dividend
Datasets
All datasets matching “dividend”liars-dividend-GPT-image-2-photorealliars-dividend-flux2-images
Liars Dividend FLUX.2 Images
100 prompt-matched synthetic images generated with black-forest-labs/FLUX.2-dev. Images are stored under FLUX.2/fake/.
Generation settings: seed 42, 1024x1024, 50 inference steps, guidance scale 4.0.
schd-dividend-payments-and-holdings
SCHD Dividend Payments and Dated Holdings
Two small, source-attributed historical tables for learning reproducible dividend and portfolio-exposure calculations.
The worked explanations accompany DividendSteps.
Contents
Configuration
Records
Observation scope
payments
40
Ex-dividend dates September 19, 2016 through June 24, 2026; original source-check date September 9, 2026.
holdings
102
September 8, 2026 disclosure, including cash, money-market and… See the full description on the dataset page: https://huggingface.co/datasets/Holaclea/schd-dividend-payments-and-holdings.unpredictable_dividend-comThe UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card.NEPSE_Dividend_FAQ
📈 NEPSE Dividend FAQ (Nepali) — Dataset README
"How much dividend did it give this time?" — the question every stock market watcher asks, now turned into 2,000 question-answer pairs.
A simple, clean, 100% Nepali-language financial FAQ dataset that builds question-answer (Q&A) pairs about the total dividend and bonus share dividend of companies listed on NEPSE. This file is a full dissection of that exact dataset — every field, every pattern, everything.
🔖 TL;DR… See the full description on the dataset page: https://huggingface.co/datasets/sabin1234/NEPSE_Dividend_FAQ.daily-paper-2026-09-23-validator-dividend-tool-call-consensus
The Validator's Dividend: Measuring the Cost-Quality Frontier of Validation-Gated Resampling versus k-Sample Consensus for Agentic Tool-Call Output on Self-Hosted H200
TL;DR — On a self-hosted quantized model, a free deterministic validator is the cheapest tool-call reliability lever: validate-then-retry recovers the detectable error fraction at marginal cost c/q per quality point, is bounded by a silent floor that no retry budget can cross, and beats both k-sample consensus… See the full description on the dataset page: https://huggingface.co/datasets/thaki-AI/daily-paper-2026-09-23-validator-dividend-tool-call-consensus.