harukicoder/hsk30-graded-readers

HSK 3.0 Graded Reader Corpus

132 word-aligned Chinese graded readers with per-word pinyin and English gloss, arranged on six difficulty shelves: 102 texts in the main split and a disjoint 30-text held-out split.

Aligned Chinese graded-reader corpora are scarce. Existing collections are unaligned plain text, locked inside commercial apps, or graded against HSK 2.0, which has been superseded twice.

Loading

from datasets import load_dataset

ds = load_dataset("harukicoder/hsk30-graded-readers", "main")
held = load_dataset("harukicoder/hsk30-graded-readers", "heldout")

Each record has id, shelf, title (hanzi / pinyin / English), text, and sentences — each sentence carrying its own English translation and a list of word tokens with hz, py and en.

Difficulty labels are deliberately not included

A level is a function of the text and the standard, and "HSK 3.0" names two different official documents that disagree on 41.5% of shared vocabulary. Baking labels in would let a stale copy of this dataset contradict the grader. Compute them instead:

import hsk30                      # pip install hsk30

tokens = [w for s in record["sentences"] for w in s["words"]]
hsk30.grade_tokens(tokens).label              # 2026 exam syllabus
hsk30.grade_tokens(tokens, standard="2021").label   # 2021 grading standard

Roughly half of these texts grade differently under the two documents.

Provenance, stated plainly

The passages were drafted with large-language-model assistance and then reviewed, edited, re-levelled and in several cases rewritten by the author. They are pedagogical material written to a level target — not naturally occurring Chinese — and should not be used as a sample of native usage, or to train a general language model.

Because they are LLM-assisted, a system evaluated on how well it matches this corpus may be rewarded for resembling its own training distribution. The accompanying benchmark avoids this by scoring against the official character standard rather than against these texts.

Human authors writing to an explicit level target hit it only 61.8% of the time across this corpus, overshooting on easy material and undershooting on hard. Treat the shelf labels as bands, not point claims.

Full datasheet (Gebru et al. format): see DATASHEET.md in the repository.

Citation

@misc{serrano2026hsk30corpus,
  title  = {HSK 3.0 Graded Reader Corpus},
  author = {Serrano, Alvaro},
  year   = {2026},
  note   = {CC BY 4.0},
  url    = {https://github.com/harukicoder/hsk30}
}