metadata
pretty_name: AMZN SEC Filings – Chunk-level Corpus (10-K, 10-Q, 8-K)
license: mit
language:
- en
tags:
- finance
- sec-edgar
- chromadb
- retrieval
- embeddings
- amzn
task_categories:
- text-retrieval
- question-answering
size_categories:
- 100K<n<1M
source_datasets:
- external:sec-edgar
AMZN SEC Filings – Chunk-level Corpus (10-K, 10-Q, 8-K)
A ready-to-use, chunk-level corpus of Amazon's (AMZN) recent SEC filings
(10-K, 10-Q, and 8-K).
Each paragraph and sentence is stored together with rich metadata,
making the dataset ideal for:
- semantic search / RAG pipelines (ChromaDB, FAISS, Weaviate, …)
- question-answering over financial filings
- experimenting with financial-domain embeddings
Time span : last 5 fiscal years (rolling window, as of 2025-05-11)
Collection : 10-K, 10-Q, 8-K (including MD&A summaries & optional exhibits)
Granularity: ~1000-char paragraphs and ≤80-token sentences
Contents
- Dataset structure
- Download & usage
- Creation process
- Intended use
- Limitations & ethical considerations
- Citation
- License
- Contact
Dataset structure
The data are stored in Arrow (the native format of 🤗 datasets)
so they can be accessed in-memory or streamed on-the-fly.
| Column | Type | Description |
|---|---|---|
text |
str | Plain-text chunk (paragraph or sentence) |
metadata |
dict | All fields defined in FilingMetadata (ticker, cik, filing_type, …) |
id |
str | SHA-1 hash → unique, deterministic identifier |
chunk_type* |
str | Paragraph / sentence / summary / exhibit / press_release |
*chunk_type is embedded inside metadata.
Total size: ≈ 200-400 k chunks (depends on new filings).
Example metadata object
{
"ticker": "AMZN",
"cik": "0001018724",
"company_name": "AMAZON.COM, INC.",
"filing_type": "10-K",
"filing_date": "2025-02-02",
"filing_period": "2024-12-31",
"filing_url": "https://www.sec.gov/Archives/...",
"section_id": "item7",
"section_title": "Management’s Discussion and Analysis",
"section_level": 1,
"chunk_index": 3,
"chunk_count": 42,
"chunk_type": "paragraph"
}