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Text Classification


Text Classification with NO model training

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NLP (Natural Language Processing) is the field of artificial intelligence that studies the interactions between computers and human languages, in particular how to program computers to process and analyze large amounts of natural language data. NLP is often applied for classifying text data. Text classification is the problem of assigning categories to text data according to its content. In order to carry out a classification use case, you need a labeled dataset for machine learning models training. So what happens if you don't have one?


How Document Classification Can Improve Business Processes

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The process of labeling documents into categories based on the type of the content is known as document classification. It can also be defined as the process of assigning one or more classes or categories to a document (depending on the type of content) to make it easy to sort and manage images, texts, and videos. Document classification can be done using artificial intelligence, machine learning, and python. This classification can be done in two ways: manually or automatically. The former gives humans full authority over the classification.


Tutorial On Keras Tokenizer For Text Classification in NLP

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Now we will compile the model using optimizer as stochastic gradient descent, loss as cross-entropy and metrics to measure the performance would be accuracy. After compiling we will train the model and check the performance on validation data. We are taking a batch size of 64 and epochs to be 10.


Keras documentation: Text classification from scratch

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Authors: Mark Omernick, Francois Chollet Date created: 2019/11/06 Last modified: 2020/05/17 Description: Text sentiment classification starting from raw text files. This example shows how to do text classification starting from raw text (as a set of text files on disk). We demonstrate the workflow on the IMDB sentiment classification dataset (unprocessed version). We use the TextVectorization layer for word splitting & indexing. Let's download the data and inspect its structure.


Text Classification with Hugging Face Transformers in TensorFlow 2 (Without Tears)

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The Hugging Face transformers package is an immensely popular Python library providing pretrained models that are extraordinarily useful for a variety of natural language processing (NLP) tasks. It previously supported only PyTorch, but, as of late 2019, TensorFlow 2 is supported as well. While the library can be used for many tasks from Natural Language Inference (NLI) to Question-Answering, text classification remains one of the most popular and practical use cases. The ktrain library is a lightweight wrapper for tf.keras in TensorFlow 2. It is designed to make deep learning and AI more accessible and easier to apply for beginners and domain experts. As of version 0.8, ktrain now includes a simplified interface to Hugging Face transformers for text classification.


Text Classification with Simple Transformers

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Using Transformer models has never been simpler! Yes that's what Simple Transformers author Thilina Rajapakse says and I agree with him so should you. You might have seen lengthy code with hundreds of lines to implement transformers models such as BERT, RoBERTa, etc. Once you understand how to use Simple Transformers you will know how easy and simple it is to use transformer models. TheSimple Transformers library is built on top of Hugging Face Transformers library. Hugging Face Transformers provides state-of-the-art general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet, T5, etc.) for Natural Language Understanding (NLU) and Natural Language Generation (NLG) and provides more than thousand pre-trained models and covers around 100 languages.


Box adds automated classification to content security product, Box Shield

ZDNet

Box on Tuesday started rolling out a new automated classification feature for Box Shield, its popular content security product. The new feature uses machine learning to automatically scan files as they're uploaded or edited in Box and apply classification labels. Box stressed that the feature should better help organizations meet compliance needs, even as employees work remotely through the COVID-19 pandemic. "Remote work has accelerated cloud adoption as businesses seek enable a distributed workforce and serve their customers digitally," Box CISO Lakshmi Hanspal said in a statement. "This requires completely new approach to security and privacy. As more work is done outside office boundaries on both managed and personal devices it is critical to have one source of truth for all of your data in order to meet new regulatory and compliance standards without slowing down business."


Text Classification with NLP: Tf-Idf vs Word2Vec vs BERT

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In this article, using NLP and Python, I will explain 3 different strategies for text multiclass classification: the old-fashioned Bag-of-Words (with Tf-Idf), the famous Word Embedding (with Word2Vec), and the cutting edge Language models (with BERT). NLP (Natural Language Processing) is the field of artificial intelligence that studies the interactions between computers and human languages, in particular how to program computers to process and analyze large amounts of natural language data. NLP is often applied for classifying text data. Text classification is the problem of assigning categories to text data according to its content. There are different techniques to extract information from raw text data and use it to train a classification model.


Text Classification using Neural Networks

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Understanding how chatbots work is important. A fundamental piece of machinery inside a chat-bot is the text classifier. Let's look at the inner workings of an artificial neural network (ANN) for text classification.


Token Manipulation Generative Adversarial Network for Text Generation

arXiv.org Artificial Intelligence

MaskGAN opens the query for the conditional language model by filling in the blanks between the given tokens. In this paper, we focus on addressing the limitations caused by having to specify blanks to be filled. We decompose conditional text generation problem into two tasks, make-a-blank and fill-in-the-blank, and extend the former to handle more complex manipulations on the given tokens. We cast these tasks as a hierarchical multi agent RL problem and introduce a conditional adversarial learning that allows the agents to reach a goal, producing realistic texts, in cooperative setting. We show that the proposed model not only addresses the limitations but also provides good results without compromising the performance in terms of quality and diversity.