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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.


Getting started with NLP: Word Embeddings, Glove and Classification

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In this blog post we are going to explain the concepts and use of word embeddings in NLP, using Glove as en example. Then we will try to apply the pre-trained Glove word embeddings to solve a text classification problem using this technique. And as in others notebook we will follow the notebook from the great course on NLP by LazyProgrammer "Natural Language Processing in Python". In my personal blog you can find a blog post or notebook with the text and code in this post. If you only want to check for the code this notebook is a better option.


Innovative Chatbot using 1-Dimensional Convolutional Layers

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We will be building an innovative chatbot with the use of one-dimensional convolutional layers. This method allows us to maximize or minimize the intensity of a particular set of values. This is best for the text data where we will be prioritizing a bunch of selective text data. We will be using a "text classification" based approach for building our chatbot. To understand more about 1-D convolution layers, we can refer to them as a layer that creates a convolution kernel that is convolved with the layer input over a single spatial (or temporal) dimension to produce a tensor of outputs.


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.


NLP Classification with Universal Language Model Fine-tuning (ULMFiT)

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Text classification is one of the important applications of NLP. Applications such as Sentiment Analysis and Identifying spam, bots, and offensive comments come under Text Classification. Until now, the approaches used for solving these problems included building Machine Learning or Deep Learning models from scratch, training them on your text data, and fine-tuning it with hyperparameters. Even though such models give decent results for applications like classifying whether a movie review is positive or negative, they may perform terribly if things become more ambiguous because most of the time there's just not enough amount of labeled data to learn from. Isn't the Imagenet using the same approach to classify the images?


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.


Natural Language Processing (NLP) with Python: 2020

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