Datasets:

Jayan12321
/

rag-evaluation-benchmark

---
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- n<1K
tags:
- rag
- retrieval-augmented-generation
- evaluation
- benchmark
- question-answering
- faithfulness
pretty_name: RAG Evaluation Benchmark & QA Test Suite
---
# RAG Evaluation Benchmark & QA Test Suite 📚

A curated ground-truth evaluation benchmark designed to assess Retrieval-Augmented Generation (RAG) pipelines across three primary dimensions:
1. **Context Recall**: Did the retriever fetch the passage containing the necessary ground-truth facts?
2. **Faithfulness**: Is the generated answer supported strictly by the retrieved evidence without hallucinated extrapolations?
3. **Answer Relevancy**: Does the generated synthesis directly answer the question?

- **Associated Space**: [RAG Engine Architecture Showcase](https://huggingface.co/spaces/Jayan12321/rag-engine)
- **GitHub Repository**: [JayanGupta/RAG-Engine](https://github.com/JayanGupta/RAG-Engine)
- **Author**: [Jayan Gupta](https://huggingface.co/Jayan12321)

---

## 📊 Dataset Schema

| Field | Type | Description |
|---|---|---|
| `question_id` | string | Unique identifier for the evaluation item. |
| `question` | string | User query spanning technical domains (quantum, space, clean energy). |
| `ground_truth_answer` | string | Verified, objective factual answer derived from the source knowledge corpus. |
| `evidence_doc` | string | Title of the source document containing the authoritative facts. |
| `question_type` | string | Type of cognitive task: `factoid`, `synthesis`, or `comparative`. |

---

## 💻 Quickstart with Python

```python
from datasets import load_dataset
# Load directly from Hugging Face
dataset = load_dataset("Jayan12321/rag-evaluation-benchmark")
# Inspect sample evaluation question
sample = dataset["train"][0]
print("Question:", sample["question"])
print("Ground Truth:", sample["ground_truth_answer"])
print("Evidence Document:", sample["evidence_doc"])
```

---

## 🛠️ Usage in RAG Benchmarking
This dataset is designed to be paired with heuristic faithfulness checkers or RAGAS-style evaluation frameworks to objectively score retriever and generator configurations.

	---
	language:
	- en
	license:
	- mit
	multilinguality:
	- monolingual
	size_categories:
	- n<1K
	tags:
	- rag
	- retrieval-augmented-generation
	- evaluation
	- benchmark
	- question-answering
	- faithfulness
	pretty_name: RAG Evaluation Benchmark & QA Test Suite
	---

	# RAG Evaluation Benchmark & QA Test Suite 📚

	A curated ground-truth evaluation benchmark designed to assess Retrieval-Augmented Generation (RAG) pipelines across three primary dimensions:
	1. Context Recall: Did the retriever fetch the passage containing the necessary ground-truth facts?
	2. Faithfulness: Is the generated answer supported strictly by the retrieved evidence without hallucinated extrapolations?
	3. Answer Relevancy: Does the generated synthesis directly answer the question?

	- Associated Space: [RAG Engine Architecture Showcase](https://huggingface.co/spaces/Jayan12321/rag-engine)
	- GitHub Repository: [JayanGupta/RAG-Engine](https://github.com/JayanGupta/RAG-Engine)
	- Author: [Jayan Gupta](https://huggingface.co/Jayan12321)

	---

	## 📊 Dataset Schema

	\| Field \| Type \| Description \|
	\|---\|---\|---\|
	\| `question_id` \| string \| Unique identifier for the evaluation item. \|
	\| `question` \| string \| User query spanning technical domains (quantum, space, clean energy). \|
	\| `ground_truth_answer` \| string \| Verified, objective factual answer derived from the source knowledge corpus. \|
	\| `evidence_doc` \| string \| Title of the source document containing the authoritative facts. \|
	\| `question_type` \| string \| Type of cognitive task: `factoid`, `synthesis`, or `comparative`. \|

	---

	## 💻 Quickstart with Python

	```python
	from datasets import load_dataset

	# Load directly from Hugging Face
	dataset = load_dataset("Jayan12321/rag-evaluation-benchmark")

	# Inspect sample evaluation question
	sample = dataset["train"][0]
	print("Question:", sample["question"])
	print("Ground Truth:", sample["ground_truth_answer"])
	print("Evidence Document:", sample["evidence_doc"])
	```

	---

	## 🛠️ Usage in RAG Benchmarking
	This dataset is designed to be paired with heuristic faithfulness checkers or RAGAS-style evaluation frameworks to objectively score retriever and generator configurations.