datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Causal-Intervention-Tests-For-Explanation-Faithfulness
Faithfulness via Causal Interventions — Evaluation Pipeline
Paper: What Does Answer Change Rate Actually Measure? A Specificity Audit
of Causal Intervention Tests for Explanation Faithfulness
Accepted at: EMNLP 2026 Workshop GroundLM, Budapest, Hungary (emnlp.org)
This pipeline implements the causal-intervention evaluation for LLM
explanation faithfulness described in the accompanying paper, including two
controls: a content-free specificity check and a decoding-noise floor.… See the full description on the dataset page: https://huggingface.co/datasets/durgesh-rao/Causal-Intervention-Tests-For-Explanation-Faithfulness.testset
Dataset Card for TreeOfLife-10M Captions
This dataset consists of generated captions, Wikipedia-derived descriptions and format examples for the TreeOfLife-10M. These captions were generated using InternVL3-38B based on biological contexts that help the model generate more accurate captions. It was used to train BioCAP, a CLIP-based model.
Dataset Details
This dataset is comprised of captions for the images in TreeOfLife-10M that were generated using InternVL3 38B.… See the full description on the dataset page: https://huggingface.co/datasets/ZihengZ/testset.EnDev_RAGAS_testset
EnDev RAGAS Test Set
Synthetic Q&A test set (42 pairs) generated with RAGAS
(TestsetGenerator.generate_with_chunks) over chunks sampled from the EnDev corpus
stored in Qdrant collection endev-bgem3-512 (Gradio-gateway Space
GIZ/EnDev_Qdrant).
Generated: 2026-09-14 UTC
Generator/judge LLM: Qwen/Qwen3-235B-A22B-Instruct-2507
Embeddings: BGE-M3 via the EnDev TEI Inference Endpoint
Columns: user_input, reference, reference_contexts, synthesizer_name
Used to evaluate the deployed… See the full description on the dataset page: https://huggingface.co/datasets/GIZ/EnDev_RAGAS_testset.gdelt-rag-golden-testset-v2
GDELT RAG Golden Test Set
Dataset Description
This dataset contains a curated set of question-answering pairs designed for evaluating RAG (Retrieval-Augmented Generation)
systems focused on GDELT (Global Database of Events, Language, and Tone) analysis. The dataset was generated using the
RAGAS framework for synthetic test data generation.
Dataset Summary
Total Examples: 12 QA pairs
Purpose: RAG system evaluation
Framework: RAGAS (Retrieval-Augmented… See the full description on the dataset page: https://huggingface.co/datasets/dwb2023/gdelt-rag-golden-testset-v2.gdelt-rag-golden-testset-v3
GDELT RAG Golden Test Set
Dataset Description
This dataset contains a curated set of question-answering pairs designed for evaluating RAG (Retrieval-Augmented Generation)
systems focused on GDELT (Global Database of Events, Language, and Tone) analysis. The dataset was generated using the
RAGAS framework for synthetic test data generation.
Dataset Summary
Total Examples: 12 QA pairs
Purpose: RAG system evaluation
Framework: RAGAS (Retrieval-Augmented… See the full description on the dataset page: https://huggingface.co/datasets/dwb2023/gdelt-rag-golden-testset-v3.gdelt-rag-golden-testset
GDELT RAG Golden Test Set
Dataset Description
This dataset contains a curated set of question-answering pairs designed for evaluating RAG (Retrieval-Augmented Generation)
systems focused on GDELT (Global Database of Events, Language, and Tone) analysis. The dataset was generated using the
RAGAS framework for synthetic test data generation.
Dataset Summary
Total Examples: 12 QA pairs
Purpose: RAG system evaluation
Framework: RAGAS (Retrieval-Augmented… See the full description on the dataset page: https://huggingface.co/datasets/dwb2023/gdelt-rag-golden-testset.gdelt-rag-golden-testset-v4
GDELT RAG Golden Test Set
Dataset Description
This dataset contains a curated set of question-answering pairs designed for evaluating RAG (Retrieval-Augmented Generation)
systems focused on GDELT (Global Database of Events, Language, and Tone) analysis. The dataset was generated using the
RAGAS framework for synthetic test data generation.
Dataset Summary
Total Examples: 12 QA pairs
Purpose: RAG system evaluation
Framework: RAGAS (Retrieval-Augmented… See the full description on the dataset page: https://huggingface.co/datasets/dwb2023/gdelt-rag-golden-testset-v4.ragas-golden-testset-personas
Dataset Card for ragas-golden-testset-personas
Dataset Description
The RAGAS Golden Dataset is a synthetically generated question-answering dataset designed for evaluating Retrieval Augmented Generation (RAG) systems. It contains high-quality question-answer pairs derived from academic papers on AI agents and agentic AI architectures.
Dataset Summary
This dataset was generated using the RAGAS TestsetGenerator framework, which creates synthetic questions… See the full description on the dataset page: https://huggingface.co/datasets/dwb2023/ragas-golden-testset-personas.biosciences-golden-testset
Biosciences RAG Golden Test Set
Dataset Description
This dataset contains 12 question-answering pairs for evaluating RAG systems on biomedical research topics. The QA pairs were synthetically generated using the RAGAS framework from 140 source documents spanning knowledge graphs, LLM applications in biomedicine, protein interaction databases, and gene-to-phenotype mapping.
Dataset Summary
Total Examples: 12 QA pairs
Purpose: RAG system evaluation ground truth… See the full description on the dataset page: https://huggingface.co/datasets/open-biosciences/biosciences-golden-testset.test-synthetic-dataset
Dataset Card for test-synthetic-dataset
This dataset has been created with distilabel.
Dataset Summary
This dataset contains a pipeline.yaml which can be used to reproduce the pipeline that generated it in distilabel using the distilabel CLI:
distilabel pipeline run --config "https://huggingface.co/datasets/MJannik/test-synthetic-dataset/raw/main/pipeline.yaml"
or explore the configuration:
distilabel pipeline info --config… See the full description on the dataset page: https://huggingface.co/datasets/MJannik/test-synthetic-dataset.testsetcvpr2019_5papers_testset_12q