math="0" shadow="0"> <root> <mxCell id="0" /> <mxCell id="1" parent="0" /> <mxCell id="iItHXJfjoVKmwTf7Rtns-1" value="" style="shape=image;verticalLabelPosition=bottom;labelBackgroundColor=default;verticalAlign=top;aspect=fixed;imageAspect=0;image...
import math import os from collections import defaultdict from pathlib import Path from typing import Dict, List, Optional, Tuple import k2 import numpy as np import sentencepiece as spm import torch import torch.nn as nn import torch.nn.functional as F from at_datamodule import AudioSetATDatamo...
- @[Zarper](https://oncemath.com)的[保研推免经验分享 - 数学系跨保 CS](https://oncemath.com/share/my-postgraduate-share/) - @[lhw](https://www.zhihu.com/people/lhw-55/posts)的[211物联网工程保研中国科学技术大学cs自然语言处理方向](https://zhuanlan.zhihu.com/p/60553247) - @[菜得...
I'm currently in the process of writing an initial code for the stm32 based multi-protocol module. I've set up and radio and everything, I just have a hard time to figure out the CRC algorithm they use. It looks like that last two bytes are CRC (I might be wrong though), I've...
import math tokenizer, model = oagbert("oagbert-v2-zh") model.eval() title = '基于随机化矩阵分解的网络嵌入方法' abstract = '''随着互联网的普及,越来越多的问题以社交网络这样的网络形式出现.网络通常用图数据表示,由于图数据处理的挑战性,如何从图中学习到重要的信息是当前被广泛关注的问题.网络嵌入...
import math import os from collections import defaultdict from pathlib import Path from typing import Dict, List, Optional, Tuple import k2 import numpy as np import sentencepiece as spm import torch import torch.nn as nn import torch.nn.functional as F from at_datamodule import AudioSetATDatamo...
padding_value=math.log(1e-10), ) feature_lengths = torch.tensor(feature_lengths, device=device) bpe_model = spm.SentencePieceProcessor() bpe_model.load(str(params.lang_dir / "bpe.model")) mmi_graph_compiler = MmiTrainingGraphCompiler( params.lang_dir, uniq_filename="lexicon.txt", device...