OSMOSIS Association’s Post

RML/YARRRML – Converting JSON to RDF 🔧 What is RML/YARRRML? RML (RDF Mapping Language) is a framework that helps you convert various data formats (like JSON) into RDF triples. YARRRML is a more human-readable syntax for RML. 💻 How I Used It: Converted film credits data from JSON files into RDF format. Automated the mapping process using RMLMapper. 💡 Why It’s Useful: RML/YARRRML allows you to easily convert complex, unstructured data into structured knowledge, which is essential for building robust Knowledge Graphs. 📢 Stay tuned for my next post on SPARQL Queries for enriching Knowledge Graphs. #RML #YARRRML #JSONtoRDF #KnowledgeGraph

8B parameters SQL code generation model (Llama 3 based) very closed to much bigger models (~ GPT-4-Turbo, Claude-3-Opus) on SQLEval Available with all weights with commercial license https://lnkd.in/gQsA2ChZ

I’ve just released a new tool! 🚀 ✨ This Text-to-SQL agent, built with LlamaIndex workflows, transforms natural language questions into SQL commands, making data access simpler and more secure. With features like intent recognition and automated SQL generation, it’s perfect for users without deep SQL knowledge 💻🔍 ✨ Read the full article here: https://lnkd.in/dtzFRUiv ✨ Explore the code on GitHub: https://lnkd.in/d2WmB_kj #Text2SQL #TextToSQL #SQL #LlamaIndex

The LET statement in SurrealQL enables you to use variables in our query language. This can be used to store results of a subquery. Using LET effectively can help you write more concise, readable, and maintainable queries. Learn more. 👉 https://sdb.li/4gHP9WN

Hannes speaks about the work of Péter Király to develop Shacl4Bib, a custom tool for the validation of library data. - Shapes Constraint Language (SHACL) is a formal language for validating RDF graphs against a set of conditions. Shacl4Bib is a mechanism to define SHACL-like rules for data sources in non-RDF based formats, such as XML, CSV and JSON. Cf. https://lnkd.in/efMijQVg #MDGAGM2024

⚙️ 𝗖𝗿𝗲𝗮𝘁𝗲 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗱𝗮𝘁𝗮𝘀𝗲𝘁 𝘂𝘀𝗶𝗻𝗴 𝘁𝗵𝗲 𝗜𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗼𝗿 ⚙️ Build a dataset for Named Entity Recognition (NER) using schema-based extraction. This helps in performing semantic searches based on the entities. Pydantic, the backbone of Instructor, enables high customization and utilizes return datatype hints for seamless schema validation. It seamlessly integrates with #LanceDB and directly inserts data into tables. 🔨 Code Implementation - https://lnkd.in/gmjepTcg Checkout for related examples and tutorials https://lnkd.in/gWYgJD8z

Biggest open text dataset release of the year! 🚀 SmolTalk is a 1M sample big synthetic dataset that was used to train SmolLM v2. It is available under Apache 2.0 and combines newly generated datasets with publicly available ones! 🧬 TL;DR; 🧩 New datasets: Smol-Magpie-Ultra (400K) for instruction tuning; Smol-contraints (36K) for precise output; Smol-rewrite (50K) & Smol-summarize (100K) for rewriting and summarization. 🤝 Public Dataset Integrations: OpenHermes2.5 (100K), MetaMathQA & NuminaMath-CoT, Self-Oss-Starcoder2-Instruct, LongAlign & SystemChats2.0 🥇 Outperforms the new Orca-AgenInstruct 1M when trained with 1.7B and 7B models 🏆 Outperform models trained on OpenHermes and Magpie Pro on IFEval and MT-Bench 🧪 Used Argilla Distilabel to generate all new synthetic datasets 🤗 Released under Apache 2.0 on Hugging Face 😍 Synthetic generation pipelines and training code released Dataset: https://lnkd.in/e894mUnQ Generation Code: https://lnkd.in/e_PDcrz3 Training Code: https://lnkd.in/eyH6zTXG Lets keep saying it: “Synthetic data is all you need”!

⚙️ 𝗖𝗿𝗲𝗮𝘁𝗲 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗱𝗮𝘁𝗮𝘀𝗲𝘁 𝘂𝘀𝗶𝗻𝗴 𝘁𝗵𝗲 𝗜𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗼𝗿 ⚙️ Build a dataset for Named Entity Recognition (NER) using schema-based extraction. This helps in performing semantic searches based on the entities. Pydantic, the backbone of Instructor, enables high customization and utilizes return datatype hints for seamless schema validation. It seamlessly integrates with LanceDB and directly inserts data into tables. 🔨 Code Implementation - https://lnkd.in/gmjepTcg Checkout for related examples and tutorials https://lnkd.in/gWYgJD8z

Here's a concise overview of the key concepts of GraphQL query language in one great definition: - GraphQL's schema-driven approach defines types and relationships, empowering clients to request specific data through queries. - Clients can modify data using mutations, while fields determine the data to retrieve. - Enhancing query flexibility, arguments, aliases, and fragments play crucial roles, with variables adding dynamism to queries. - Directives enable conditional execution, and introspection allows clients to explore schema structures and capabilities, making GraphQL a powerful and versatile querying language. #GraphQL #QueryLanguage #Schema #Mutations #Directives #Introspection