Want an alternative for LLMs with a simpler implementation? 👇 *** Named Entity Recognition (NER) *** is a natural language processing (NLP) task that seeks to identify named entities in text and classify them into predefined categories, such as person names, organizations, locations, and quantities. NER is a fundamental task in NLP with a wide range of applications, such as information extraction, question answering, and machine translation. There are many different implementations of NER, but some of the most common ones include: - Rule-based NER: This approach uses a set of hand-crafted rules to identify named entities. This is the simplest and most straightforward approach, but it can be difficult to create rules that cover all possible cases. - Statistical NER: This approach uses machine learning to learn the patterns that distinguish named entities from other words. This is a more powerful approach than rule-based NER, but it requires a large amount of training data. - Hybrid NER: This approach combines rule-based and statistical NER. This can be a good way to improve the accuracy of NER systems. Some of the top Python libraries for NER include: - spaCy: This is a popular NLP library that includes a built-in NER model. - NLTK: This is another popular NLP library that has a NER module. - Stanford CoreNLP: This is a Java-based NLP library that includes a NER model. - Flair: This is a newer NLP library that is designed for NER and other sequence labeling tasks. The best Python library for NER will depend on the specific needs of the project. - If you are just getting started with NER, spaCy is a good choice because it is easy to use and has a good accuracy. - If you need a more powerful NER system, Stanford CoreNLP or Flair may be a better choice. Here are some examples of how NER can be used: 1- To extract information from news articles, such as the names of people, organizations, and locations. 2- To answer questions about a text, such as "Who is the president of the United States?" 3- To translate text from one language to another. 4- To improve the accuracy of machine learning models that are trained on text data. NER is a powerful tool that can be used for a variety of tasks. By understanding the different implementations of NER and the top Python libraries, you can choose the right approach for your project. There is a lot of overlap between NER and GEN-AI APIs use cases, but this is for a later post. #data #datascience #ner #python #nltk #spacy #flair #stanfordcorenlp #nlp #llm #largelanguagemodels #gpt #gpt4 #gpt3 Illustration credits @ Shaip
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Executive and Thought Leadership in "Data Driven", "BigData", "Data Science", "Cloud", "Data Analytics" & "AI / ML"
Large Language Models: SBERT: Learn how siamese BERT networks accurately transform sentences into embeddings Introduction It is no secret that transformers made evolutionary progress in NLP. Based on transformers, many other machine learning models have evolved. One of them is BERT which primarily consists of several stacked transformer encoders. Apart from being used for a set of different problems like sentiment analysis or question answering, BERT became increasingly popular for constructing word embeddings — vectors of numbers representing semantic meanings of words. Representing words in the form of embeddings gave a huge advantage as machine learning algorithms cannot work with raw texts but can operate on vectors of vectors. This allows comparing different words by their similarity by using a standard metric like Euclidean or cosine distance. The problem is that, in practice, we often need to construct embeddings not for single words but instead for whole sentences. However, the basic BERT version builds embeddings only on the word level. Due to this, several BERT-like approaches were later developed to solve this problem which will be discussed in this article. By progressively discussing them, we will then reach to the state-of-the-art model called SBERT.For getting a deep understanding of how SBERT works under the hood, it is recommended that you are already familiar with BERT. If not, the previous part of this article series explains it in detail. Large Language Models: BERT BERT First of all, let us remind how BERT processes information. As an input, it takes a [CLS] token and two sentences separated by a special [SEP] token. Depending on the model configuration, this information is processed 12 or 24 times by multi-head attention blocks. The output is then aggregated and passed to a simple regression model to get the final label.BERT architecture For more information on BERT inner workings, you can refer to the previous part of this article series: Cross-encoder architecture It is possible to use BERT for calculation of similarity between a pair of documents. Consider the objective of finding the most similar pair of sentences in a large collection. To solve this problem, each possible pair is put inside the BERT model. This leads to quadratic complexity during inference. For instance, dealing with n = 10 000 sentences requires n * (n — 1) / 2 = 49 995 000 inference BERT computations which is not really scalable. Other approaches Analysing the inefficiency of cross-encoder architecture, it seems logical to precompute embeddings independently for each sentence. After that, we can directly compute the chosen distance metric on all pairs of documents which is much faster than feeding a quadratic number of pairs of sentences to BERT. Unfortunately, this approach is not possible with BERT: the core problem of… #MachineLearning #ArtificialIntelligence #DataScience
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Decision Scientist @ Mu-Sigma👷 || Kaggler (Highest World Rank - 236/200,000+) 🏵|| Medium Content writer✍️|| YouTuber 📷
Hey Folks 👋! ✅ NLP is one of the most demanding⚡ fields in data science because the whole purpose of data science is to get insight from data and NLP makes it possible to make better decisions with text-based data. Read about the end-to-end lifecycle of an NLP project and about the NLP topic modeling technique using python[WITH CODES]. ▶ Table of content: -------------------------- 1. What is NLP? 2. How does an NLP Life-Cycle look end to end? 3. What is Topic Modeling and How does it work? 4. What is LDA (Latent Dirichlet Allocation)? 5. Implementation and code walkthrough (Here we are going to cover lots of buzzwords used in NLP, so make sure to understand each term) 6. What is pyLDAVis? Don't forget to subscribe to the channel🎦, I am very close to my first 500 subs♥️. ◉ Find my Youtube🎥: https://lnkd.in/gkBQsxwb ◉ About me: ----------------------------------------------------------------- ✅I am a "Decision Scientist at Mu Sigma Inc. 👷" || Kaggler (Highest World Rank - 236/200,000+) (Kaggle )🏵|| Medium Content writer✍️(Medium ) || YouTuber 📷(70k+ views) (YouTube ) , 🌺 Learning and exploring how Math, Business, and Technology can help us to make better decisions in the field of data science. ◉ Find my all handles🔁: https://lnkd.in/dCTfTneV ◉ Find my Kaggle📓: https://lnkd.in/g_XeR9J2 ◉ Find my Medium✍️: https://lnkd.in/dERdbeNi ◉ Subscribe to my Free Newsletter📰: https://lnkd.in/gEVgycBc ----------------------------------------------------------------- #businessanalytics #datascience #datascientist #kaggle #technology #learning #vlog #MUSIGMA #musigma #dataanalytics #datascience #datascientists #innovation #technology #ai #machinelearning #machinelearningalgorithms #dataengineering #machinelearningsolutions #automation
NLP📜Topic Modeling📳- LDA (Latent Dirichlet Allocation) 💬💻🧠[with codes]
blog.devgenius.io
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Exciting News in the NLP World! 📚 🌟 I am thrilled to introduce you'll to one of the highly-anticipated books of this year that's about to redefine the NLP game and shine a light on the intricate mathematics behind it! 🔍 hashtag #NLP hashtag #MachineLearning 📖 Title: "Mastering NLP from Foundations to LLMs" 📝 "Applying Advanced Techniques from Rule-Based to LLMs for Solving Real-World Business Problems" 👏 Authored by Industry Experts Lior Gazit and Meysam Ghaffari, PhD. This comprehensive guide is set to be a game-changer for both seasoned pros and eager newcomers. Here's a sneak peek into what "Mastering NLP from Foundations to LLMs" holds in store: **Key Features:** 1. 📊 Deep Dive into Technical Aspects: This book offers in-depth coverage of the technical facets of NLP, with proof-based explanations of crucial concepts and techniques. 2. 🌐 Real-World Application: Expect real-world use cases and complete code examples illustrating how NLP can tackle complex business problems. 3. 🧮 **Mathematics Demystified:** With a strong focus on mathematical foundations, you'll unlock how these principles power effective solutions across diverse business scenarios. hashtag #MathBehindNLP **Book Description:** The journey begins with an exploration of the mathematical foundations of machine learning, seamlessly extending into advanced NLP applications, including Large Language Models (LLMs) and AI implementations. Each chapter is thoughtfully designed to guide you through the NLP landscape. What You Will Learn : 🧠 Master the mathematical foundations that underlie machine learning and NLP. hashtag #MathMastery 📊 Implement effective text data preprocessing techniques. hashtag #DataPrepSkills 🖥️ Create ML-NLP system designs using Python for practical applications. hashtag #PythonNLP 📝 Learn text modeling and classification using both traditional and deep learning approaches. hashtag #TextModeling hashtag #DeepLearning 🧠 Grasp the theory, design, and application of Large Language Models. hashtag #LLMInsights 🔮 Gain valuable insights into current and future NLP trends from experts in the field. hashtag #NLPInsights "Mastering NLP from Foundations to LLMs" promises to be an indispensable resource in your NLP journey. Get ready to elevate your NLP skills and stay ahead of the curve! 🚀 Pre-order your copy today and be part of the NLP revolution! Link to Amazon US Pre-order : https://lnkd.in/d8ezqHFf
Mastering NLP from Foundations to LLMs: Applying Advanced Techniques from Rule-Based to LLMs for Solving Real World Business Problems
amazon.com
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Muhammad Qasim Zia Khan Nasir Hussain 📚 **Text Analysis Journey with spaCy: Unveiling Hidden Insights!** I'm excited to share my recent adventure in Text Analysis using the incredible spaCy library! 🚀 In this project, I delved into the world of Natural Language Processing (NLP) to unravel the secrets hidden within textual data. Let me take you through my exploration step-by-step: **Step 1: Named Entity Recognition (NER) - Discovering Entities:** One of the most intriguing aspects of NLP is Named Entity Recognition (NER). With this powerful technique, I could automatically identify entities like people's names, organizations, locations, and more within the text. Each entity was labeled with its corresponding entity type, making it easy to comprehend and analyze. **Step 2: Sentence Tokenization - Decoding Text Structure:** As I continued my journey, I delved into Sentence Tokenization. This fascinating process involved breaking down paragraphs of text into individual sentences. This allowed me to grasp the structure and flow of the content effortlessly. I could now analyze each sentence independently, gaining valuable insights into the text's context. **Step 3: Word Tokenization - Unraveling Textual Building Blocks:** Next, I dived into the world of Word Tokenization. Witnessing the language processing algorithms split sentences into individual words was both captivating and enlightening. This step laid the foundation for understanding the text at its granular level, enabling more profound analysis. **Interactive Web App - Sharing My Discoveries:** To make my findings accessible to others, I created an interactive web app using Streamlit. Users can now paste or type their own text into the app's text area. The app then performs the same NER, Sentence Tokenization, and Word Tokenization on the provided text, enabling others to uncover insights just like I did! **Empowering Data-Driven Decision Making:** My Text Analysis journey has demonstrated the immense potential of NLP in understanding textual data. Whether it's for analyzing news articles, blog posts, or any other text, the power of spaCy has proven to be a game-changer. NER, Sentence Tokenization, and Word Tokenization are essential tools for any language enthusiast, data scientist, or decision-maker seeking deeper insights. **Bonus Step: Dataset Creation and Download:** As a cherry on top, I incorporated a data export feature in the app. After performing Text Analysis, the app generates a dataset with the Named Entities, Sentence Tokens, and Word Tokens. Users can then download this dataset as a CSV file, enabling further analysis or integration with other tools.
