Datasets:
id stringlengths 20 20 | title stringlengths 3 120 | text stringlengths 2 1.16k |
|---|---|---|
000014fc76936158fd11 | Expense: Definition, Types, and How Expenses Are Recorded | Capital expenditures, also known as CapEx, are funds used by a company for acquiring, upgrading, and maintaining physical assets. They differ from other business expenses as the IRS requires capital expenses to be capitalized and written off slowly over time, while most other costs can be expensed or written off agains... |
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Investopedia — Training, unified schema
A seeded sample of FinLang/investopedia-embedding-dataset, made into retrieval training pairs and reshaped into the strict schema shared by every dataset in this collection. One of the 15 domain sources (code, medical, science, finance, legal) added to the collection's general sources.
| Source | FinLang/investopedia-embedding-dataset @ c29a3a95a0b0 |
| Task | finance question → answer passage |
| Domain · languages | finance · eng |
| Queries / documents / qrels | 60,000 / 59,345 / 60,000 |
| Qrels per query | min 1 · mean 1.0 · max 1 |
| Score values | 2 ×60,000 (2: the first positive, 1: any other) |
| Layout | queries · corpus · qrels · hard-negatives · teacher-scores, split train |
| Splits | corpus: train · hard-negatives: train · qrels: train · queries: train · teacher-scores: train |
| Hard negatives | sources: dense · 5,946,942 rows |
| Teacher scores | none yet (0 rows): jinaai/jina-reranker-v3.5 scores come next |
| Ids | sha1(text)[:20]; identical texts collapse to one document / query |
| License | cc-by-nc-4.0 |
Schema
| config | columns | rules |
|---|---|---|
queries |
id: string, text: string |
ids unique and non-empty; every query has ≥ 1 qrel |
corpus |
id: string, title: string, text: string |
title is always present ("" when the source has none) |
qrels |
query-id: string, corpus-id: string, score: int32 |
referential integrity to both tables; no duplicate pairs; no floats |
hard-negatives |
query-id: string, corpus-id: string, rank: int32, source: string |
one row per negative; (query-id, corpus-id, source) unique; never a labelled positive of the same query |
teacher-scores |
query-id: string, corpus-id: string, teacher: string, score: float32 |
one row per scored pair (positives included); a row means scored — never a placeholder |
Files are Parquet, sorted by id, zstd-compressed, sharded at 500 MB. Every rule above is checked before publishing; provenance.json records the source revision, what changed, and the output file hashes.
What changed from the source
- sampled: a seeded random sample (seed 1) of up to 60,000 pairs
- reshaped: the question is the query, the answer passage the document, with the article title (HTML removed) as its title
- filtered: a leading "In / According to / Based on the (given / provided) passage / text / context," was removed from the questions and the first letter capitalised
- decontaminated (exact): a pair was dropped when its normalised query equals any evaluation query, or a positive equals a document of a test or dev corpus; a repeated query keeps its first pair
- decontaminated (near-duplicates): 0 passages and 0 queries that nearly copy a text of an evaluation set (word 13-grams for passages, 8-grams for queries; at least half shared with one text of the 23 test corpora (BEIR, RTEB, LitSearch) and the 3 dev corpora) were removed, and with them 0 queries in total
- text: leading and trailing whitespace stripped; otherwise as converted above
- ids re-keyed to
sha1(text)[:20]: 0 documents and 0 queries collapsed into identical texts
Hard negatives and teacher scores
Filled by the collection's annotation pipeline (annotation=jina35). Interim: the candidates are final, the teacher scores are still to come.
- Candidates: dense retrieval with
jinaai/jina-embeddings-v5-text-smallover this corpus to depth 1,000; 100 candidates per query drawn from the rank windows 1–30 (30), 31–100 (30), 101–300 (20), 301–1000 (20), the query's labelled positives excluded.rankis the dense rank;sourceisdensefor a mined row anddatasetfor a negative the source labels itself. - Teacher scores: none yet.
teacher-scoresholds 0 rows until thejinaai/jina-reranker-v3.5scores (listwise, as in the other repositories) are filled in;datasetscannot return a 0-example split, so read that file withpyarrow/pandasmeanwhile. The candidates stay.
| configs | queries | hard negatives | teacher scores |
|---|---|---|---|
hard-negatives · teacher-scores |
60,000 (all) | 5,946,942 (5,946,942 dense) | 0 |
Load it
from datasets import load_dataset
queries = load_dataset("Hyukkyu/train-investopedia", "queries", split="train")
corpus = load_dataset("Hyukkyu/train-investopedia", "corpus", split="train")
qrels = load_dataset("Hyukkyu/train-investopedia", "qrels", split="train")
negatives = load_dataset("Hyukkyu/train-investopedia", "hard-negatives", split="train")
scores = load_dataset("Hyukkyu/train-investopedia", "teacher-scores", split="train")
License and attribution
The data is redistributed under the source's terms — cc-by-nc-4.0. All credit belongs to the original authors; see the source repository (https://huggingface.co/datasets/FinLang/investopedia-embedding-dataset). This repository is an independent repackaging.
Non-commercial. The source is CC BY-NC 4.0, and so is this copy.
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