datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
human-templated-captions-1bcsv delimiter is = ".,|,."
apparently python doesn't like multichar delimiters using the native csv so there's some issues with environments when loading.
This seemed like a good idea to avoid overlapping potential characters, but in practice it turned into additional overhead and bugs. I'll be manually converting the split to parquet and providing a proper file split soon.
Additionally with the parquet will introduce the large caption split; which are considerably longer captions for the… See the full description on the dataset page: https://huggingface.co/datasets/AbstractPhil/human-templated-captions-1b.3M_Academic_Papers_Titles_and_Abstracts
Comprehensive Academic Papers Dataset: 3M+ Research Paper Titles and Abstracts
📋 Overview
This dataset is a comprehensive collection of over 3 million research paper titles and abstracts, curated and consolidated from multiple high-quality academic sources. The dataset provides a unified, clean, and standardized format for researchers, data scientists, and machine learning practitioners working on natural language processing, academic research analysis, and knowledge… See the full description on the dataset page: https://huggingface.co/datasets/beta3/3M_Academic_Papers_Titles_and_Abstracts.econ_paper_abstracts
Dataset Card for Economics Paper Dataset
Dataset Summary
The Economics Research Paper Dataset was designed to support the development of the LLaMA-2-Econ models, with a focus on Title Generation, Abstract Classification, and Question & Answer (Q&A) tasks. It comprises abstracts and titles of economics research papers, along with synthetic Q&A pairs derived from the abstracts, to facilitate training of large language models for economics-specific applications.… See the full description on the dataset page: https://huggingface.co/datasets/onurkeles/econ_paper_abstracts.abstraction-level-stability-v01Cardinal Meta Dataset 3.1Abstraction Level Stability
Purpose
Test whether claims stay at the correct abstraction level
Test whether level changes are named and justified
Test whether concrete cases are not inflated into general truths
Central question
What level is this claim operating at
What this dataset catches
Instance to general jumps
Proxy to property inflation
Model to reality reification
Short term change treated as long term trend
Principle treated as effectiveness… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/abstraction-level-stability-v01.