RQle.AI的动态

Check out this Google #Colab that offers a behind-the-scene look at the steps required to extract key insights from YouTube videos. It leverages Google DeepMind #Gemini for text summarization, demonstrating a powerful for content analysis. #GenerativeAI #Showcase #NLP #Summarization

?? Exploring BERT: Understanding Token Embeddings and Attention Masks ?? I’ve been exploring BERT (Bidirectional Encoder Representations from Transformers) and focusing on how it processes token embeddings and attention masks. Key Takeaways: Tokenization: BERT converts sentences into tokens, including special tokens like [CLS] , [SEP] and [PAD]. Attention Masks: These masks indicate valid tokens (1) and padding (0), allowing the model to focus only on relevant tokens. Embeddings: Each token has an embedding that captures its contextual meaning. I learned how to extract these embeddings. Implementation: I created a colab notebook to explore token embeddings and attention masks. You can check out my full colab notebook to see the details. Colab notebook link -https://lnkd.in/gpD4va8K I’m open to ideas and suggestions. If you have insights or experiences related to BERT, NLP, or any other topics, please share them in the comments. #BERT #NLP #AI #DataScience

Project: Fine-tuning GPT-2 for Medical Question-Answering Overview: This project involves fine-tuning the GPT-2 language model on a Medical Q&A dataset to enhance its ability to answer medical questions. Key Achievements: -Conducted data preprocessing, exploratory data analysis (EDA), and feature extraction. -Loaded a pre-trained tokenizer for efficient text processing. -Fine-tuned the GPT-2 model for improved medical question-answering. -Uploaded the model to Hugging Face Model Hub. -Deployed an interactive application on Hugging Face Spaces using Gradio. #MachineLearning #NaturalLanguageProcessing #NLP #GPT2 #HuggingFace #AI #DeepLearning #MedicalAI #DataScience #QuestionAnswering #Gradio https://lnkd.in/daijsQaQ

Proud to share the second notebook in my NLP series! Last week, we explored essential text preprocessing techniques, such as tokenization, stopwords removal, stemming and lemmatization (link below). This week, we focus on foundational methods for converting text into numerical formats suitable for training machine learning models. In this notebook, we explore the Bag of Words (BoW) and TF-IDF methods, key starting points for anyone interested in NLP. These techniques are crucial for transforming raw text into a structured format that machine learning algorithms can utilize. In the future, we will discuss more sophisticated and powerful techniques, such as word embeddings. You can find the notebook here: https://lnkd.in/dN_mtW8f With this first simple method of converting text into numbers, in the next notebooks we will already be able to train a basic machine learning model. Stay tuned! I aim to publish the next one within two weeks. I hope you find this series useful. Feedback and discussions are highly welcome! Previous notebook on text preprocessing: https://lnkd.in/dkTWgAsm #NLP #MachineLearning #DataScience #AI

?? Evaluating Zero-Shot Learning for Political Advertisement Classification ?? In my latest post, I evaluate the performance of a #ZeroShot #TextClassification model on an expert labeled dataset of over 10,000 televised political ads provided by the Wesleyan Media Project. These ads, aired in various 2018 U.S. races, are labeled based on their primary purpose: promoting a candidate, attacking a candidate, or contrasting candidates.?To extract the ad text, I used a transformer model and transcribed the audio. ?? Key Insights: - The zero-shot model performs reasonably well in distinguishing between different types of political advertisements, achieving a balanced accuracy of ~80% without any training data! - While confidence in predictions can vary, experimenting with more tailored prompts and context may improve performance. - If you're looking for a quick solution with minimal setup, zero-shot learning is a powerful approach to rapidly test model performance before committing to more complex methods. ?? Practical Takeaway: Whether to refine prompts or train a dedicated model for your classification task depends on your project’s goals and time constraints. Check out my post here! https://lnkd.in/ePperSA7 to see how you can apply this to your own classification tasks! ?? #DataScience #NLP #ZeroShotLearning #AI #MachineLearning #PoliticalAds

I decided to start writing a series of Python notebooks on Natural Language Processing (NLP)! The first notebook is now online, focusing on essential text pre-processing techniques. This series will cover everything from basic models to advanced topics like transformers and neural networks. Whether you're new to NLP or looking to deepen your knowledge, there's something for everyone. For now, only the first notebook is available, but I hope to have the time to publish new ones approximately every two weeks.? Constructive feedback is highly appreciated as it will help me improve and deliver better content. Check out the first notebook and stay tuned for more insights into the world of NLP! :-) https://lnkd.in/dkTWgAsm #NLP #MachineLearning #DataScience #AI

Build Customized Chatbots in 25 Days (2/25) ?? Exploring Large Language Models (LLMs) and NLP ?? In my last post, I introduced the concept of building a custom chatbot. Today, let’s dive into the technologies behind it: Large Language Models (LLMs) and Natural Language Processing (NLP). What are Large Language Models (LLMs)? ?? LLMs are advanced algorithms trained on extensive text data to understand and generate human-like text. They are essential for building chatbots capable of sophisticated interactions. NLP enables computers to understand and process human language. Key tasks include: Named Entity Recognition (NER): Identifies and classifies entities like names and dates. ??? Sentiment Classification: Analyzes the sentiment of text (positive, negative, neutral). Text Summarization: Creates concise summaries of longer texts. ???? Machine Translation: Transalates text between languages. ???? Question Answering: Provides precise answers based on the text. ??? From Words to Numbers: Word2Vec ?? To process text, we convert words into numerical values using techniques like Word2Vec. This method maps words to numbers based on their context. For instance, in the sentence "What a wonderful movie! Full of action and drama," Word2Vec would map the word "wonderful" to a vector based on the surrounding words. Word2Vec uses deep neural networks to generate these vectors, with hidden layer weights stored as embeddings representing the numerical values of words. ?? See it in Action: For a practical demonstration of sentiment classification using Word2Vec, check out my Colab notebook here. This notebook includes examples with movie reviews to illustrate how Word2Vec maps words to vectors classifies sentiment, and some explanations. https://lnkd.in/gz_JMUBN However, Word2Vec has limitations, such as its static nature and lack of context sensitivity, which can lead to confusion between words like "bank" (river vs. financial institution). This is also demonstrated in the notebook. ???? Next Up: Transformer Embeddings ?? To overcome Word2Vec’s limitations, we use transformer embeddings, which capture context more effectively. Stay tuned for my next post where I'll explain transformer architecture and its advantages. #NLP #LLM #Word2Vec #NER #Sentiment_Analysis #Chatbot #Analytics #Transformers

?? Unlocking the Power of Text Embedding with Cohere and Hugging Face ?? Text embedding lies at the heart of the remarkable capabilities of Large Language Models (LLMs). It transforms textual data into numerical vectors, enabling machines to understand and process language effectively. In my latest notebook, we delve into the world of text embedding using Cohere's Embed endpoint and the rich email intent classification dataset from HuggingFace. Join me on this exciting journey as we harness the potential of text embedding to unlock deeper insights and drive innovation in natural language processing. Notebook link: https://lnkd.in/dXbFcFdJ _Love and AI_ ???? #TextEmbedding #Cohere #HuggingFace #NLP #AI

Hugging Face is making waves in the fields of machine learning, deep learning, and natural language processing (NLP). If you're not familiar with it yet, now is the perfect time to dive in! ??? With cutting-edge models for text generation, translation, summarization, and more, Hugging Face is simplifying complex tasks. The latest buzz is all about Pipelines, a feature that grants easier access to these powerful models. Pipelines offer functionality for over 20 tasks, including: - Text categorization ?? (Emotional intelligence or spam identification) - Question answering ?? (Automated Q&A systems) - Text generation (ideal for content creators) For a quick start, dive into Colab here: https://lnkd.in/drWP8Bkg. Explore example notebooks at: https://lnkd.in/dSc2aMV9. It's time to unleash your creativity and embark on remarkable projects! ???? #NLP #MachineLearning #DeepLearning #AI #HuggingFace #Transformers #DataScience #AIInnovation

I'm excited to share that I've fine-tuned the Google Gemini family model "google/gemma-1.1-2b-it" and pushed it to Hugging Face! ?? By fine-tuning this model, I've improved its accuracy and ability to generate more relevant and coherent responses. Check it out here: https://lnkd.in/dCUBvwk7 Benefits of Fine-Tuning: 1. Increased accuracy in response to prompts 2. More relevant and coherent text generation 3. Improved performance for specific tasks Prerequisites: Hugging Face account Colab URL: https://lnkd.in/dehcFZQh This Colab notebook provides a step-by-step guide on how to fine-tune the model yourself. Feel free to experiment and see the difference! Follow me for more. #finetuning #LLM #HuggingFace #google #gemma #AI #NLP