id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
20,798 | 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... | 单条样本推理 |
20,799 | 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, text_segmentate, get_pool_emb
from bert4torch.tokenizers import Tokenizer
from bert4torch.losse... | null |
20,800 | 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
from bert4torch.optimizers import extend_with_exponential_moving_average
import torch.... | null |
20,801 | 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, text_segmentate, get_pool_emb, seed_everything
from bert4torch.callbac... | null |
20,802 | 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.optimizers import extend_with_exponential_moving_average, get_linear_schedule_with_warm... | null |
20,803 | 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, text_segmentate, get_pool_emb
from bert4torch.tokeniz... | null |
20,804 | from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, text_segmentate, ListDataset, seed_everything, Checkpoint
import torch.nn as nn
import torch
import torch.optim as optim
import random, os, numpy as np
from torch.... | null |
20,805 | 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 |
20,806 | 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 |
20,807 | 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.callbacks import AdversarialTraining
import torch.nn as nn
import torch
import torch.optim as optim
import tor... | null |
20,808 | import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.layers import MixUp
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.op... | null |
20,809 | from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModelDDP
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, text_segmentate, ListDataset, seed_everything
import torch.nn as nn
import torch
import torch.optim as optim
from to... | null |
20,810 | from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModelDP, add_trainer
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, text_segmentate, ListDataset, seed_everything
import torch.nn as nn
import torch
import torch.optim as o... | null |
20,811 | from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModelDDP
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, text_segmentate, ListDataset, seed_everything
import torch.nn as nn
import torch
import torch.optim as optim
from to... | null |
20,812 | from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModelDP, add_trainer
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, text_segmentate, ListDataset, seed_everything
import torch.nn as nn
import torch
import torch.optim as o... | null |
20,813 | import AdamW
from torch.utils.data import DataLoader
from bert4torch.models import build_transformer_model, AccelerateTrainer
from bert4torch.tokenizers import Tokenizer
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, text_segmentate, ListDataset, seed_everything, get_pool_em... | null |
20,814 | from bert4torch.layers import GlobalPointer
from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.snippets import sequence_padding, ListDataset
from bert4torch.callbacks import Callback
from bert4torch.losses import SparseMultilabelCategoricalCrosse... | null |
20,815 | json
from bert4torch.layers import GlobalPointer
from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.snippets import sequence_padding, ListDataset
from bert4torch.callbacks import Callback
from bert4torch.losses import SparseMultilabelCategoricalC... | 评估函数，计算f1、precision、recall |
20,816 | from bert4torch.layers import LayerNorm
from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.snippets import sequence_padding, ListDataset
from bert4torch.callbacks import Callback
from bert4torch.callbacks import AdversarialTraining
from tqdm impo... | 单独抽出来，这样读取数据时候，可以根据spoes来选择跳过 |
20,817 | from bert4torch.layers import LayerNorm
from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.snippets import sequence_padding, ListDataset
from bert4torch.callbacks import Callback
from bert4torch.callbacks import AdversarialTraining
from tqdm impo... | null |
20,818 | from bert4torch.layers import LayerNorm
from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.snippets import sequence_padding, ListDataset
from bert4torch.callbacks import Callback
from bert4torch.callbacks import AdversarialTraining
from tqdm impo... | 评估函数，计算f1、precision、recall |
20,819 | from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset
from bert4torch.layers import TplinkerHandshakingKernel
from tqdm import tqdm
import torch
import torch.nn ... | 把第i行，第j列转化成上三角flat后的序号 |
20,820 | from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset
from bert4torch.layers import TplinkerHandshakingKernel
from tqdm import tqdm
import torch
import torch.nn ... | null |
20,821 | json
from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset
from bert4torch.layers import TplinkerHandshakingKernel
from tqdm import tqdm
import torch
import torc... | 评估函数，计算f1、precision、recall |
20,822 | from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset
from bert4torch.losses import MultilabelCategoricalCrossentropy
from bert4torch.layers import TplinkerHands... | 把第i行，第j列转化成上三角flat后的序号 |
20,823 | from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset
from bert4torch.losses import MultilabelCategoricalCrossentropy
from bert4torch.layers import TplinkerHands... | 获取最后一个分类层的的映射关系 |
20,824 | from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset
from bert4torch.losses import MultilabelCategoricalCrossentropy
from bert4torch.layers import TplinkerHands... | null |
20,825 | json
from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset
from bert4torch.losses import MultilabelCategoricalCrossentropy
from bert4torch.layers import Tplinker... | 评估函数，计算f1、precision、recall |
20,826 | from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.layers import MultiHeadAttentionLayer, PositionWiseFeedForward
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, seed_everything
from tqdm i... | 单独抽出来，这样读取数据时候，可以根据spoes来选择跳过 |
20,827 | from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.layers import MultiHeadAttentionLayer, PositionWiseFeedForward
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, seed_everything
from tqdm i... | null |
20,828 | from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.layers import MultiHeadAttentionLayer, PositionWiseFeedForward
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, seed_everything
from tqdm i... | null |
20,829 | from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.layers import MultiHeadAttentionLayer, PositionWiseFeedForward
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, seed_everything
from tqdm i... | 评估函数，计算f1、precision、recall |
20,830 | from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.snippets import sequence_padding, ListDataset
from bert4torch.callbacks import Callback
from tqdm import tqdm
import torch
from torch.utils.data import DataLoader, Dataset
import torch.optim as o... | 单独抽出来，这样读取数据时候，可以根据spoes来选择跳过 |
20,831 | from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.snippets import sequence_padding, ListDataset
from bert4torch.callbacks import Callback
from tqdm import tqdm
import torch
from torch.utils.data import DataLoader, Dataset
import torch.optim as o... | null |
20,832 | from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.snippets import sequence_padding, ListDataset
from bert4torch.callbacks import Callback
from tqdm import tqdm
import torch
from torch.utils.data import DataLoader, Dataset
import torch.optim as o... | null |
20,833 | from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.snippets import sequence_padding, ListDataset
from bert4torch.callbacks import Callback
from tqdm import tqdm
import torch
from torch.utils.data import DataLoader, Dataset
import torch.optim as o... | Evaluate the model on `steps` batches. |
20,834 | import numpy as np
from bert4torch.layers import LayerNorm
from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.snippets import sequence_padding, ListDataset
from bert4torch.callbacks import Callback
from tqdm import tqdm
import torch
from torch.ut... | 单独抽出来，这样读取数据时候，可以根据spoes来选择跳过 |
20,835 | import numpy as np
from bert4torch.layers import LayerNorm
from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.snippets import sequence_padding, ListDataset
from bert4torch.callbacks import Callback
from tqdm import tqdm
import torch
from torch.ut... | null |
20,836 | json
import numpy as np
from bert4torch.layers import LayerNorm
from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.snippets import sequence_padding, ListDataset
from bert4torch.callbacks import Callback
from tqdm import tqdm
import torch
from tor... | 评估函数，计算f1、precision、recall |
20,837 | import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, get_pool_emb, seed_everything
import torch.nn as nn
import torch
import torch.optim as optim
from torch.utils.data import DataLoader
... | null |
20,838 | from bert4torch.snippets import sequence_padding
from tqdm import tqdm
import numpy as np
import scipy.stats
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.tokenizers import Tokenizer
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, get_pool_e... | 加载数据（带标签） 单条格式：(文本1, 文本2, 标签) |
20,839 | from bert4torch.snippets import sequence_padding
from tqdm import tqdm
import numpy as np
import scipy.stats
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.tokenizers import Tokenizer
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, get_pool_e... | null |
20,840 | from bert4torch.snippets import sequence_padding
from tqdm import tqdm
import numpy as np
import scipy.stats
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.tokenizers import Tokenizer
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, get_pool_e... | null |
20,841 | from bert4torch.snippets import sequence_padding
from tqdm import tqdm
import numpy as np
import scipy.stats
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.tokenizers import Tokenizer
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, get_pool_e... | null |
20,842 | from bert4torch.snippets import sequence_padding
from tqdm import tqdm
import numpy as np
import scipy.stats
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.tokenizers import Tokenizer
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, get_pool_e... | null |
20,843 |
The provided code snippet includes necessary dependencies for implementing the `load_data` function. Write a Python function `def load_data(filenames)` to solve the following problem:
加载数据（带标签） 单条格式：(文本1, 文本2, 标签)
Here is the function:
def load_data(filenames):
"""加载数据（带标签）
单条格式：(文本1, 文本2, 标签)
"""
D... | 加载数据（带标签） 单条格式：(文本1, 文本2, 标签) |
20,844 | from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, get_pool_emb
import torch.nn as nn
import torch
import torch.optim as optim
from torch.utils.data import D... | null |
20,845 | from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, get_pool_emb
import torch.nn as nn
import torch
import torch.optim as optim
from torch.utils.data import D... | null |
20,846 | from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, get_pool_emb
import torch.nn as nn
import torch
import torch.optim as optim
from torch.utils.data import D... | null |
20,848 | from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, get_pool_emb
import torch.nn as nn
import torch
import torch.optim as optim
from torch.utils.data import D... | null |
20,849 | from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, get_pool_emb
import torch.nn as nn
import torch
import torch.optim as optim
from torch.utils.data import D... | null |
20,850 | from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, get_pool_emb
import torch.nn as nn
import torch
import torch.optim as optim
from torch.utils.data import D... | null |
20,851 | from bert4torch.snippets import sequence_padding
from tqdm import tqdm
import numpy as np
import scipy.stats
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.tokenizers import Tokenizer
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, get_pool_e... | 加载数据（带标签） 单条格式：(文本1, 文本2, 标签) |
20,852 | from bert4torch.snippets import sequence_padding
from tqdm import tqdm
import numpy as np
import scipy.stats
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.tokenizers import Tokenizer
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, get_pool_e... | null |
20,853 | from bert4torch.snippets import sequence_padding
from tqdm import tqdm
import numpy as np
import scipy.stats
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.tokenizers import Tokenizer
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, get_pool_e... | null |
20,854 | from bert4torch.snippets import sequence_padding
from tqdm import tqdm
import numpy as np
import scipy.stats
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.tokenizers import Tokenizer
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, get_pool_e... | null |
20,855 | import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, get_pool_emb, seed_everything
import torch.nn as nn
import torch
import torch.optim as optim
from torch.utils.data import DataLoader
... | null |
20,856 | from bert4torch.snippets import sequence_padding
from tqdm import tqdm
import numpy as np
import scipy.stats
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.tokenizers import Tokenizer
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, get_pool_e... | 加载数据（带标签） 单条格式：(文本1, 文本2, 标签) |
20,857 | from bert4torch.snippets import sequence_padding
from tqdm import tqdm
import numpy as np
import scipy.stats
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.tokenizers import Tokenizer
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, get_pool_e... | null |
20,858 | from bert4torch.snippets import sequence_padding
from tqdm import tqdm
import numpy as np
import scipy.stats
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.tokenizers import Tokenizer
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, get_pool_e... | null |
20,859 | from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.snippets import sequence_padding, ListDataset, get_pool_emb
from bert4torch.layers import BERT_WHITENING
from tqdm import tqdm
import torch
from torch.utils.data import DataLoader
import scipy.st... | null |
20,860 | import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from tqdm import tqdm
from bert4torch.tokenizers import Tokenizer, load_vocab
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.snippets import ListDataset, sequence_padding
from bert4torch.call... | null |
20,861 | import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from tqdm import tqdm
from bert4torch.tokenizers import Tokenizer, load_vocab
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.snippets import ListDataset, sequence_padding
from bert4torch.call... | null |
20,862 | import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from tqdm import tqdm
from bert4torch.tokenizers import Tokenizer, load_vocab
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.snippets import ListDataset, sequence_padding
from bert4torch.call... | null |
20,863 | from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, get_pool_emb, seed_everything
from bert4torch.losses import ContrastiveLoss
import torch
import torch.opti... | null |
20,864 | om bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, get_pool_emb, seed_everything
import torch.nn as nn
import torch
import torch.optim as optim
from torch.util... | null |
20,865 | om bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, get_pool_emb, seed_everything
import torch.nn as nn
import torch
import torch.optim as optim
from torch.util... | null |
20,866 | from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, get_pool_emb, seed_everything
import torch.nn as nn
import torch
import torch.optim as optim
from torch.utils.data import DataLoader, TensorDataset
fr... | null |
20,867 | from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, get_pool_emb, seed_everything
import torch.nn as nn
import torch
import torch.optim as optim
from torch.utils.data import DataLoader, TensorDataset
fr... | null |
20,868 | import torch
from torch import Tensor
import numpy as np
import pandas as pd
The provided code snippet includes necessary dependencies for implementing the `cos_sim` function. Write a Python function `def cos_sim(vector_a, vector_b)` to solve the following problem:
计算两个向量之间的余弦相似度 :param vector_a: 向量 a :param vector_b:... | 计算两个向量之间的余弦相似度 :param vector_a: 向量 a :param vector_b: 向量 b :return: sim |
20,869 | import torch
from torch import Tensor
import numpy as np
import pandas as pd
def cos_sim4matrix(arr, brr):
return 0.5 + 0.5 * (arr.dot(brr.T) / (np.sqrt(np.sum(arr * arr)) * np.sqrt(np.sum(brr * brr, axis = 1)))) | null |
20,870 | import torch
from torch import Tensor
import numpy as np
import pandas as pd
def cos_sim4matrix_2(arr, brr):
return (arr.dot(brr.T) / (np.sqrt(np.sum(arr * arr)) * np.sqrt(np.sum(brr * brr, axis=1)))) | null |
20,871 | import torch
from torch import Tensor
import numpy as np
import pandas as pd
The provided code snippet includes necessary dependencies for implementing the `read_q_std_q_corpus` function. Write a Python function `def read_q_std_q_corpus(q_std_file, q_std_vectors_file, q_corpus_file, q_corpus_vectors_file)` to solve th... | 读取q_std、q_corpus语料和向量 |
20,872 | import torch
from torch import Tensor
import numpy as np
import pandas as pd
def pytorch_cos_sim(a: Tensor, b: Tensor):
if not isinstance(a, torch.Tensor):
a = torch.tensor(a)
if not isinstance(b, torch.Tensor):
b = torch.tensor(b)
if len(a.shape) == 1:
a = a.unsqueeze(0)
if len(... | 计算召回topK的指标 |
20,873 | from bert4torch.losses import ContrastiveLoss
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, get_pool_emb, seed_everything
import torch.nn as nn
import torch
import torch.optim as optim
from torch.... | null |
20,875 | ort Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, get_pool_emb
import torch.nn as nn
import torch
import torch.optim as optim
from torch.utils.data import DataLoader
from sklearn.metrics... | null |
20,876 | ort Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, get_pool_emb
import torch.nn as nn
import torch
import torch.optim as optim
from torch.utils.data import DataLoader
from sklearn.metrics... | null |
20,877 | ort Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, get_pool_emb
import torch.nn as nn
import torch
import torch.optim as optim
from torch.utils.data import DataLoader
from sklearn.metrics... | null |
20,878 | from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, get_pool_emb, seed_everything
import torch.nn as nn
import torch
import torch.optim as optim
from torch.utils.data import DataLoader
from sklearn.metr... | null |
20,879 | from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, get_pool_emb, seed_everything
import torch.nn as nn
import torch
import torch.optim as optim
from torch.utils.data import DataLoader
from sklearn.metr... | null |
20,880 | from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, get_pool_emb, seed_everything
import torch.nn as nn
import torch
import torch.optim as optim
from torch.utils.data import DataLoader
from sklearn.metr... | null |
20,881 | from torch.nn import Module
from basic_language_model_chatglm import cli_demo
def auto_configure_device_map(num_gpus: int) -> Dict[str, int]:
# embeddings.word_embeddings 占用1层
# LayerNormFinal 和 lm_head 占用1层
# transformer.layers 占用 28 层
# 总共30层分配到num_gpus张卡上
num_trans_layers = 28
per_gpu_layers ... | null |
20,882 | import ChatGlm2OpenaiApi
from bert4torch.pipelines import ChatOpenaiClient, ChatOpenaiClientSseclient
def call_openai(stream=True):
url = 'http://127.0.0.1:8000'
messages = [
{"content": "你好", "role": "user"},
{"content": "你好，我是法律大模型", "role": "assistant"},
{"content": "基金从业... | null |
20,883 | import ChatGlm2OpenaiApi
from bert4torch.pipelines import ChatOpenaiClient, ChatOpenaiClientSseclient
def call_sseclient():
url = 'http://127.0.0.1:8000/chat/completions'
body = {
"messages": [
{"content": "你好", "role": "user"},
{"content": "你好，我是法律大模型", ... | null |
20,884 |
cli_demo = ChatGlmCli(dir_path, generation_config=generation_config, quantization_config=quantization_config)
async def create_item(request: Request):
json_post_raw = await request.json()
json_post = json.dumps(json_post_raw)
json_post_list = json.loads(json_post)
prompt = json_post_l... | null |
20,885 | from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import os
from bert4torch.quantization import quantize_cpm_kernels
def clear():
os.system('cls' if platform.system() == 'Windows' else 'clear') | null |
20,886 | import numpy as np
from bert4torch.models import build_transformer_model
from bert4torch.tokenizers import SpTokenizer
from bert4torch.generation import AutoRegressiveDecoder
import torch
import jieba
jieba.initialize()
The provided code snippet includes necessary dependencies for implementing the `pre_tokenize` funct... | 分词前处理函数，'\n'替换成'▃', ' '替换成'▂' |
20,887 | import torch
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
synonyms_generator = SynonymsGenerator(bos_token_id=None, eos_toke... | 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。 效果： >>> gen_synonyms(u'微信和支付宝哪个好？') [ u'微信和支付宝，哪个好?', u'微信和支付宝哪个好', u'支付宝和微信哪个好', u'支付宝和微信哪个好啊', u'微信和支付宝那个好用？', u'微信和支付宝哪个好用', u'支付宝和微信那个更好', u'支付宝和微信哪个好用', u'微信和支付宝用起来哪个好？', u'微信和支付宝选哪个好', ] |
20,888 | import build_transformer_model
from bert4torch.snippets import sequence_padding, text_segmentate
import torch.nn as nn
import torch
import torch.optim as optim
from torch.utils.data import DataLoader
import torch
from bert4torch.models import build_transformer_model, DeepSpeedTrainer
from bert4torch.snippets import Lis... | null |
20,889 | import build_transformer_model
from bert4torch.snippets import sequence_padding, text_segmentate
import torch.nn as nn
import torch
import torch.optim as optim
from torch.utils.data import DataLoader
import torch
from bert4torch.models import build_transformer_model, DeepSpeedTrainer
from bert4torch.snippets import Lis... | null |
20,890 | from bert4torch.models import build_transformer_model
from bert4torch.snippets import sequence_padding, text_segmentate
import torch.nn as nn
import torch
import torch.optim as optim
from torch.utils.data import DataLoader
import torch
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.sni... | null |
20,891 | from bert4torch.models import build_transformer_model
from bert4torch.snippets import sequence_padding, text_segmentate
import torch.nn as nn
import torch
import torch.optim as optim
from torch.utils.data import DataLoader
import torch
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.sni... | null |
20,892 | from dataclasses import dataclass, field
from typing import List, Optional, Dict, Sequence
from bert4torch.snippets import log_warn
import torch
from torch import nn
import os
def get_model_config(model):
if model == 'bloom':
model_type = 'bloom'
dir_path = 'E:/pretrain_ckpt/bloom/bloomz-560m'
... | null |
20,893 | from dataclasses import dataclass, field
from typing import List, Optional, Dict, Sequence
from bert4torch.snippets import log_warn
import torch
from torch import nn
import os
def get_nbit_lora_model(model, load_in_nbit=None, use_lora=False):
# 量化
if load_in_nbit == 8:
model.gradient_checkpointing_enab... | null |
20,894 | from dataclasses import dataclass, field
from typing import List, Optional, Dict, Sequence
from bert4torch.snippets import log_warn
import torch
from torch import nn
import os
class Conversation:
"""A class that manages prompt templates and keeps all conversation history."""
# The name of this template
name... | Register a new conversation template. |
20,895 | from glob import glob
import torch
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import DataLoader
from bert4torch.optimizers import get_linear_schedule_with_warmup
from bert4torch.snippets import DottableDict, ListDataset, sequence_padding, seed_everything
from bert4torch.models imp... | null |
20,896 | import torch.nn as nn
import torch
import torch.optim as optim
from torch.utils.data import DataLoader
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.snippets import ListDataset, sequence_padding, DottableDict
from bert4torch.callbacks import Callback, Logger
from bert4torch.optimizers... | null |
20,897 | from bert4torch.models import build_transformer_model
from bert4torch.snippets import sequence_padding, text_segmentate, ListDataset, DottableDict
from bert4torch.callbacks import Callback, Logger
import torch.nn as nn
import torch
import torch.optim as optim
from torch.utils.data import DataLoader
import torch
from be... | Preprocessing the datasets. part of code modified from https://github.com/lm-sys/FastChat |
20,898 | from bert4torch.models import build_transformer_model
from bert4torch.snippets import sequence_padding, text_segmentate, ListDataset, DottableDict
from bert4torch.callbacks import Callback, Logger
import torch.nn as nn
import torch
import torch.optim as optim
from torch.utils.data import DataLoader
import torch
from be... | null |
20,899 | from bert4torch.models import build_transformer_model
from bert4torch.snippets import sequence_padding
import torch.nn as nn
import torch
import torch.optim as optim
from torch.utils.data import DataLoader
import torch
from bert4torch.models import build_transformer_model
from bert4torch.snippets import IterDataset, Do... | null |
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