datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
opengloss-v2.0-qa-pairs
Superseded by OpenGloss v2.1 (2026-09-07): 109,633 lexemes and 250,003 live senses — twice this release's coverage — plus a new opengloss-v2.1-inflections form→lemma lookup. v2.0 stays published for reproducibility.
OpenGloss v2.0 — QA Pairs
Question/answer pairs written per sense and answerable only from that sense's own stored text — its gloss, its examples, its entry's encyclopedia article and etymology — with every source labelled by an id the answer has to cite. Uncited… See the full description on the dataset page: https://huggingface.co/datasets/mjbommar/opengloss-v2.0-qa-pairs.msmarco-v2.1-snowflake-arctic-embed-m-v2.0
Snowflake Arctic Embed M V2.0 Embeddings for MSMARCO V2.1 for TREC-RAG
This dataset contains the embeddings for the MSMARCO-V2.1 dataset which is used as the corpora for TREC RAG
All embeddings are created using Snowflake's Arctic Embed M v2.0 and are intended to serve as a simple baseline for dense retrieval-based methods.
Note, that the embeddings are not normalized so you will need to normalize them before usage.
Retrieval Performance
Retrieval performance for… See the full description on the dataset page: https://huggingface.co/datasets/Snowflake/msmarco-v2.1-snowflake-arctic-embed-m-v2.0.RobloxQA-v2.0
RobloxQA V2.0
RobloxQA is a multiple-choice question-answer dataset covering the Roblox engine and the Luau language. It is designed to evaluate a model's knowledge and understanding of Roblox game development, and can also be used as training data.
Every question is written to test general, transferable Roblox/Luau knowledge: API/class/property/method/event behavior, language semantics, types, constraints, concepts, and best practices.
Each question is fully self-contained… See the full description on the dataset page: https://huggingface.co/datasets/TorpedoSoftware/RobloxQA-v2.0.opengloss-v2.0-senses
Superseded by OpenGloss v2.1 (2026-09-07): 109,633 lexemes and 250,003 live senses — twice this release's coverage — plus a new opengloss-v2.1-inflections form→lemma lookup. v2.0 stays published for reproducibility.
OpenGloss v2.0 — Senses
The sense-level view of OpenGloss v2.0 and the repo most consumers want: one row per live sense, with its canonical gloss, its eight reading-level and register renditions, its sense-tagged example sentences with headword character spans, its… See the full description on the dataset page: https://huggingface.co/datasets/mjbommar/opengloss-v2.0-senses.hyperion-v2.0
Hyperion v2.0
Introduction
Hyperion is a comprehensive question answering and conversational dataset designed to promote advancements in AI research with a particular emphasis on reasoning and understanding in scientific domains such as science, medicine, mathematics, and computer science. It integrates data from a wide array of datasets, facilitating the development of models capable of handling complex inquiries and instructions.
Dataset Description
Hyperion… See the full description on the dataset page: https://huggingface.co/datasets/Locutusque/hyperion-v2.0.newsqa_200_11064_v2.0.0
Private NewsQA RAG Evaluation Dataset
Private, human-reviewed evaluation source data for the NewsQA RAG project.
This repository is not a prebuilt retrieval index. Chunk, BM25, Chroma, and
ground-truth chunk mappings must be rebuilt from the pinned release.
Version: v1.0.0
Evaluation articles: 200
Distractor articles: 10864
Source questions: 1340
Redistribution rights for the upstream NewsQA-derived text must be verified
before changing this repository from private to public.
squad-nl-v2.0
SQuAD-NL v2.0 [translated SQuAD / XQuAD]
SQuAD-NL v2.0 is a translation of The Stanford Question Answering Dataset (SQuAD) v2.0.
Since the original English SQuAD test data is not public, we reserve the same documents that were used for XQuAD for testing purposes. These documents are sampled from the original dev data split. The English data is automatically translated using Google Translate (February 2023) and the test data is manually post-edited.
This version of SQuAD-NL also… See the full description on the dataset page: https://huggingface.co/datasets/GroNLP/squad-nl-v2.0.squad_v2.0ChatML-hercules-v2.0Locutusque/hercules-v2.0 in ChatML format.
Python code used for conversion:
from datasets import load_dataset
import pandas
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
pretrained_model_name_or_path="Felladrin/Llama-160M-Chat-v1"
)
dataset = load_dataset("Locutusque/hercules-v2.0", split="train")
def format(columns):
messages = []
conversation = columns["conversations"]
for i in range(len(conversation)):
message =… See the full description on the dataset page: https://huggingface.co/datasets/Felladrin/ChatML-hercules-v2.0.squad-nl-v2.0
SQuAD-NL v2.0 for Sentence Transformers
The SQuAD-NL v2.0 dataset (on Hugging Face: GroNLP/squad-nl-v2.0), modified for use in Sentence Transformers as a dataset of type "Pair with Similarity Score".
Score
We added an extra column score to the original dataset.
The value of score is 1.0 if the question has an answer in the context (no matter where), and 0.0 if there are no answers in the context.
The allows the evaluation of embedding models that aim to pair queries… See the full description on the dataset page: https://huggingface.co/datasets/NetherlandsForensicInstitute/squad-nl-v2.0.