optim
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, seed_everything
from bert4torch.tokenizers import ... | null |
20,753 | import torch
from torch.utils.data import DataLoader
import torch.nn as nn
import torch.optim as optim
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, seed_everything
from bert4torch.layers import CRF
from bert4torch.tokenizers import Tokenizer
from bert4torch.mo... | null |
20,754 | import torch
from torch.utils.data import DataLoader
import torch.nn as nn
import torch.optim as optim
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, seed_everything
from bert4torch.layers import CRF
from bert4torch.tokenizers import Tokenizer
from bert4torch.mo... | null |
20,755 | import torch
from torch.utils.data import DataLoader
import torch.nn as nn
import torch.optim as optim
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, seed_everything
from bert4torch.layers import CRF
from bert4torch.tokenizers import Tokenizer
from bert4torch.mo... | null |
20,756 | import numpy as np
from bert4torch.models import build_transformer_model, BaseModel
import torch
from torch.utils.data import DataLoader
import torch.nn as nn
import torch.optim as optim
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, seed_everything
from bert4to... | 把第i行，第j列转化成上三角flat后的序号 |
20,757 | import numpy as np
from bert4torch.models import build_transformer_model, BaseModel
import torch
from torch.utils.data import DataLoader
import torch.nn as nn
import torch.optim as optim
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, seed_everything
from bert4to... | 获取最后一个分类层的的映射关系 |
20,758 | import numpy as np
from bert4torch.models import build_transformer_model, BaseModel
import torch
from torch.utils.data import DataLoader
import torch.nn as nn
import torch.optim as optim
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, seed_everything
from bert4to... | null |
20,759 | import numpy as np
from bert4torch.models import build_transformer_model, BaseModel
import torch
from torch.utils.data import DataLoader
import torch.nn as nn
import torch.optim as optim
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, seed_everything
from bert4to... | null |
20,760 |
def collate_fn(batch):
batch_token_ids, batch_labels, batch_entity_ids, batch_entity_labels = [], [], [], []
for d in batch:
tokens = tokenizer.tokenize(d[0], maxlen=maxlen)
mapping = tokenizer.rematch(d[0], tokens)
start_mapping = {j[0]: i for i, j in enumerate(mapping) if j}
... | null |
20,761 | import numpy as np
import torch
from torch.utils.data import DataLoader
import torch.nn as nn
import torch.optim as optim
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, seed_everything
from bert4torch.layers import CRF
from bert4torch.tokenizers import Tokenizer... | null |
20,762 | import numpy as np
import torch
from torch.utils.data import DataLoader
import torch.nn as nn
import torch.optim as optim
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, seed_everything
from bert4torch.layers import CRF
from bert4torch.tokenizers import Tokenizer... | null |
20,763 | import numpy as np
import torch
from torch.utils.data import DataLoader
import torch.nn as nn
import torch.optim as optim
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, seed_everything
from bert4torch.layers import CRF
from bert4torch.tokenizers import Tokenizer... | null |
20,764 | import numpy as np
import torch
from torch.utils.data import DataLoader
import torch.nn as nn
import torch.optim as optim
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, seed_everything
from bert4torch.layers import CRF
from bert4torch.tokenizers import Tokenizer... | null |
20,765 | import numpy as np
import torch
from torch.utils.data import DataLoader
import torch.nn as nn
import torch.optim as optim
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, seed_everything
from bert4torch.layers import CRF
from bert4torch.tokenizers import Tokenizer... | null |
20,766 | import numpy as np
import torch
from torch.utils.data import DataLoader
import torch.nn as nn
import torch.optim as optim
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, seed_everything
from bert4torch.layers import CRF
from bert4torch.tokenizers import Tokenizer... | null |
20,767 | import torch
from torch.utils.data import DataLoader
import torch.nn as nn
import torch.optim as optim
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, seed_everything
from bert4torch.layers import CRF
from bert4torch.tokenizers import Tokenizer
from bert4torch.mo... | 加载数据 单条格式：[词1, 词2, 词3, ...] |
20,768 | import torch
from torch.utils.data import DataLoader
import torch.nn as nn
import torch.optim as optim
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, seed_everything
from bert4torch.layers import CRF
from bert4torch.tokenizers import Tokenizer
from bert4torch.mo... | 标签含义 0: 单字词； 1: 多字词首字； 2: 多字词中间； 3: 多字词末字 |
20,769 | import torch
from torch.utils.data import DataLoader
import torch.nn as nn
import torch.optim as optim
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, seed_everything
from bert4torch.layers import CRF
from bert4torch.tokenizers import Tokenizer
from bert4torch.mo... | null |
20,770 | import torch
from torch.utils.data import DataLoader
import torch.nn as nn
import torch.optim as optim
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, seed_everything
from bert4torch.layers import CRF
from bert4torch.tokenizers import Tokenizer
from bert4torch.mo... | 简单的评测 该评测指标不等价于官方的评测指标，但基本呈正相关关系， 可以用来快速筛选模型。 |
20,771 | import torch
from torch.utils.data import DataLoader
import torch.nn as nn
import torch.optim as optim
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, seed_everything
from bert4torch.layers import CRF
from bert4torch.tokenizers import Tokenizer
from bert4torch.mo... | 预测结果到文件，便于用官方脚本评测 使用示例： predict_to_file('/root/icwb2-data/testing/pku_test.utf8', 'myresult.txt') 官方评测代码示例： data_dir="/root/icwb2-data" $data_dir/scripts/score $data_dir/gold/pku_training_words.utf8 $data_dir/gold/pku_test_gold.utf8 myresult.txt > myscore.txt （执行完毕后查看myscore.txt的内容末尾） |
20,772 | import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader
import torch.nn as nn
import torch.optim as optim
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, seed_everything
from bert4torch.optimizers import get_linear_schedule_with_warmu... | null |
20,773 | import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader
import torch.nn as nn
import torch.optim as optim
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, seed_everything
from bert4torch.optimizers import get_linear_schedule_with_warmu... | null |
20,774 | import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader
import torch.nn as nn
import torch.optim as optim
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, seed_everything
from bert4torch.optimizers import get_linear_schedule_with_warmu... | null |
20,775 |
def collate_fn(batch):
batch_token_ids, batch_start_labels, batch_end_labels = [], [], []
for d in batch:
tokens = tokenizer.tokenize(d[0], maxlen=max_len)[1:] # 不保留[CLS]
mapping = tokenizer.rematch(d[0], tokens)
start_mapping = {j[0]: i for i, j in enumerate(mapping) if j}
en... | null |
20,776 |
def evaluate(data):
X, Y, Z = 0, 1e-10, 1e-10
for token_ids, labels in tqdm(data, desc='Evaluation'):
start_logit, end_logit = model.predict(token_ids) # [btz, seq_len, 2]
mask, start_ids, end_ids = labels
# entity粒度
entity_pred = span_decode(start_logit, end_logit, mask)
... | null |
20,777 | import numpy as np
import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader
import torch.nn as nn
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, seed_everything
from bert4torch.optimizers import get_linear_schedule_with_warmup
from be... | null |
20,778 |
def collate_fn(batch):
batch_token_ids, batch_start_labels, batch_end_labels = [], [], []
for d in batch:
tokens = tokenizer.tokenize(d[0], maxlen=max_c_len)
mapping = tokenizer.rematch(d[0], tokens)
start_mapping = {j[0]: i for i, j in enumerate(mapping) if j}
end_mapping = {j... | null |
20,779 |
def evaluate(data):
X, Y, Z = 0, 1e-10, 1e-10
for inputs, labels in tqdm(data, desc='Evaluation'):
start_logit, end_logit, span_logits = model.predict(inputs)
mask, start_labels, end_labels, span_labels = labels
# entity粒度
entity_pred = decode(start_logit, end_logit, mask)
... | null |
20,780 |
def collate_fn(batch):
batch_token_ids, batch_segment_ids, batch_start_labels, batch_end_labels = [], [], [], []
batch_ent_type = []
for d in batch:
tokens_b = tokenizer.tokenize(d[0], maxlen=max_c_len)[1:] # 不保留[CLS]
mapping = tokenizer.rematch(d[0], tokens_b)
start_mapping = {j[... | null |
20,781 |
def evaluate(data):
X, Y, Z = 0, 1e-10, 1e-10
for (token_ids, segment_ids), labels in tqdm(data, desc='Evaluation'):
start_logit, end_logit = model.predict([token_ids, segment_ids]) # [btz, seq_len, 2]
mask, start_ids, end_ids, ent_type = labels
# entity粒度
entity_pred = mrc_d... | null |
20,782 | import argparse
import json
import pandas as pd
from tqdm import tqdm
from model import BertClient
def create_document(doc, emb, index_name):
return {
'_op_type': 'index',
'_index': index_name,
'text': doc['text'],
'title': doc['title'],
'text_vector': emb
} | null |
20,783 | import argparse
import json
import pandas as pd
from tqdm import tqdm
from model import BertClient
def load_dataset(path):
docs = []
df = pd.read_csv(path, encoding='utf-8')
for row in df.iterrows():
series = row[1]
doc = {
'title': series.Title,
'text': series.Descr... | null |
20,784 | import argparse
import json
import pandas as pd
from tqdm import tqdm
from model import BertClient
bc = BertClient(batch_size=128, use_tqdm=False)
The provided code snippet includes necessary dependencies for implementing the `bulk_predict` function. Write a Python function `def bulk_predict(docs, batch_size=256)` to ... | Predict bert embeddings. |
20,785 | import argparse
import json
from elasticsearch import Elasticsearch
from elasticsearch.helpers import bulk
def load_dataset(path):
with open(path, 'r', encoding='utf-8') as f:
return [json.loads(line) for line in f] | null |
20,786 | import os
from pprint import pprint
from flask import Flask, render_template, jsonify, request
from elasticsearch import Elasticsearch
from src.model import BertClient
SEARCH_SIZE = 10
INDEX_NAME = 'jobsearch'
def index():
return render_template('index.html')
"
=
class BertClient(object):
def __init__(self, ... | null |
20,787 | from bert4torch.tokenizers import Tokenizer
from bert4torch.snippets import sequence_padding
import numpy as np
def preprocess(text_list):
batch_token_ids, batch_segment_ids = [], []
for text in text_list:
token_ids, segment_ids = tokenizer.encode(text, maxlen=512)
batch_token_ids.append(token_... | null |
20,788 | from bert4torch.tokenizers import Tokenizer
from bert4torch.snippets import sequence_padding
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `postprocess` function. Write a Python function `def postprocess(res)` to solve the following problem:
后处理
Here is the function... | 后处理 |
20,789 | numpy as np
import torch
import torch.nn as nn
from bert4torch.snippets import get_pool_emb
from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
import time
from tqdm import tqdm
def to_numpy(tensor):
return tensor.detach().cpu().numpy() if tensor.requires_gr... | null |
20,790 | from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.snippets import sequence_padding, get_pool_emb
from bert4torch.generation import AutoRegressiveDecoder
from bert4torch.tokenizers import Tokenizer, load_vocab
from bert4torch.snippets import WebServing
synonyms_generator = SynonymsGenerat... | 含义： 产生sent的n个相似句，然后返回最相似的k个。 做法：用seq2seq生成，并用encoder算相似度并排序。 |
20,791 | import logging
import logging.config
from typing import Optional, Text
from src.utils.configs import Configuration
TRACE_LOG = "tracelogger"
def get_trace_log():
return logging.getLogger(TRACE_LOG) | null |
20,792 | import json
def send_msg(requestData):
url = 'http://localhost:8082/recommendinfo'
headers = {'content-type': 'application/json'}
ret = requests.post(url, json=requestData, headers=headers, stream=True)
if ret.status_code==200:
text = json.loads(ret.text)
return text | null |
20,793 | from sanic import Sanic
from typing import Optional, Text
import src.config.constants as constants
import src.utils.loggers as loggers
import json
def create_app(confs: Optional[Text] = None):
def start_server(confs: Optional[Text] = None, port: int = constants.DEFAULT_SERVER_PORT):
server = create_app(confs)
... | null |
20,794 | from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, text_segmentate, ListDataset, seed_everything, get_pool_emb
from bert4torch.losses import UDALoss
import torch.nn as nn
import torch
import torch.optim as optim
fr... | null |
20,795 | import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, text_segmentate, ListDataset, seed_everything, get_pool_emb
import torch.nn as nn
import torch
import torch.optim as optim
from torch.utils.data i... | null |
20,796 | import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, text_segmentate, ListDataset, seed_everything, get_pool_emb
import torch.nn as nn
import torch
import torch.optim as optim
from torch.utils.data i... | 单条样本推理 |
20,797 | from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, DeepSpeedTrainer
from bert4torch.callbacks import Callback, Logger
from bert4torch.snippets import sequence_padding, text_segmentate, ListDataset, seed_everything, get_pool_emb
import torch.nn as nn
import torch
import to... | null |
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