Socialgist’s Post

For any insights folks researching Review Data APIs - we did the homework for you 😉 If you're thinking "I can just query review sites on my social listening tool", here's why you should consider APIs to step the innovation up a notch 👇 1. Training predictive AI models 2. Agencies carrying out social listening for multiple clients 3. Deploying specialized NLP models and transformations 👕 An example we've seen: An intelligence agency customizes NLP & predictive AI models to decode industry-specific language nuances, like slang and style concepts in fashion. This requires APIs that can handle large-scale data integration and allow for customized data streams (i.e. data structure, filtering, enrichments). 📕 Here's the full article: https://lnkd.in/e5zBNekp Shout out to Juan Combariza for carrying out the research. Procuring third-party APIs can eat up weeks of time for social listening teams championing new initiatives. For our own data platform (Datastreamer), we've had to research and technically integrate dozens of third-party APIs. We hope the lessons we've learned along the way can speed up your own evaluations. P.S. Many of these API's are now pre-integrated in Datastreamer, so you can add & enrich these real-time data feeds to your own apps & reports in minutes ⚡ TLDR; the top choices were Socialgist, APILayer (reviewapi . com), and Webz.io

Understanding Segment Embeddings in BERT Segment embeddings are crucial in transformer models like BERT for handling multiple inputs, such as pairs of sentences. Here's a quick overview: 1. Input Sequence: Sentences like "I like cats" and "I like dogs" are combined with `[CLS]` and `[SEP]` tokens to create a unified input. 2. Tokenization: The input is tokenized into recognizable units for the model. 3. Segment Labels: Each sentence's tokens are assigned segment labels (`0` for the first sentence, `1` for the second), helping the model differentiate between them. 4. Embedding Vectors: Token and segment embeddings are added together to form the input matrix for the model’s transformer layers. 5. Key Role in NLP Tasks: Segment embeddings enhance the model's ability to understand relationships between multiple sentences, improving performance in tasks like question answering. Follow for more insights on NLP, machine learning, and AI!

Let's dive into RAG today! RAG is a 'concept' and a technique in the field of NLP and ML, particularly related to improving the performance and capabilities of language models What is RAG:? As we all know that Large Language Models (LLMs) are trained on vast volumes of data and RAG extends the already powerful capabilities of LLMs to specific domains or an organization's internal knowledge base, all without the need to retrain the model. It is a cost-effective approach to improving LLM output so it remains relevant, accurate, and useful in various contexts. What is the need of it:? Known challenges of LLMs include: 1.Providing inaccurate information when it lacks the answer. 2.Offering outdated or generic information instead of a specific, up-to-date response. 3.Generating responses from unreliable sources. 4.Producing incorrect responses due to terminology confusion, where various training sources use similar terms to discuss different concepts. RAG helps tackle these issues by guiding the LLM to find the right info from trusted sources. This gives organizations more say over what's generated, and users understand how the response is created. Benefits of RAG: 1. Cost-Effective: RAG makes it cheaper to add new data to generative AI models compared to retraining them from scratch, making AI more accessible. 2. Up-to-Date Info: RAG lets developers feed real-time data like news and social media directly into AI models, ensuring users get the latest information. 3. Trustworthy Output: RAG helps AI provide accurate info with clear sources, boosting user confidence and trust in the technology. 4. Developer Control: With RAG, developers can tweak AI's data sources easily, adapting it to different needs and ensuring it gives appropriate responses. They can also fix any mistakes quickly, making AI more reliable for various tasks. How does Retrieval-Augmented Generation work? 1.Gathering External Data: New data, like documents or records, is collected from various sources and converted into a format AI can understand using numerical representations. 2.Finding Relevant Info: When a user asks a question, The user query is converted to a vector representation and matched with the vector databases andAI searches the external data for relevant info using mathematical calculations. 3.Adding Context: AI incorporates the relevant data into its response, making sure it's accurate and helpful. 4.Keeping Data Updated: To ensure the info stays current, the documents are regularly updated and their representations refreshed. will learn more about RAG in the following post.

1. input Sequence Preparation: - We start with two separate sentences: "I like cats" (Input 1) and "I like dogs" (Input 2). These inputs are combined into a single sequence, using special tokens `[CLS]` to signify the start of the sequence and `[SEP]` to indicate the separation between sentences. The combined sequence is: `[CLS] I like cats [SEP] I like dogs [SEP]`. - The use of `[CLS]` at the start is standard in BERT for classification tasks, and the `[SEP]` token is used to separate the sentences, signaling that two distinct inputs are present. 2. Tokenization and Token Indexing: - Each word in the input sequence is converted into tokens, including special tokens. Here, each word and each special token becomes a distinct token, resulting in a sequence of 8 tokens: `[CLS]`, `I`, `like`, `cats`, `[SEP]`, `I`, `like`, `dogs`. - Tokenization is crucial because it breaks down sentences into recognizable units that the model can process. 3. Segment Labeling: - Segment embeddings help the model distinguish between different parts of the input. Here, tokens from the first sentence ("I like cats") are assigned a segment label of `0`, and tokens from the second sentence ("I like dogs") are labeled `1`. - The resulting labels are: [0, 0, 0, 0, 1, 1, 1, 1], where `0` represents tokens from the first sentence and `1` represents tokens from the second sentence. This distinction helps the model understand which tokens belong to which sentence. 4. Vector Representation and Embedding : - Each token is mapped to a high-dimensional vector using the model's token embeddings. Additionally, each segment label is mapped to a vector via segment embeddings. These segment embeddings are added to the token embeddings to produce the final vector representation. - In the image, the final representation is shown as an 8x768 matrix, where `8` is the number of tokens, and `768` is the dimensionality of each token's vector representation. This matrix is a crucial input to the transformer layers. 5. Use of Segment Embeddings in NLP Tasks: - Segment embeddings allow BERT and similar models to process pairs of sentences and understand their relationship. This is essential for tasks like question answering, where the model must understand how different parts of the input relate to each other. - By distinguishing between different segments, the model can better capture contextual information, leading to improved performance on tasks that require understanding the interaction between multiple sentences. This detailed explanation illustrates how segment embeddings enable transformer models to handle complex tasks involving multiple inputs. By incorporating segment information into the model’s processing, these embeddings play a vital role in many natural language understanding applications. Follow for more in-depth content on natural language processing, machine learning, and AI advancements!

🔍 Navigating 𝐃𝐚𝐭𝐚 𝐃𝐫𝐢𝐟𝐭 𝐢𝐧 𝐍𝐋𝐏: Why It Matters and How to Handle It? What Is Data Drift? In simple terms, data drift happens when the type of text your NLP model was trained on starts to differ from the real-world text it’s being fed after deployment. And guess what? It happens all the time. Whether it's the introduction of new words, changes in topics, or just the way people communicate, NLP models can start to struggle when faced with these shifts. Let’s break down some common forms of data drift: 1. Topic Shift 📚 Imagine you’ve built a sentiment analysis model to understand movie reviews. Over time, if reviews start focusing on streaming experiences or video quality instead of plot or acting, your model may begin to give incorrect predictions. This is a topic shift. 2. Semantic Drift 🧠 Words can change their meanings. Take "cloud," for example. It once referred to weather, but now it’s commonly associated with computing. If your model hasn’t been updated, it might struggle with this evolving meaning. 3. User Input Style Shift 💬 In a chatbot, if people start using more slang, abbreviations, or emojis than the formal sentences your model was trained on, you may see a decline in performance. 4. Named Entity Drift 📍 In Named Entity Recognition (NER), if new names, places, or products emerge after your model was trained, it might not know how to handle these new entities. For example, if a new tech company becomes popular, but your model wasn’t trained on it, it might misinterpret the entity. So, 𝐇𝐨𝐰 𝐃𝐨 𝐖𝐞 𝐇𝐚𝐧𝐝𝐥𝐞 𝐃𝐚𝐭𝐚 𝐃𝐫𝐢𝐟𝐭? 🤔 Here’s the reality: data drift is unavoidable. But you can take steps to minimize its impact: 1. Monitor Continuously: Keep an eye on your model’s performance over time. If you see a dip in accuracy or an increase in errors, it might be a sign of drift.    2. Check Feature Distributions: Compare the features of your current data with your training data. You might need to retrain or fine-tune your model if there are large differences. 3. Embedding Space Changes: Monitor changes in word embeddings. If the way words are represented shifts over time, you might need to adapt your model. 4. Frequent Model Updates: Don’t let your model get stale. Regularly update it with fresh data that reflects current trends, language usage, and user behavior. Why Does This Matter? 🛠️ Ignoring data drift can lead to poor model performance, and in many cases, incorrect predictions. For businesses relying on NLP models—whether for customer service chatbots, text classification, or sentiment analysis—this could mean a loss of customer trust and credibility. So, always monitor how your data and language trends are changing. If you do, your NLP models will continue to deliver high-quality results long after deployment. #DataDrift #NLP #MachineLearning #ArtificialIntelligence #AI #ModelMonitoring #ModelManagement #NaturalLanguageProcessing

#VectorEmbeddings simplify complex data representation. Let me show you how to create vector embeddings in just 5 minutes. But before, what are vector embeddings? Vector embeddings are dense representations of objects (including words, images or user profiles) in a continuous vector space. Each object is represented by a point (or vector) in this space, where the distance and direction between points capture semantic or contextual relationships between the objects. For example in NLP, similar words are mapped close together in the embedding space. Here is my complete article on vector embeddings with a hands-on tutorial you can try: https://lnkd.in/gS7pwYHJ

This is a fantastic breakdown of the Transformer architecture! 🚀 Your explanation covers all the essential components — from the attention mechanism to positional encoding and beyond — making it easy for anyone to grasp the underlying math and intuition behind Transformers. 📊 The visual representation in your video is a great addition, allowing viewers to follow along with complex concepts like QKV vectors and multi-head attention. 💡 The way you emphasize the role of scaled dot-product attention, multi-head configurations, and feed-forward networks really highlights how these layers work together to extract contextual relationships from sequences. It’s impressive how each detail, such as positional encoding formulas or layer normalization strategies, is presented with clarity and backed up by formulas to illustrate the process step-by-step. 🔍 For anyone looking to dive deep into Transformers or gain a stronger mathematical understanding of the architecture, this video is a must-watch. 🎥 The coverage of decoding strategies like beam search and top-k sampling further enriches the learning experience, offering a comprehensive guide to how these models work end-to-end. Thank you for putting together such a detailed and insightful explanation. 🙏 It's a valuable resource for both beginners and advanced enthusiasts looking to master the fundamentals of Transformer models. #AIResearch #Transformers #MachineLearning #DeepLearning #NLP #AttentionMechanisms #GenerativeAI

🚀 Unlocking the Power of RAG with LLama3 Are you ready to dive into the fascinating world of Retrieval-Augmented Generation (RAG)? 🌟 In this post, I’ll introduce you to the synergy between RAG and LLama3, an advanced language model that takes NLP capabilities to new heights. What is RAG? RAG is a groundbreaking framework that combines retrieval and generative models to deliver accurate and contextually relevant responses. It excels in tasks like question-answering, summarization, and conversational agents. Here’s how it works: Retrieve: Relevant documents are retrieved from a corpus based on the user’s query. Unlike traditional search engines, RAG leverages advanced language models for semantic understanding. Aggregate: The retrieved information is condensed and summarized to extract key insights. Generate: Finally, RAG uses the aggregated information to create coherent responses. Meet LLama3 LLama3, the retrieval component in the RAG framework, is a game-changer. Unlike traditional search engines, it understands queries more nuancedly and retrieves documents based on semantic relevance. Building Your Own RAG Project To showcase the integration of RAG with LLama3, let’s build a project using Phidata. This framework enhances language models’ capabilities, including reading data from local PDFs. Here’s how you can get started: PDF Knowledge Base: Use Phidata’s PDFKnowledgeBase to read local PDF files, convert them into vector embeddings, and load them into a vector database. Assistant Integration: Set up an Assistant with the knowledge base, allowing you to answer queries based on the PDF content. Remember, RAG and LLama3 empower you to create intelligent, context-aware applications. Let’s explore this exciting field together! 🤖📚

"𝗘𝘃𝗲𝗿 𝗪𝗼𝗻𝗱𝗲𝗿𝗲𝗱 𝗛𝗼𝘄 𝗔𝗜 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝘀 𝗘𝗺𝗼𝘁𝗶𝗼𝗻𝘀? 𝗕𝘂𝗶𝗹𝗱 𝗮 𝗦𝗲𝗻𝘁𝗶𝗺𝗲𝗻𝘁 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗧𝗼𝗼𝗹 𝘄𝗶𝘁𝗵 𝗚𝗣𝗧! 💬🤖" We’ve all seen AI tools that can analyze emotions in text, but have you ever thought about building one yourself? With GPT, it’s easier than you think! Here’s a beginner-friendly project to help you create your own Sentiment Analysis Tool and dive deeper into practical AI applications. 𝟭. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗦𝗲𝗻𝘁𝗶𝗺𝗲𝗻𝘁 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀? Sentiment analysis helps you understand the emotions behind the text, whether it’s positive, negative, or neutral. From analyzing product reviews to gauging customer feedback, it’s a powerful way to unlock insights hidden in text data. 𝟮. 𝗪𝗵𝘆 𝗨𝘀𝗲 𝗚𝗣𝗧? GPT’s language understanding makes it perfect for sentiment analysis. By fine-tuning GPT with a labelled dataset, you can train it to detect emotions in text with impressive accuracy—without needing to be a data scientist! 𝟯. 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗕𝗿𝗲𝗮𝗸𝗱𝗼𝘄𝗻: Here’s how you can get started with building your own tool: 𝘾𝙤𝙡𝙡𝙚𝙘𝙩 𝘿𝙖𝙩𝙖: Grab text data from sources like Twitter, customer reviews, emails or you can use a dataset from Kaggle. 𝙋𝙧𝙚𝙥𝙧𝙤𝙘𝙚𝙨𝙨 𝙩𝙝𝙚 𝙏𝙚𝙭𝙩: Clean the text and break it into tokens so GPT can better understand it. 𝙁𝙞𝙣𝙚-𝙩𝙪𝙣𝙚 𝙂𝙋𝙏: Train GPT on your dataset to classify sentiments—whether they’re positive, negative, or neutral. 𝙏𝙚𝙨𝙩 & 𝙏𝙬𝙚𝙖𝙠:Test the model on new data and adjust for better accuracy. 𝘿𝙚𝙥𝙡𝙤𝙮: Use your sentiment analysis tool to analyze live data or create a simple app for fun! 𝟰. 𝗪𝗵𝘆 𝗕𝘂𝗶𝗹𝗱 𝗧𝗵𝗶𝘀?  Sentiment analysis isn’t just an AI buzzword—it’s used in marketing, customer support, and product development every day. Building this project gives you hands-on experience with GPT and teaches you how to apply AI to real-world problems. 𝟱. 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝘁𝗼 𝗚𝗲𝘁 𝗦𝘁𝗮𝗿𝘁𝗲𝗱: Hugging Face Tutorial for Sentiment Analysis: https://lnkd.in/gXjMBC5M OpenAI – GPT-3 API Fine-Tuning Guide: https://lnkd.in/gAtjxDUU Get Creative with GPT! Once you’ve built your tool, you can apply it to so many areas. Want to analyze social media trends? Or maybe track how customers feel about your product in real time? The possibilities are endless. #AIProjects #GPT #SentimentAnalysis #MachineLearning #AIDevelopment #TechInnovation #BuildWithAI #LearnWithMe

🚀 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐞𝐫 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 𝐄𝐱𝐩𝐥𝐚𝐢𝐧𝐞𝐝 𝐟𝐫𝐨𝐦 𝐒𝐜𝐫𝐚𝐭𝐜𝐡 𝐰𝐢𝐭𝐡 𝐃𝐞𝐭𝐚𝐢𝐥𝐞𝐝 𝐌𝐚𝐭𝐡 𝐄𝐱𝐚𝐦𝐩𝐥𝐞𝐬 🚀 📽 𝐕𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤: https://lnkd.in/g46TDu8u Transformers are encoder-decoder models that have revolutionized NLP tasks. Unlike previous RNN-based models, they process entire sequences in parallel, leveraging attention mechanisms to capture contextual relationships. 𝐊𝐞𝐲 𝐜𝐨𝐦𝐩𝐨𝐧𝐞𝐧𝐭𝐬: 𝟏. 𝐄𝐧𝐜𝐨𝐝𝐞𝐫: Processes input sequence 𝟐. 𝐃𝐞𝐜𝐨𝐝𝐞𝐫: Generates output sequence 𝟑. 𝐀𝐭𝐭𝐞𝐧𝐭𝐢𝐨𝐧 𝐦𝐞𝐜𝐡𝐚𝐧𝐢𝐬𝐦𝐬: Self-attention and cross-attention ⚙️ 𝐀𝐭𝐭𝐞𝐧𝐭𝐢𝐨𝐧 𝐢𝐬 𝐀𝐥𝐥 𝐘𝐨𝐮 𝐍𝐞𝐞𝐝 The core of the transformer is its attention mechanism. Let's break it down: 1. 𝐐𝐮𝐞𝐫𝐲, 𝐊𝐞𝐲, 𝐕𝐚𝐥𝐮𝐞 (𝐐𝐊𝐕) 𝐕𝐞𝐜𝐭𝐨𝐫𝐬:   • Query (Q): What we're looking for   • Key (K): Where to look   • Value (V): What to retrieve 2. 𝐒𝐜𝐚𝐥𝐞𝐝 𝐃𝐨𝐭-𝐏𝐫𝐨𝐝𝐮𝐜𝐭 𝐀𝐭𝐭𝐞𝐧𝐭𝐢𝐨𝐧:   • 𝐅𝐨𝐫𝐦𝐮𝐥𝐚: Attention(Q, K, V) = softmax(QK^T / √d_k)V   • Scaling factor (√d_k) prevents vanishing gradients 3. 𝐌𝐮𝐥𝐭𝐢-𝐇𝐞𝐚𝐝 𝐀𝐭𝐭𝐞𝐧𝐭𝐢𝐨𝐧:   • Allows model to focus on different aspects simultaneously   • Typically uses 8-16 attention heads 🧮 𝐓𝐡𝐞 𝐖𝐨𝐫𝐤𝐢𝐧𝐠 𝐁𝐞𝐡𝐢𝐧𝐝 𝐀𝐭𝐭𝐞𝐧𝐭𝐢𝐨𝐧 For each token in a sequence: 1. Compute attention scores with all other tokens 2. Apply softmax to get attention weights 3. Use weights to compute weighted sum of values 📊 𝐏𝐨𝐬𝐢𝐭𝐢𝐨𝐧𝐚𝐥 𝐄𝐧𝐜𝐨𝐝𝐢𝐧𝐠 Since transformers process input in parallel, they need positional information: • Uses sine and cosine functions of different frequencies • 𝐅𝐨𝐫𝐦𝐮𝐥𝐚: PE(pos, 2i) = sin(pos/10000^(2i/d_model)) • 𝐅𝐨𝐫𝐦𝐮𝐥𝐚: PE(pos, 2i+1) = cos(pos/10000^(2i/d_model)) This allows the model to understand token positions without sequential processing. 🔍 𝐅𝐞𝐞𝐝-𝐅𝐨𝐫𝐰𝐚𝐫𝐝 𝐍𝐞𝐭𝐰𝐨𝐫𝐤𝐬 (𝐅𝐅𝐍): • Two linear transformations with a ReLU activation • 𝐅𝐨𝐫𝐦𝐮𝐥𝐚: FFN(x) = max(0, xW_1 + b_1)W_2 + b_2 • Expands input dimension (e.g., 512 to 2048) then projects back 💡 𝐋𝐚𝐲𝐞𝐫 𝐍𝐨𝐫𝐦𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐑𝐞𝐬𝐢𝐝𝐮𝐚𝐥 𝐂𝐨𝐧𝐧𝐞𝐜𝐭𝐢𝐨𝐧𝐬: • Helps stabilize training and avoid vanishing/exploding gradients • 𝐅𝐨𝐫𝐦𝐮𝐥𝐚: LayerNorm(x + Sublayer(x)) 🤖 𝐃𝐞𝐜𝐨𝐝𝐢𝐧𝐠 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬: 𝟏. 𝐆𝐫𝐞𝐞𝐝𝐲 𝐃𝐞𝐜𝐨𝐝𝐢𝐧𝐠: Always choose most probable next token 𝟐. 𝐁𝐞𝐚𝐦 𝐒𝐞𝐚𝐫𝐜𝐡: Explore multiple possible sequences 𝟑. 𝐓𝐨𝐩-𝐤 𝐒𝐚𝐦𝐩𝐥𝐢𝐧𝐠: Sample from top k most probable tokens For more details refer to my video and the PDF provided 📽𝐕𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤: https://lnkd.in/g46TDu8u