README.md

---
license: cc-by-4.0
task_categories:
- question-answering
- text-retrieval
language:
- en
tags:
- chunking
- scientific
- academic-papers
- nlp
- qasper
- rag
- retrieval
size_categories:
- 1K<n<10K
configs:
- config_name: corpus
  data_files:
  - split: train
    path: corpus/train-*
- config_name: questions
  data_files:
  - split: train
    path: questions/train-*
dataset_info:
- config_name: corpus
  features:
  - name: id
    dtype: string
  - name: title
    dtype: string
  - name: text
    dtype: string
  - name: num_sections
    dtype: int64
  splits:
  - name: train
    num_bytes: 6489700
    num_examples: 243
  download_size: 3222047
  dataset_size: 6489700
- config_name: questions
  features:
  - name: id
    dtype: string
  - name: paper_id
    dtype: string
  - name: question
    dtype: string
  - name: answer
    dtype: string
  - name: chunk-must-contain
    dtype: string
  splits:
  - name: train
    num_bytes: 985455
    num_examples: 1507
  download_size: 476865
  dataset_size: 985455
---

<div align="center">

# 🍵 Sencha: Scientific Paper Chunking Assessment

**S**ci**en**tific **Cha**llenges - A dataset for evaluating chunking algorithms on academic papers.

</div>

## Overview

Sencha is designed to test how well chunking algorithms handle **long-form scientific documents**. It contains full-text NLP research papers with questions that require finding specific information across multiple sections.

### Key Challenges

- Handling structured sections (Abstract, Methods, Results, etc.)
- Preserving citation context (BIBREF tags)
- Managing hierarchical section headers
- Chunking technical content with equations and terminology

## Dataset Structure

### Corpus

The `corpus` config contains 250 full-text NLP papers.

| Column | Type | Description |
|--------|------|-------------|
| `id` | string | ArXiv paper ID |
| `title` | string | Paper title |
| `text` | string | Full paper text in markdown format |
| `num_sections` | int | Number of sections in the paper |

### Questions

The `questions` config contains 1,146 questions about paper content.

| Column | Type | Description |
|--------|------|-------------|
| `id` | string | Unique question identifier |
| `paper_id` | string | Reference to corpus document (ArXiv ID) |
| `question` | string | Question about the paper content |
| `answer` | string | Answer to the question |
| `chunk-must-contain` | string | Evidence passage that answers the question |

## Statistics

| Metric | Value |
|--------|-------|
| Papers | 250 |
| Questions | 1,146 |
| Avg paper length | ~26,400 chars (~5,300 words) |
| Min paper length | ~5,600 chars |
| Max paper length | ~98,500 chars |
| Avg must-contain length | 613 chars |
| Domain | NLP/Computational Linguistics |

## Usage

```python
from datasets import load_dataset

# Load the corpus
corpus = load_dataset("chonkie-ai/sencha", "corpus", split="train")

# Load the questions
questions = load_dataset("chonkie-ai/sencha", "questions", split="train")

# Use with MTCB evaluator
from mtcb import SenchaEvaluator
from chonkie import RecursiveChunker

evaluator = SenchaEvaluator(
    chunker=RecursiveChunker(chunk_size=512),
    embedding_model="voyage-3-large"
)
result = evaluator.evaluate(k=[1, 3, 5, 10])
```

## Sample Topics

The papers cover various NLP topics including:
- Sentiment analysis and affective computing
- Word embeddings and language models
- Text classification and NER
- Question answering systems
- Machine translation
- Social media analysis
- Clinical NLP

## Source

Derived from [QASPER](https://allenai.org/data/qasper) (NAACL 2021) by Allen AI - a dataset for question answering on scientific research papers.

## License

CC-BY-4.0 (following QASPER license)