datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
bfsi-bench
BFSI-Bench
BFSI-Bench is a benchmark for testing how well language models answer questions about India’s banking, financial services, and insurance (BFSI) rules.
In this domain, the correct answer often depends on circulars and regulations that change frequently, and the official sources (sites like RBI, SEBI, and IRDAI) can be hard to find, parse, and keep current. BFSI-Bench measures five capability areas:
Jurisdiction-Aware Compliance: Disambiguate to the Indian context, or… See the full description on the dataset page: https://huggingface.co/datasets/ground-truth/bfsi-bench.StockSensei_Ground_Truth
Financial Advice Finetuning Ground Truth Dataset
Georgia Institute of Technology, College of Computing
Authors: Hersh Dhillon, Mathan Mahendran, Will Ferguson, Ayushi Mathur, Dorsa Ajami
December 2024
Motivation
Given the unprecendented rise of day trading, social-media based financial advice, and trading apps, more people then ever are buying and selling stocks
without proper financial literacy. Oftentimes, people make high-risk trades with little more quantitative… See the full description on the dataset page: https://huggingface.co/datasets/iamwillferguson/StockSensei_Ground_Truth.ground-truth-ob
Ground Truth OB
This repository contains ground_truth_kb.csv, a tabular ground-truth or knowledge-base resource. The current repository is deliberately small and contains no executable training or evaluation script.
Recommended use
Load the CSV, inspect its column names and encoding, validate identifiers and labels, and record the provenance of every ground-truth field before joining it with model outputs. Keep an immutable copy of the raw file and create derived… See the full description on the dataset page: https://huggingface.co/datasets/MR-CODESPIKE/ground-truth-ob.clinical-structural-similarity-scoring-against-ground-truth-v0.1What this dataset tests
Whether a model can match the later-discovered explanationby structural logic, not by diagnosis label.
Input
pre-explanation case summary and data
predicted structure
ground truth structure
Required outputs
structural_similarity_score_0_100
alignment_strengths
divergence_points
Representation format
Predicted and ground truth structures use this schema text
systems A B C
nodes n1 n2 n3
edges n1->n2 n2->n3
phases p1 p2 p3
failure_modes f1 f2
Typical… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-structural-similarity-scoring-against-ground-truth-v0.1.Gavin_yiddish_raw_HTR_and_groundtruth_paragraphsraw-htr-handchecked-groundtruth-smallwhisper_8_avg_ground_truth_scores_2columnstcga-paad-ground-truth