id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
20,900 | from glob import glob
import torch
from torch import nn
from bert4torch.snippets import DottableDict, ListDataset, sequence_padding
from bert4torch.models import BaseModel, build_transformer_model
from bert4torch.generation import SeqGeneration
from bert4torch.callbacks import Callback, Logger
from bert4torch.trainer i... | null |
20,901 | from glob import glob
import torch
from torch import nn
from bert4torch.snippets import DottableDict, ListDataset, sequence_padding
from bert4torch.models import BaseModel, build_transformer_model
from bert4torch.generation import SeqGeneration
from bert4torch.callbacks import Callback, Logger
from bert4torch.trainer i... | null |
20,902 | from bert4torch.snippets import sequence_padding
from bert4torch.callbacks import Callback
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 transformers import AutoTokenizer
from bert... | null |
20,903 | from bert4torch.snippets import sequence_padding
from bert4torch.callbacks import Callback
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 transformers import AutoTokenizer
from bert... | null |
20,904 | torch
from bert4torch.models import build_transformer_model
from transformers import AutoTokenizer
import re
import json
import os
device = 'cuda' if torch.cuda.is_available() else 'cpu'
tokenizer.padding_side = 'left'
model = build_transformer_model(config_path=config_path, checkpoint_path=checkpoint_path).half()
... | Naive Bayes-based Context Extension 演示代码 |
20,905 | 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, DeepSpeedTrainer
from bert4torch.snippets... | null |
20,906 | 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, DeepSpeedTrainer
from bert4torch.snippets... | null |
20,907 | import build_transformer_model
from bert4torch.snippets import sequence_padding, text_segmentate
from bert4torch.callbacks import Callback
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
... | null |
20,908 | import build_transformer_model
from bert4torch.snippets import sequence_padding, text_segmentate
from bert4torch.callbacks import Callback
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
... | null |
20,909 | 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 |
20,910 | 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 |
20,911 | 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.snippets import ListDatase... | null |
20,912 | 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.snippets import ListDatase... | null |
20,913 | from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.snippets import sequence_padding
from bert4torch.callbacks import Callback
from bert4torch.optimizers import get_linear_schedule_with_warmup
import json
import codecs
import numpy as np
from tqdm... | null |
20,914 | from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.snippets import sequence_padding
from bert4torch.callbacks import Callback
from bert4torch.optimizers import get_linear_schedule_with_warmup
import json
import codecs
import numpy as np
from tqdm... | 从句子s中找与w最相近的片段， 借助分词工具和ngram的方式尽量精确地确定边界。 |
20,915 | from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.snippets import sequence_padding
from bert4torch.callbacks import Callback
from bert4torch.optimizers import get_linear_schedule_with_warmup
import json
import codecs
import numpy as np
from tqdm... | null |
20,916 | from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.snippets import sequence_padding
from bert4torch.callbacks import Callback
from bert4torch.optimizers import get_linear_schedule_with_warmup
import json
import codecs
import numpy as np
from tqdm... | 输入question和headers，转SQL |
20,917 | from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.snippets import sequence_padding
from bert4torch.callbacks import Callback
from bert4torch.optimizers import get_linear_schedule_with_warmup
import json
import codecs
import numpy as np
from tqdm... | 判断两个SQL字典是否全匹配 |
20,918 | 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, get_pool_emb
from bert4torch.layers import CRF
from bert4torch.tokenizers import Tokenizer
from bert4torch.models... | null |
20,919 | 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, get_pool_emb
from bert4torch.layers import CRF
from bert4torch.tokenizers import Tokenizer
from bert4torch.models... | null |
20,920 | e
from tqdm import tqdm
from bert4torch.models import build_transformer_model
from bert4torch.tokenizers import Tokenizer, load_vocab
from bert4torch.snippets import sequence_padding, ListDataset
from bert4torch.callbacks import Callback
from bert4torch.generation import AutoRegressiveDecoder
import torch
from torch.ut... | null |
20,921 | e
from tqdm import tqdm
from bert4torch.models import build_transformer_model
from bert4torch.tokenizers import Tokenizer, load_vocab
from bert4torch.snippets import sequence_padding, ListDataset
from bert4torch.callbacks import Callback
from bert4torch.generation import AutoRegressiveDecoder
import torch
from torch.ut... | null |
20,922 | from itertools import groupby
from tqdm import tqdm
from bert4torch.losses import SparseMultilabelCategoricalCrossentropy
from bert4torch.tokenizers import Tokenizer
from bert4torch.layers import EfficientGlobalPointer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.snippets import sequ... | null |
20,923 | from itertools import groupby
from tqdm import tqdm
from bert4torch.losses import SparseMultilabelCategoricalCrossentropy
from bert4torch.tokenizers import Tokenizer
from bert4torch.layers import EfficientGlobalPointer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.snippets import sequ... | 评估函数，计算f1、precision、recall |
20,924 | from itertools import groupby
from tqdm import tqdm
from bert4torch.losses import SparseMultilabelCategoricalCrossentropy
from bert4torch.tokenizers import Tokenizer
from bert4torch.layers import EfficientGlobalPointer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.snippets import sequ... | 预测结果到文件，方便提交 |
20,925 | from bert4torch.models import build_transformer_model, BaseModel, BERT
from bert4torch.snippets import sequence_padding, ListDataset
from bert4torch.callbacks import Callback
from bert4torch.tokenizers import Tokenizer
from bert4torch.layers import BertLayer
import torch
from torch.utils.data import DataLoader, Dataset... | null |
20,926 | from bert4torch.models import build_transformer_model, BaseModel, BERT
from bert4torch.snippets import sequence_padding, ListDataset
from bert4torch.callbacks import Callback
from bert4torch.tokenizers import Tokenizer
from bert4torch.layers import BertLayer
import torch
from torch.utils.data import DataLoader, Dataset... | null |
20,927 | json
from bert4torch.models import build_transformer_model, BaseModel, BERT
from bert4torch.snippets import sequence_padding, ListDataset
from bert4torch.callbacks import Callback
from bert4torch.tokenizers import Tokenizer
from bert4torch.layers import BertLayer
import torch
from torch.utils.data import DataLoader, Da... | 输出预测结果到文件 结果文件可以提交到 https://www.cluebenchmarks.com 评测。 |
20,928 | import os
import json
import torch
import sqlite3
import torch.optim as optim
import transformers
from tqdm import tqdm
from tokenizers import AddedToken
from func_timeout import func_set_timeout, FunctionTimedOut
from torch.utils.tensorboard import SummaryWriter
from torch.utils.data import DataLoader
from transformer... | null |
20,929 | import os
import json
import torch
import sqlite3
import torch.optim as optim
import transformers
from tqdm import tqdm
from tokenizers import AddedToken
from func_timeout import func_set_timeout, FunctionTimedOut
from torch.utils.tensorboard import SummaryWriter
from torch.utils.data import DataLoader
from transformer... | null |
20,930 | import os
import json
import torch
import sqlite3
import torch.optim as optim
import transformers
from tqdm import tqdm
from tokenizers import AddedToken
from func_timeout import func_set_timeout, FunctionTimedOut
from torch.utils.tensorboard import SummaryWriter
from torch.utils.data import DataLoader
from transformer... | null |
20,931 | import build_transformer_model, BaseModel
from bert4torch.tokenizers import Tokenizer, load_vocab
from bert4torch.snippets import sequence_padding, text_segmentate, ListDataset
from bert4torch.callbacks import Callback
from bert4torch.generation import AutoRegressiveDecoder
import torch
from torch.utils.data import Dat... | null |
20,932 | import build_transformer_model, BaseModel
from bert4torch.tokenizers import Tokenizer, load_vocab
from bert4torch.snippets import sequence_padding, text_segmentate, ListDataset
from bert4torch.callbacks import Callback
from bert4torch.generation import AutoRegressiveDecoder
import torch
from torch.utils.data import Dat... | null |
20,933 | from bert4torch.tokenizers import Tokenizer, load_vocab
import torch
from torch import nn, optim
from torch.utils.data import DataLoader
from bert4torch.snippets import sequence_padding, ListDataset, take_along_dim
from bert4torch.callbacks import Callback
from cchess import *
device = 'cuda' if torch.cuda.is_available... | 数据生成器 |
20,934 | from bert4torch.tokenizers import Tokenizer, load_vocab
import torch
from torch import nn, optim
from torch.utils.data import DataLoader
from bert4torch.snippets import sequence_padding, ListDataset, take_along_dim
from bert4torch.callbacks import Callback
from cchess import *
def convert(tf_path, torch_path):
# 用... | null |
20,935 | import importlib.util
from typing import Any, Tuple, Union
import torch
from packaging import version
from torch4keras.snippets.import_utils import is_package_available
import sys
The provided code snippet includes necessary dependencies for implementing the `is_accelerate_available` function. Write a Python function ... | 是否可以使用accelerate |
20,936 | import importlib.util
from typing import Any, Tuple, Union
import torch
from packaging import version
from torch4keras.snippets.import_utils import is_package_available
import sys
The provided code snippet includes necessary dependencies for implementing the `is_flash_attn_available` function. Write a Python function ... | 是否可以使用包flash_attn |
20,937 | import importlib.util
from typing import Any, Tuple, Union
import torch
from packaging import version
from torch4keras.snippets.import_utils import is_package_available
import sys
The provided code snippet includes necessary dependencies for implementing the `is_xformers_available` function. Write a Python function `d... | 是否可以使用xformers加速 |
20,938 | import importlib.util
from typing import Any, Tuple, Union
import torch
from packaging import version
from torch4keras.snippets.import_utils import is_package_available
import sys
The provided code snippet includes necessary dependencies for implementing the `is_fastapi_available` function. Write a Python function `de... | 是否可以使用包fastapi |
20,939 | import importlib.util
from typing import Any, Tuple, Union
import torch
from packaging import version
from torch4keras.snippets.import_utils import is_package_available
import sys
def is_pydantic_available():
return is_package_available('pydantic') | null |
20,940 | import importlib.util
from typing import Any, Tuple, Union
import torch
from packaging import version
from torch4keras.snippets.import_utils import is_package_available
import sys
def is_trl_available():
return is_package_available("trl") | null |
20,941 | import importlib.util
from typing import Any, Tuple, Union
import torch
from packaging import version
from torch4keras.snippets.import_utils import is_package_available
import sys
def is_sseclient_available():
return importlib.util.find_spec("sseclient") | null |
20,942 | import importlib.util
from typing import Any, Tuple, Union
import torch
from packaging import version
from torch4keras.snippets.import_utils import is_package_available
import sys
def is_streamlit_available():
return is_package_available('streamlit') | null |
20,943 | from contextlib import contextmanager
import torch
from torch import nn
import os
def parse_flag_from_env(key, default=False):
"""Returns truthy value for `key` from the env if available else the default."""
value = os.environ.get(key, str(default))
return str_to_bool(value) == 1 # As its name indicates `s... | A context manager under which models are initialized with all parameters on the meta device, therefore creating an empty model. Useful when just initializing the model would blow the available RAM. Args: include_buffers (`bool`, *optional*): Whether or not to also put all buffers on the meta device while initializing. ... |
20,944 | import json
import torch
import gc
import inspect
from torch4keras.snippets import *
from torch.utils.checkpoint import CheckpointFunction
import shutil
import re
from pathlib import Path
The provided code snippet includes necessary dependencies for implementing the `insert_arguments` function. Write a Python function... | 装饰器，为类方法增加参数（主要用于类的__init__方法） |
20,945 | import json
import torch
import gc
import inspect
from torch4keras.snippets import *
from torch.utils.checkpoint import CheckpointFunction
import shutil
import re
from pathlib import Path
The provided code snippet includes necessary dependencies for implementing the `delete_arguments` function. Write a Python function... | 装饰器，为类方法删除参数（主要用于类的__init__方法） |
20,946 | import json
import torch
import gc
import inspect
from torch4keras.snippets import *
from torch.utils.checkpoint import CheckpointFunction
import shutil
import re
from pathlib import Path
The provided code snippet includes necessary dependencies for implementing the `cal_ts_num` function. Write a Python function `def ... | 查看某个tensor在gc中的数量 |
20,947 | import json
import torch
import gc
import inspect
from torch4keras.snippets import *
from torch.utils.checkpoint import CheckpointFunction
import shutil
import re
from pathlib import Path
The provided code snippet includes necessary dependencies for implementing the `get_state_dict_dtype` function. Write a Python func... | Returns the first found floating dtype in `state_dict` if there is one, otherwise returns the first dtype. |
20,948 | import json
import torch
import gc
import inspect
from torch4keras.snippets import *
from torch.utils.checkpoint import CheckpointFunction
import shutil
import re
from pathlib import Path
The provided code snippet includes necessary dependencies for implementing the `set_default_torch_dtype` function. Write a Python f... | 设置默认权重类型 |
20,949 | import json
import torch
import gc
import inspect
from torch4keras.snippets import *
from torch.utils.checkpoint import CheckpointFunction
import shutil
import re
from pathlib import Path
The provided code snippet includes necessary dependencies for implementing the `load_state_dict_into_meta_model` function. Write a ... | 把state_dict导入meta_model 为了代码简洁，这里device_map需要外部手动指定, 形式如{'embeddings.word_embeddings': 0, 'LayerNormFinal': 0, 'lm_head': 0} |
20,950 | import json
import torch
import gc
import inspect
from torch4keras.snippets import *
from torch.utils.checkpoint import CheckpointFunction
import shutil
import re
from pathlib import Path
The provided code snippet includes necessary dependencies for implementing the `old_checkpoint` function. Write a Python function `... | 兼容torch<1.11.0时仅允许输入输出是位置参数 通过闭包来对返回参数进行控制 |
20,951 | import json
import torch
import gc
import inspect
from torch4keras.snippets import *
from torch.utils.checkpoint import CheckpointFunction
import shutil
import re
from pathlib import Path
The provided code snippet includes necessary dependencies for implementing the `cuda_empty_cache` function. Write a Python function... | 清理gpu显存 |
20,952 | import json
import torch
import gc
import inspect
from torch4keras.snippets import *
from torch.utils.checkpoint import CheckpointFunction
import shutil
import re
from pathlib import Path
The provided code snippet includes necessary dependencies for implementing the `modify_variable_mapping` function. Write a Python f... | 对variable_mapping的返回值（字典）进行修改 |
20,953 | import json
import torch
import gc
import inspect
from torch4keras.snippets import *
from torch.utils.checkpoint import CheckpointFunction
import shutil
import re
from pathlib import Path
if os.environ.get('SAFETENSORS_FIRST', False):
SAFETENSORS_BINS = ['.safetensors', '.bin'] # 优先查找safetensors格式权重
else:
SAFE... | 从一个文件夹copy到另一个文件夹 :param src: str, copy from src :param dst: str, copy to dst |
20,954 | import json
import torch
import gc
import inspect
from torch4keras.snippets import *
from torch.utils.checkpoint import CheckpointFunction
import shutil
import re
from pathlib import Path
if os.environ.get('SAFETENSORS_FIRST', False):
SAFETENSORS_BINS = ['.safetensors', '.bin'] # 优先查找safetensors格式权重
else:
SAFE... | 获取local文件夹下的config文件路径 1. model_name: 从hf下载 2. local_file且config_path为None: 重新在local_file所在目录找对应的config_path 3. local_dir且config_path为None: 重新在local_dir找对应的config_path |
20,955 | import json
import torch
import gc
import inspect
from torch4keras.snippets import *
from torch.utils.checkpoint import CheckpointFunction
import shutil
import re
from pathlib import Path
if os.environ.get('SAFETENSORS_FIRST', False):
SAFETENSORS_BINS = ['.safetensors', '.bin'] # 优先查找safetensors格式权重
else:
SAFE... | 获取该local文件夹下的ckpt文件、文件列表 1. model_name: 从hf下载 2. local_file且config_path为None: 重新在local_file所在目录找对应的config_path 3. local_dir且config_path为None: 重新在local_dir找对应的config_path |
20,956 | import unicodedata
import six
import numpy as np
import re
import torch
from torch.nn.utils.rnn import pad_sequence
from torch4keras.snippets import *
import random
The provided code snippet includes necessary dependencies for implementing the `is_string` function. Write a Python function `def is_string(s)` to solve t... | 判断是否是字符串 |
20,957 | import unicodedata
import six
import numpy as np
import re
import torch
from torch.nn.utils.rnn import pad_sequence
from torch4keras.snippets import *
import random
The provided code snippet includes necessary dependencies for implementing the `truncate_sequences` function. Write a Python function `def truncate_sequen... | 截断总长度至不超过maxlen |
20,958 | import unicodedata
import six
import numpy as np
import re
import torch
from torch.nn.utils.rnn import pad_sequence
from torch4keras.snippets import *
import random
The provided code snippet includes necessary dependencies for implementing the `text_segmentate` function. Write a Python function `def text_segmentate(te... | 将文本按照标点符号划分为若干个短句 :param text: 待划分的句子 :param maxlen: int, 截断长度 :param seps: 分隔符 :param strips: ''.strip() :param truncate: True表示标点符号切分后仍然超长时, 按照maxlen硬截断分成若干个短句 :return: List[str], 划分后的句子列表 |
20,959 | import unicodedata
import six
import numpy as np
import re
import torch
from torch.nn.utils.rnn import pad_sequence
from torch4keras.snippets import *
import random
The provided code snippet includes necessary dependencies for implementing the `merge_segmentate` function. Write a Python function `def merge_segmentate(... | 把m个句子合并成不超过maxlen的n个句子, 主要用途是合并碎句子 :param sequences: List(str), 短句子列表 :param maxlen: int, 最大长度 :param sep: str, 合并使用的分隔符, 可以是，。等标点符号 |
20,960 | import unicodedata
import six
import numpy as np
import re
import torch
from torch.nn.utils.rnn import pad_sequence
from torch4keras.snippets import *
import random
The provided code snippet includes necessary dependencies for implementing the `text_augmentation` function. Write a Python function `def text_augmentatio... | 简单的EDA策略, 增删改 :param texts: 需要增强的文本/文本list :param noise_dict: 噪音数据, 元素为str的list, tuple, set :param noise_len: 噪音长度, 优先试用 :param noise_p: 噪音比例 :param skip_words: 跳过的短语, string/list :param strategy: 修改的策略, 包含增insert, 删delete, 改replace, 随机random :param allow_dup: 是否允许同一个位置多次EDA |
20,961 | import unicodedata
import six
import numpy as np
import re
import torch
from torch.nn.utils.rnn import pad_sequence
from torch4keras.snippets import *
import random
is_py2 = six.PY2
if not is_py2:
basestring = str
The provided code snippet includes necessary dependencies for implementing the `lowercase_and_normali... | 转小写，并进行简单的标准化 |
20,962 | import unicodedata
import six
import numpy as np
import re
import torch
from torch.nn.utils.rnn import pad_sequence
from torch4keras.snippets import *
import random
The provided code snippet includes necessary dependencies for implementing the `sequence_padding` function. Write a Python function `def sequence_padding(... | 将序列padding到同一长度 |
20,963 | import unicodedata
import six
import numpy as np
import re
import torch
from torch.nn.utils.rnn import pad_sequence
from torch4keras.snippets import *
import random
def parallel_apply_generator(func, iterable, workers, max_queue_size, dummy=False, random_seeds=True):
"""多进程或多线程地将func应用到iterable的每个元素中（直接从bert4keras中... | 多进程或多线程地将func应用到iterable的每个元素中（直接从bert4keras中移植过来）。 注意这个apply是异步且无序的，也就是说依次输入a,b,c，但是输出可能是func(c), func(a), func(b)。 :param callback: 处理单个输出的回调函数； :param dummy: False是多进程/线性，True则是多线程/线性；windows需设置dummy=True :param random_seeds: 每个进程的随机种子； :param unordered: 若为False，则按照输入顺序返回，仅当callback为None时生效。 |
20,964 | import unicodedata
import six
import numpy as np
import re
import torch
from torch.nn.utils.rnn import pad_sequence
from torch4keras.snippets import *
import random
The provided code snippet includes necessary dependencies for implementing the `get_pool_emb` function. Write a Python function `def get_pool_emb(hidden_s... | 获取句向量 :param hidden_state: torch.Tensor/List(torch.Tensor)，last_hidden_state/all_encoded_layers :param pooled_output: torch.Tensor, bert的pool_output输出 :param attention_mask: torch.Tensor :param pool_strategy: str, ('cls', 'last-avg', 'mean', 'last-max', 'max', 'first-last-avg', 'custom') :param custom_layer: int/List[i... |
20,965 | import unicodedata
import six
import numpy as np
import re
import torch
from torch.nn.utils.rnn import pad_sequence
from torch4keras.snippets import *
import random
The provided code snippet includes necessary dependencies for implementing the `create_position_ids_start_at_padding` function. Write a Python function `d... | 生成padding_ids, 从padding_idx+1开始。忽略填充符号 |
20,966 | import unicodedata
import six
import numpy as np
import re
import torch
from torch.nn.utils.rnn import pad_sequence
from torch4keras.snippets import *
import random
def entity_extract_rule(text:str, pattern:str=None, label:str=None, start:int=0, end:int=-1, dotall:bool=True,
replace_pattern:Optional[Union[... | 按照预设的正则规则来解析实体, 允许占位符 |
20,967 | import torch
import torch.nn as nn
import numpy as np
from bert4torch.activations import get_activation
from bert4torch.layers.core import LayerNorm
import random
import warnings
import math
class BottleneckAdapterLayer(nn.Module):
'''BottleneckAdapterLayer'''
def __init__(self, adapter_input_size, bottleneck_s... | null |
20,968 | from torch import nn
import torch
import math
import torch.nn.functional as F
from typing import Union, List
The provided code snippet includes necessary dependencies for implementing the `get_sinusoid_encoding_table` function. Write a Python function `def get_sinusoid_encoding_table(n_position, d_hid, base=10000.0, n... | sinusoid编码 :param n_position: int, 位置长度 :param d_hid: int, 位置编码长度 :param padding_idx: padding的token_ids :param ntk_alpha: int, 要扩展的倍数 :param rope_ratio: int, chatglm中32k的插值 :return: [seq_len, d_hid] |
20,969 | from __future__ import absolute_import, division, print_function, unicode_literals
import collections
import logging
from typing import Any, Literal
import unicodedata
from io import open
from bert4torch.snippets import truncate_sequences, is_string, lowercase_and_normalize, sequence_padding
import re
import six
from c... | 加载词典文件到dict |
20,970 | from __future__ import absolute_import, division, print_function, unicode_literals
import collections
import logging
from typing import Any, Literal
import unicodedata
from io import open
from bert4torch.snippets import truncate_sequences, is_string, lowercase_and_normalize, sequence_padding
import re
import six
from c... | 去除文本中的空白符 |
20,971 | from __future__ import absolute_import, division, print_function, unicode_literals
import collections
import logging
from typing import Any, Literal
import unicodedata
from io import open
from bert4torch.snippets import truncate_sequences, is_string, lowercase_and_normalize, sequence_padding
import re
import six
from c... | Checks whether `chars` is a whitespace character. |
20,972 | from __future__ import absolute_import, division, print_function, unicode_literals
import collections
import logging
from typing import Any, Literal
import unicodedata
from io import open
from bert4torch.snippets import truncate_sequences, is_string, lowercase_and_normalize, sequence_padding
import re
import six
from c... | Checks whether `chars` is a control character. |
20,973 | from __future__ import absolute_import, division, print_function, unicode_literals
import collections
import logging
from typing import Any, Literal
import unicodedata
from io import open
from bert4torch.snippets import truncate_sequences, is_string, lowercase_and_normalize, sequence_padding
import re
import six
from c... | Checks whether `chars` is a punctuation character. |
20,974 | from __future__ import absolute_import, division, print_function, unicode_literals
import collections
import logging
from typing import Any, Literal
import unicodedata
from io import open
from bert4torch.snippets import truncate_sequences, is_string, lowercase_and_normalize, sequence_padding
import re
import six
from c... | Converts `text` to Unicode (if it's not already), assuming utf-8 input. |
20,975 | import math
import torch
from torch import nn
from packaging import version
The provided code snippet includes necessary dependencies for implementing the `_gelu_python` function. Write a Python function `def _gelu_python(x)` to solve the following problem:
Original Implementation of the GELU activation function in Go... | Original Implementation of the GELU activation function in Google BERT repo when initially created. For information: OpenAI GPT's GELU is slightly different (and gives slightly different results): 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3)))) This is now written in C in nn.functi... |
20,976 | import math
import torch
from torch import nn
from packaging import version
The provided code snippet includes necessary dependencies for implementing the `_gelu_new` function. Write a Python function `def _gelu_new(x)` to solve the following problem:
Implementation of the GELU activation function currently in Google ... | Implementation of the GELU activation function currently in Google BERT repo (identical to OpenAI GPT). Also see the Gaussian Error Linear Units paper: https://arxiv.org/abs/1606.08415 |
20,977 | import math
import torch
from torch import nn
from packaging import version
def gelu_fast(x):
return 0.5 * x * (1.0 + torch.tanh(x * 0.7978845608 * (1.0 + 0.044715 * x * x))) | null |
20,978 | import math
import torch
from torch import nn
from packaging import version
def quick_gelu(x):
return x * torch.sigmoid(1.702 * x) | null |
20,979 | import math
import torch
from torch import nn
from packaging import version
The provided code snippet includes necessary dependencies for implementing the `_silu_python` function. Write a Python function `def _silu_python(x)` to solve the following problem:
See Gaussian Error Linear Units (Hendrycks et al., https://ar... | See Gaussian Error Linear Units (Hendrycks et al., https://arxiv.org/abs/1606.08415) where the SiLU (Sigmoid Linear Unit) was originally introduced and coined, and see Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning (Elfwing et al., https://arxiv.org/abs/1702.03118) and... |
20,980 | import math
import torch
from torch import nn
from packaging import version
The provided code snippet includes necessary dependencies for implementing the `_mish_python` function. Write a Python function `def _mish_python(x)` to solve the following problem:
See Mish: A Self-Regularized Non-Monotonic Activation Functio... | See Mish: A Self-Regularized Non-Monotonic Activation Function (Misra., https://arxiv.org/abs/1908.08681). Also visit the official repository for the paper: https://github.com/digantamisra98/Mish |
20,981 | import math
import torch
from torch import nn
from packaging import version
def linear_act(x):
return x | null |
20,982 | import math
import torch
from torch import nn
from packaging import version
def swiglu(x, dim=-1):
x = torch.chunk(x, 2, dim=dim)
return silu(x[0]) * x[1] | null |
20,983 | import math
import torch
from torch import nn
from packaging import version
ACT2FN = {
"relu": nn.functional.relu,
"silu": silu,
"swish": silu,
"swiglu": swiglu,
"gelu": gelu,
"tanh": torch.tanh,
"gelu_new": _gelu_new,
"gelu_fast": gelu_fast,
"quick_gelu": quick_gelu,
"mish": mis... | 根据activation_string返回对应的激活函数 :param activation_string: str, 传入的激活函数名 :return: Any |
20,984 | import numpy as np
import math
import os
import torch
from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model
from bert4torch.snippets import sequence_padding
import re
The provided code snippet includes necessary dependencies for implementing the `get_bool_ids_greater_than` f... | 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 ... |
20,985 | import numpy as np
import math
import os
import torch
from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model
from bert4torch.snippets import sequence_padding
import re
The provided code snippet includes necessary dependencies for implementing the `get_span` function. Write a ... | 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... |
20,986 | import numpy as np
import math
import os
import torch
from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model
from bert4torch.snippets import sequence_padding
import re
def get_id_and_prob(spans, offset_map):
prompt_length = 0
for i in range(1, len(offset_map)):
... | null |
20,987 | import numpy as np
import math
import os
import torch
from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model
from bert4torch.snippets import sequence_padding
import re
The provided code snippet includes necessary dependencies for implementing the `cut_chinese_sent` function. ... | Cut the Chinese sentences more precisely, reference to "https://blog.csdn.net/blmoistawinde/article/details/82379256". |
20,988 | import numpy as np
import math
import os
import torch
from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model
from bert4torch.snippets import sequence_padding
import re
def dbc2sbc(s):
rs = ""
for char in s:
code = ord(char)
if code == 0x3000:
... | null |
20,989 | import time
import json
import requests
from contextlib import asynccontextmanager
from typing import Any, Dict, List, Literal, Optional, Union
from bert4torch.snippets import log_info, log_warn, cuda_empty_cache, AnyClass
from bert4torch.snippets import is_fastapi_available, is_pydantic_available, is_sseclient_availab... | null |
20,990 | import time
import json
import requests
from contextlib import asynccontextmanager
from typing import Any, Dict, List, Literal, Optional, Union
from bert4torch.snippets import log_info, log_warn, cuda_empty_cache, AnyClass
from bert4torch.snippets import is_fastapi_available, is_pydantic_available, is_sseclient_availab... | 添加ChatWebDemo |
20,991 | import os
import torch
from typing import Union, Optional
from bert4torch.models import build_transformer_model
from bert4torch.snippets import log_warn_once, cuda_empty_cache, is_streamlit_available, log_info
from packaging import version
import gc
class ChatCli(Chat):
'''在命令行中交互的demo'''
def __init__(self, *ar... | 添加ChatCliDemo |
20,992 | import os
import torch
from typing import Union, Optional
from bert4torch.models import build_transformer_model
from bert4torch.snippets import log_warn_once, cuda_empty_cache, is_streamlit_available, log_info
from packaging import version
import gc
class ChatWebGradio(Chat):
'''gradio实现的网页交互的demo
默认是stream输出，默... | 添加ChatWebDemo |
20,993 | import os
import torch
from typing import Union, Optional
from bert4torch.models import build_transformer_model
from bert4torch.snippets import log_warn_once, cuda_empty_cache, is_streamlit_available, log_info
from packaging import version
import gc
class ChatWebStreamlit(Chat):
def __init__(self, *args, max_length... | 添加ChatWebDemo |
20,994 | from torch.nn import Linear, Embedding
from torch.nn.parameter import Parameter
import torch.nn.functional as F
import bz2
import torch
import base64
import ctypes
from typing import List
import re
from tqdm import tqdm
from functools import partial
import inspect
from bert4torch.snippets import log_error
def compress... | null |
20,995 | from torch.nn import Linear, Embedding
from torch.nn.parameter import Parameter
import torch.nn.functional as F
import bz2
import torch
import base64
import ctypes
from typing import List
import re
from tqdm import tqdm
from functools import partial
import inspect
from bert4torch.snippets import log_error
def extract_... | null |
20,996 | from torch.nn import Linear, Embedding
from torch.nn.parameter import Parameter
import torch.nn.functional as F
import bz2
import torch
import base64
import ctypes
from typing import List
import re
from tqdm import tqdm
from functools import partial
import inspect
from bert4torch.snippets import log_error
class CacheTe... | 从chagglm-6b移植过来的的量化，方便以int8和int4进行推理 源链接：https://huggingface.co/THUDM/chatglm-6b/blob/main/quantization.py Replace fp16 linear with quantized linear 这里修改了hard code, 可以适配其他模型 target_modules: str/list, 指定对某些层做量化 |
20,997 | from torch.nn import Linear, Embedding
from torch.nn.parameter import Parameter
import torch.nn.functional as F
import bz2
import torch
import base64
import ctypes
from typing import List
import re
from tqdm import tqdm
from functools import partial
import inspect
from bert4torch.snippets import log_error
The provided... | transformer的load_in_8bit, 源自transformer源代码 |
20,998 | from torch.optim.lr_scheduler import LambdaLR
from torch.optim.optimizer import Optimizer
import torch
The provided code snippet includes necessary dependencies for implementing the `get_linear_schedule_with_warmup` function. Write a Python function `def get_linear_schedule_with_warmup(optimizer, num_warmup_steps, num... | 带warmup的schedule, 源自transformers包optimization.py中 :param num_warmup_steps: 需要warmup的步数, 一般为 num_training_steps * warmup_proportion(warmup的比例, 建议0.05-0.15) :param num_training_steps: 总的训练步数, 一般为 train_batches * num_epoch |
20,999 | from torch.optim.lr_scheduler import LambdaLR
from torch.optim.optimizer import Optimizer
import torch
The provided code snippet includes necessary dependencies for implementing the `extend_with_exponential_moving_average` function. Write a Python function `def extend_with_exponential_moving_average(model, decay=0.999... | 模型权重的指数滑动平均, 不参加梯度更新，只是记录滑动平均的参数，给预测使用 注意区别于类似adam一类的自适应学习率优化器, 针对一阶二阶梯度的指数滑动平均, 两者完全不同 Example: >>> # 初始化 >>> ema = ExponentialMovingAverage(model, 0.999) >>> # 训练过程中, 更新完参数后, 同步update ema_weights weights >>> def train(): >>> optimizer.step() >>> ema.step() >>> # eval前, 调用apply_ema_weights(); eval之后, restore_raw_weigh... |
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