https://blog.sksoumik.com/tags/nlp/ Recent content in NLP on Sadman Kabir Soumik Hugo -- gohugo.io en Copyright © 2022, Sadman Kabir Soumik Tue, 18 Mar 2025 00:00:00 +0000 https://blog.sksoumik.com/artificial-intelligence/different-types-of-llms-architectures/ Tue, 18 Mar 2025 00:00:00 +0000 https://blog.sksoumik.com/artificial-intelligence/different-types-of-llms-architectures/ Let's first break it down: what exactly are large language models (LLMs), why do we call them 'large,' and how are they different from other types of language models? An LLM is a machine learning model trained on massive amounts of text using transformer-based architectures (or their variations). These models can generate, understand, and process human-like text, making them useful for tasks like translation, summarization, reasoning, coding etc,. How is an LLM different from other language models? https://blog.sksoumik.com/artificial-intelligence/from-rnn-to-transformers-without-math/ Mon, 30 Jan 2023 00:00:00 +0000 https://blog.sksoumik.com/artificial-intelligence/from-rnn-to-transformers-without-math/ Transformer-based models are a types of neural network architecture that uses self-attention mechanisms to process input data. They were introduced in the paper "Attention Is All You Need" by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin in 2017, and have since become a popular choice for many natural language processing task. Prerequisite: Before going further, I assume that you have a basic understanding of how neural networks work. https://blog.sksoumik.com/artificial-intelligence/multi-class-text-classification-apache-spark-mllib/ Tue, 24 May 2022 00:00:00 +0000 https://blog.sksoumik.com/artificial-intelligence/multi-class-text-classification-apache-spark-mllib/ Spark MLlib MLlib is a library for machine learning in Spark that aims to make it easy to use and scalable for practical applications. It includes tools for common ML tasks, such as classification, regression, clustering, and collaborative filtering, as well as featurization methods for feature extraction, transformation, dimensionality reduction, and selection. MLlib also provides tools for building, evaluating, and tuning ML pipelines, as well as utilities for linear algebra, statistics, and data handling. https://blog.sksoumik.com/artificial-intelligence/keyphrase-extraction-techniques-with-bert-embeddings-pos-patterns/ Thu, 19 May 2022 00:00:00 +0000 https://blog.sksoumik.com/artificial-intelligence/keyphrase-extraction-techniques-with-bert-embeddings-pos-patterns/ Keyphrases are important pieces of information that can be extracted from text documents. These are words or phrases that summarize the main ideas or topics of a text, and they can be useful for a variety of applications, such as document summarization, text classification, and information retrieval. In this blog post, we will explore how keyphrases can be extracted from text documents, and discuss some of the techniques and tools that can be used for this task. https://blog.sksoumik.com/artificial-intelligence/a_brief_introduction_on_openai_gpt_3/ Sat, 20 Nov 2021 00:00:00 +0000 https://blog.sksoumik.com/artificial-intelligence/a_brief_introduction_on_openai_gpt_3/ Author: Sadman Kabir Soumik GPT-3, or Generative Pretrained Transformer 3, is a state-of-the-art language model developed by OpenAI. It has been trained on a massive amount of text data, including books, articles, and websites, to generate coherent and relevant text based on a given context. GPT-3 is a transformer-based model, which means that it uses a type of neural network architecture called a transformer to process the input text. This allows the model to capture long-range dependencies and generate text that is more coherent and human-like than previous models. https://blog.sksoumik.com/artificial-intelligence/understanding-word-embeddings/ Fri, 11 Dec 2020 00:00:00 +0000 https://blog.sksoumik.com/artificial-intelligence/understanding-word-embeddings/ Word embedding is a technique in natural language processing (NLP) where words are represented as vectors of real numbers. This allows words with similar meanings to have similar representation, and can be used in various NLP tasks such as machine translation and text classification. There are several different techniques for word embedding in natural language processing (NLP), including: TF-IDF — Term Frequency-Inverse Document Frequency TF-IDF (Term Frequency-Inverse Document Frequency) is a technique used in natural language processing to measure the importance of a word in a document. https://blog.sksoumik.com/artificial-intelligence/understanding-recurrent-neural-networks/ Sun, 25 Oct 2020 00:00:00 +0000 https://blog.sksoumik.com/artificial-intelligence/understanding-recurrent-neural-networks/ Recurrent Neural Network A recurrent neural network (RNN), is a type of neural network that can process sequential data, like text, audio, or time series data. Here's how it works: first, the RNN takes in some input data, which could be a word in a sentence, a sound wave from an audio recording, or a measurement from a sensor at a specific time. Then, the RNN processes this input and generates an output, which could be a predicted next word in a sentence, a generated audio waveform, or a predicted sensor measurement. https://blog.sksoumik.com/artificial-intelligence/nlp-text-cleaning-methods-explained/ Mon, 04 May 2020 00:00:00 +0000 https://blog.sksoumik.com/artificial-intelligence/nlp-text-cleaning-methods-explained/ Cleaning text for natural language processing (NLP) tasks is an important step that can help improve the performance of your model. In this blog post, we will discuss some common text cleaning techniques and how to apply them to your text data. The first step in cleaning text for NLP is to remove any noisy or irrelevant information. This can include things like HTML tags, URLs, and other extraneous characters. Removing this information can help the model focus on the relevant content and improve its performance.