SyGra - Graph-oriented Synthetic data generation Pipeline
An open-source collection of datasets, guides, and rankings for B2B email marketing and lead generation. Your go-to resource for sales prospecting strategies.
A complete BattleScribe and New Recruit data repository for Warhammer Fantasy Battles 6th Edition. Includes all official and experimental rules, FAQs, and community contributions.
Markdown mirror of the MinistryPlatform Help Center (help.acst.com) for querying by AI coding assistants via Context7 MCP. Content © ACS Technologies, mirrored with permission.
Following is the Open Email Marketing Dataset; you can use it without any restrictions.
(EXPERIMENTAL) Evidence-grounded data filter that checks PubMed claims against scientific evidence to curate medical AI training data.
BIVREST — a group verification protocol for role-based behavior. "Believe/Don't Believe" voting with structured logging of actions and group decisions. A data collection method for behavioral research.
Machine-readable dataset for public Department of War / PURSUE UFO-UAP Release 01 records.
A DOI-targeted Q&A corpus encoding the documented judgment of the shimo4228 research program (AKC, Contemplative Agent, AAP, Authorship Strategy) as bilingual (EN+JA) training data. Operational form of Authorship Strategy Layer 4 tactic 7 (LLM-first ingest).
🔧 Modular pipeline for generating high-quality, domain-specific datasets for LLM fine-tuning — from PDFs and web scraping to synthetic Q&A generation, quality filtering, and training-ready formatting.
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Automatic URL, document, and stdin to Markdown converter with OCR and metadata
👨🏫This project was developed under the guidance of Mr. Lokesh Sir as part of the AI & ML Training Program. It explores LLM integration using Google Gemini APIs with a custom UI built on Streamlit.
Sample edition of The Stack Enriched: annotated, secure, and optimized code dataset, this is a sample version
Archive the first official U.S. Department of War UAP declassification, containing 162 verified technical files, photographs, and videos.
Stratified LLM Subsets delivers diverse training data at 100K-1M scales across pre-training (FineWeb-Edu, Proof-Pile-2), instruction-following (Tulu-3, Orca AgentInstruct), and reasoning distillation (Llama-Nemotron). Embedding-based k-means clustering ensures maximum diversity across 5 high-quality open datasets.
High-throughput Q&A data synthesis pipeline for training LLMs. Self-hosted, "SOTA", open-source — generate 7.2K+ validated pairs/day
High quality dataset containing ~2k lines of synthetically generated LLM training examples for spontaneous observations
Claude/agent skill: build LLM training data from YouTube transcripts in bulk (Apify)
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