How to Build Sustom Datasets for Text in Pytorch

In this video we go through a bit more in depth into custom datasets and implement more advanced functions for dealing with text. Specifically we’re looking at a image captioning dataset (Flickr8k data set) with an image and a corresponding caption text that describes what’s going on in the image. I think the general principles from this video can be utilized to any project you’re working with when dealing with text data be it either translation, question answering, sentiment analysis etc. I also recommend taking a look at my Torchtext which can also be quite helpful and simplify the data loading process.

Flickr8k Dataset used in the video:

https://www.kaggle.com/dataset/e1cd22…

Github repository:

https://github.com/AladdinPerzon/Mach…

OUTLINE:

0:00 - Introduction

2:05 - Overview of what we’re going to do

4:05 - Imports

5:20 - Setup of Pytorch Dataset for loading Flickr

11:50 - Setup of Vocabulary and Numericalization

22:19 - Creating Collate for Padding of Batch

25:20 - Function for getting data loader

29:15 - Running code & fixing couple of errors

33:09 - Ending

#pytorch

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Buddha Community

Inside ABCD, A Dataset To Build In-Depth Task-Oriented Dialogue Systems

According to a recent study, call centre agents’ spend approximately 82 percent of their total time looking at step-by-step guides, customer data, and knowledge base articles.

Traditionally, dialogue state tracking (DST) has served as a way to determine what a caller wants at a given point in a conversation. Unfortunately, these aspects are not accounted for in popular DST benchmarks. DST is the core part of a spoken dialogue system. It estimates the beliefs of possible user’s goals at every dialogue turn.

To reduce the burden on call centre agents and improve the SOTA of task-oriented dialogue systems, AI-powered customer service company ASAPP recently launched an action-based conversations dataset (ABCD). The dataset is designed to help develop task-oriented dialogue systems for customer service applications. ABCD consists of a fully labelled dataset with over 10,000 human dialogues containing 55 distinct user intents requiring sequences of actions constrained by company policies to accomplish tasks.

https://twitter.com/asapp/status/1397928363923177472

The dataset is currently available on GitHub.

#developers corner #asapp abcd dataset #asapp new dataset #build enterprise chatbot #chatbot datasets latest #customer support datasets #customer support model training #dataset for chatbots #dataset for customer datasets

Hands-on Guide to Pattern - A Python Tool for Effective Text Processing and Data Mining

Text Processing mainly requires Natural Language Processing( NLP), which is processing the data in a useful way so that the machine can understand the Human Language with the help of an application or product. Using NLP we can derive some information from the textual data such as sentiment, polarity, etc. which are useful in creating text processing based applications.

Python provides different open-source libraries or modules which are built on top of NLTK and helps in text processing using NLP functions. Different libraries have different functionalities that are used on data to gain meaningful results. One such Library is Pattern.

Pattern is an open-source python library and performs different NLP tasks. It is mostly used for text processing due to various functionalities it provides. Other than text processing Pattern is used for Data Mining i.e we can extract data from various sources such as Twitter, Google, etc. using the data mining functions provided by Pattern.

In this article, we will try and cover the following points:

• NLP Functionalities of Pattern
• Data Mining Using Pattern

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#developers corner #data mining #text analysis #text analytics #text classification #text dataset #text-based algorithm

Laravel 7 Full Text Search MySQL

Here, I will show you how to create full text search in laravel app. You just follow the below easy steps and create full text search with mysql db in laravel.

Laravel 7 Full Text Search Mysql

Let’s start laravel full-text search implementation in laravel 7, 6 versions:

1. Step 1: Install Laravel New App
2. Step 2: Configuration DB .evn file
3. Step 3: Run Migration
4. Step 4: Install Full Text Search Package
5. Step 5: Add Fake Records in DB
6. Step 6: Add Routes,
7. Step 7: Create Controller
8. Step 8: Create Blade View
9. Step 9: Start Development Server

https://www.tutsmake.com/laravel-full-text-search-tutorial/

#laravel full text search mysql #laravel full text search query #mysql full text search in laravel #full text search in laravel 6 #full text search in laravel 7 #using full text search in laravel

How to Build Sustom Datasets for Text in Pytorch

In this video we go through a bit more in depth into custom datasets and implement more advanced functions for dealing with text. Specifically we’re looking at a image captioning dataset (Flickr8k data set) with an image and a corresponding caption text that describes what’s going on in the image. I think the general principles from this video can be utilized to any project you’re working with when dealing with text data be it either translation, question answering, sentiment analysis etc. I also recommend taking a look at my Torchtext which can also be quite helpful and simplify the data loading process.

Flickr8k Dataset used in the video:

https://www.kaggle.com/dataset/e1cd22…

Github repository:

https://github.com/AladdinPerzon/Mach…

OUTLINE:

0:00 - Introduction

2:05 - Overview of what we’re going to do

4:05 - Imports

5:20 - Setup of Pytorch Dataset for loading Flickr

11:50 - Setup of Vocabulary and Numericalization

22:19 - Creating Collate for Padding of Batch

25:20 - Function for getting data loader

29:15 - Running code & fixing couple of errors

33:09 - Ending

#pytorch

Text Classification With PyTorch

A baseline model with LSTMs

The question remains open: how to learn semantics? what is semantics? would DL-based models be capable to learn semantics?

Introduction

The aim of this blog is to explain how to build a text classifier based on LSTMs as well as how it is built by using the PyTorch framework.

I would like to start with the following question: how to classify a text? Several approaches have been proposed from different viewpoints under different premises, but what is the most suitable one?. It’s interesting to pause for a moment and question ourselves: how we as humans can classify a text?, what do our brains take into account to be able to classify a text?. Such questions are complex to be answered.

Currently, we have access to a set of different text types such as emails, movie reviews, social media, books, etc. In this sense, the text classification problem would be determined by what’s intended to be classified (e.g. _is it intended to classify the polarity of given text? Is it intended to classify a set of movie reviews by category? Is it intended to classify a set of texts by topic? _). In this regard, the problem of text classification is categorized most of the time under the following tasks:

• Sentiment analysis
• News categorization
• Topic analysis
• Question answering
• Natural language inference

In order to go deeper into this hot topic, I really recommend to take a look at this paper: Deep Learning Based Text Classification: A Comprehensive Review.

Methodology

The two keys in this model are: tokenization and recurrent neural nets. Tokenization refers to the process of splitting a text into a set of sentences or words (i.e. tokens). In this regard, tokenization techniques can be applied at sequence-level or word-level. In order to understand the bases of tokenization you can take a look at: Introduction to Information Retrieval.

In the other hand, RNNs (Recurrent Neural Networks) are a kind of neural network which are well-known to work well on sequential data, such as the case of text data. In this case, it’s been implemented a special kind of RNN which is LSTMs (Long-Short Term Memory). LSTMs are one of the _improved _versions of RNNs, essentially LSTMs have shown a better performance working with _longer sentences. _In order to go deeper about what RNNs and LSTMs are, you can take a look at: Understanding LSTMs Networks.

Since the idea of this blog is to present a baseline model for text classification, the text preprocessing phase is based on the tokenization technique, meaning that each _text sentence _will be tokenized, then each _token _will be transformed into its index-based representation. Then, each _token sentence based indexes _will be passed sequentially through an embedding layer, this embedding layer will output an embedded representation of each token whose are passed through a two-stacked LSTM neural net, then the last LSTM’s hidden state will be passed through a two-linear layer neural net which outputs a single value filtered by a sigmoid activation function. The following image describes the model architecture:

#pytorch #text-mining #lstm #text-classification #python