2023年12月19日 · The cats of NLP research! Feline friends hard at work in the lab, or emotional support during CUDA OOM errors. Ragdolls who love the RSA model, Persians who model phrase structure, and tabbies who train transformers! Add your cat: fill out our Google Form!

Learn word embeddings jointly with the main task you care about (such as document classification or sentiment prediction). In this setup, you start with random word vectors and then learn word vectors in the same way you learn the weights of a neural network.

2021年3月26日 · Using NMF with 2 topics we also got a clear theme of cats vs dogs. It was very interesting to see because the generic theme of cats and dogs came up. Dogs displayed a more active image than...

2021年3月29日 · 本次的 NLP （Natural Language Processing）任务是利用深度学习中的 卷积神经网络 （CNN）和 循环神经网络 （RNN）来对文本的情感进行分类。 数据集传送门. 训练集共有15万余项，语言为英文，情感分为 0~4 共五种情感。 例子： 输入： A series of escapades demonstrating the adage that what is good for the goose is also good for the gander , some of which occasionally amuses but none of which amounts to much of a story .

2024年8月22日 · Natural Language Processing (NLP) stands at the forefront of this effort, bridging the gap between human communication and machine understanding. In this article, we’ll look into the top 10 techniques used in NLP, offering clear explanations, practical applications, and even some code snippets to help you get started.

2017年10月29日 · Classifying images into cats and dogs is a very popular application of deep learning, but what about their names? Can you tell from the name of a pet whether it is a cat or a dog? To some extent, yes. For example, a pet named “Mr. Tiddles” or “Garfield” is very likely to be a cat, whereas “Rex”, “Rover” or “Fido” is probably ...

2024年2月29日 · In this article, I will demonstrate my process for classifying cats and dogs using Convolutional Neural Network or CNN. I will share my experiences in experimenting with various techniques, such...

2020年10月5日 · Abstract: NLP models are shown to suffer from robustness issues, i.e., a model's prediction can be easily changed under small perturbations to the input. In this work, we present a Controlled Adversarial Text Generation (CAT-Gen) model that, given an input text, generates adversarial texts through controllable attributes that are known to be ...

Messages['NLP_Result'] = Messages['Body'].apply(lambda x: nlp(x)._.cats) 这里，NLP_Result列中的每个值将被赋给x变量。 nlp(x)将创建一个NLP对象，其中包含您要访问的必要属性。然后，nlp(x)._.cats将返回预期值。

Opennlp classificator, which suggests categories from issue title/description - abcdw/nlp-cats