Unleash the Power of FastText for Text Classification with Python!
Get ready to tackle any text classification task with ease, from sentiment analysis to spam filtering, and everything in between.
FastText is a fast, efficient, and easy-to-use library that makes text classification a breeze. Say goodbye to complicated models and hello to accurate predictions with FastText and Python!
Recurrent neural network is a sequence model, used mainly for Natural Language Processing tasks. In this Recurrent neural network tutorial we will understand how RNN works with real numbers in excel and we will see some applications of recurrent neural network.
FastText is an NLP library developed by the Facebook research team for text classification and word embeddings. FastText is popular due to its training speed and accuracy. If you want you can read the official fastText paper. There are different frameworks of FastText: In this fastText tutorial post, I will only talk about fastText word …
Discover the power of fastText and Python in accurately detecting the language of text. In this blog post, we explore the built-in language detection system of fastText and how it can be implemented in Python. With code examples and practical insights, you’ll learn how to train and use a language detection model with fastText. Whether you’re a beginner or an experienced practitioner in NLP, fastText is a must-have tool in your arsenal. Read on to learn more about accurate language detection using fastText and Python.
A few years ago, the idea of text-to-speech software would have been laughable, if not impossible. However, with deep learning and artificial neural networks on the rise, it’s quite possible to convert text to speech on your own computer. In this guide, you’ll learn how to perform text-to-speech conversion with your own Python script to make speech synthesis more natural sounding than ever before possible.
In this tutorial, I will show you how you can finetune a generative model named t5 to make our own chatbot. We will finetune t5 model using Openassist dataset. Why to fine tune T5? In my last tutorial, I explained how you can train transformer models like: BERT, RoBERTa, etc. But those are extractive models. …
After releasing chatgpt, large language model or LLM is a hot cake in the market now. Every organization wants to make their own chatbot. But fine tuning a large language model (LLM) is a difficult task. In this tutorial, I will show you how you can easily finetune LLM like Pythia, LLAMA, GPT-J, GPT-NEO, Falcon, …
Finetune LLM (LLAMA, GPT-Neo, GPT-J, Pythia) in Google Colab Read More »
Chatbots are becoming more popular and useful in various domains, such as customer service, e-commerce, education,entertainment, etc. However, building a chatbot that can understand and respond to natural language is not an easy task. It requires a lot of data (or dataset) for training machine-learning models of a chatbot and make them more intelligent and …
We all remember that during our school days, teachers used to assign homework tasks, and the next day they would ask questions on those topics. BERT is built to do the exact same type of question answering job for us. We can feed a passage of text to BERT, and it can help provide answers …
If you want to train BERT model on your custom domain data, then this tutorial is for you. In this tutorial, I will guide you for training data preparation for custom BERT model in Python. Note: I will show you the steps to prepare training data to train custom BERT model for Question Answering. You …
