解决了数据的问题,下面我们正式搭建 N-Gram 模型:NGramLanguageModeler。 模型继承自 torch.nn.Module,我们之前讲过,用于搭建神经网络的组件都可以从 torch.nn.Module 中继承,在构建神经网络时,我们需要重写它们的 forward() 方法。 class NGramLanguageModeler(nn.Module): # 初始化时需要指定:单词表大小、想要嵌...
大语言模型框架Python ngram语言模型python 一、概述 对于语音识别来说,大体上就分为三个方面,一个是声学模型(acoustical model)的训练,一个是语言模型(language model)的训练,最后就是对给定一段语音的解码了,当然,咱们今天讨论的是第二部分,其他的就先丢到一边吧!(在这给大家打一打气,其实语言模型是这三个方...
计算下一个单词以及对应的概率user_input="I like "predict(model,user_input)根据提供的语料库，通过N...
通过安装 kenlm 的 python sdk 后我们就可以使用了。 import kenlm strings = ["蒙牛纯牛奶", "蒙牛咖啡奶", "蒙牛存牛奶"] kn_model = kenlm.Model('language_model_char.klm') for i in range(len(strings)): print("query: ", strings[i]) print("ngram_score: ", kn_model.score(strings[i]...
Finally, it also demonstrates an effective use case of this interface by showing how to leverage it to build a Python language model server. Such a server can prove to be extremely useful when the language model needs to be queried by multiple clients over a network: the language model must...
Here is the Python implementation for a bigram language model using Laplace smoothing: ` ` ` Suppose the text A has been tokenized, and special symbols and are added. Each sentence is represented by a list of words. Vocab is the list of all words, and K is the parameter for Laplace...
N-Gram划分Python实现 将一句话按照bi-gram的方式进行划分,代码如下: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 defcreate_ngram(input_list,n):#input_list为待划分的文本 #n为长度 ngram_list=[]iflen(input_list)<=n:ngram_list.append(input_list)else:fortmpinzip(*[input_list[i:]fori...
本文主要介绍n-gram语言模型,如果想要了解语言模型的相关知识可以看《带你理解语言模型》。 ▲参数的数量 代码语言:javascript 代码运行次数:0 运行 AI代码解释 商品 和 服务 商品 和服 物美价廉 服务 和 货币 代码语言:javascript 代码运行次数:0 运行
python 3.6.9 docker-ce > 19.03.5 docker-API 1.40 nvidia-container-toolkit > 1.3.0-1 nvidia-container-runtime > 3.4.0-1 nvidia-docker2 > 2.5.0-1 nvidia-driver >= 455.23 Note: A compatible NVIDIA GPU would be required. Installation ...
The Python code also writes out the n-gram probabilities to disk into thedev/folder, which you can then inspect with the attached Jupyter notebookdev/visualize_probs.ipynb. The C model is identical in functionality but skips the cross-validation. Instead, it hardcodesn=4, smoothing=0.01, ...