Add comprehensive README with tags and documentation

README.md

@@ -1,39 +1,20 @@
 ---
-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: 6640173
-    num_examples: 250
-  download_size: 3308998
-  dataset_size: 6640173
-- 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: 941967
-    num_examples: 1146
-  download_size: 445065
-  dataset_size: 941967
+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:
@@ -44,3 +25,100 @@ configs:
   - split: train
     path: questions/train-*
 ---
+
+<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)