Experiments on\nthree real-world datasets show that the proposed framework boosts the\nrobustness of pre-trained models by a large margin. We believe that this work\ncan greatly promote the application of NLP models in actual scenarios, although\nthe algorithm we use is simple and ...
For the time being, don’t worry about stemming and lemmatization but treat them as steps for textual data cleaning using NLP (Natural language processing). We will discuss stemming and lemmatization later in the tutorial. Tasks such asText classification or spam filteringmakes use of NLP along ...
NLP has a wide range of uses, and of the most common use cases is Text Classification. The classification of text into different categories automatically is known as text classification. The detection of spam or ham in an email and the categorization of news articles are ...
Capabilities provided by caikit-nlp: TaskModule(s)Salient Feature(s) TextGenerationTask 1. PeftPromptTuning 2. TextGeneration 1. Prompt Tuning, Multi-task Prompt tuning 2. Fine-tuning Both modules above provide optimized inference capability using Text Generation Inference Server TextClassificationTask...
Applications of text classification Exploring Naïve Bayes Learning Bayes' theorem by examples The mechanics of Naïve Bayes Implementing Naïve Bayes from scratch Implementing Naïve Bayes with scikit-learn Classification performance evaluation Model tuning and cross-validation Summary Exercise Classifying...
一、什么是seq2seq,以及它和Attention机制有什么关系 seq2seq是一种NLP常见的框架——这种框架不要求输入和输出序列是维度是一样的。许多NLP task的输入输出维度不统一,比如机器翻译、图像的图注生成、摘要生成、自动问答等。seq2seq框架大多包含encoder和decoder。 Attention机制只是一种思想——即,人在...Seq...
This book is a deep dive into the exciting world of machine learning. What's unique about this book is the clarity with which it explains concepts from first principles and teaches by example in a way that is accessible to a wide audience. You will learn how to implement key algorithms fr...
Our objective is to predict the sentiment (either positive or negative) of a blob of English text using machine learning. We sometimes refer to this type of ML as Natural Language Processing (NLP) because it involves machines making sense of language. The dataset provided to us contains 25,...
human operators, because it is part of the Bayes error which is irreducible for a given classification problem. On the other hand, the approaches we review in the paper are for the estimation error, which measures how far the learned networkNis from the best network of the same architecture....
Using Python-based text mining tools, the comments were processed to extract significant keywords and sentiments. A combination of natural language processing (NLP) techniques, including Jieba word segmentation and TF-IDF (Term Frequency-Inverse Document Frequency) analysis, was employed to extract key...