Datasets:

qninhdt
/

lexi-grader-sft

license: other
task_categories:
  - text-generation
language:
  - en
tags:
  - grammatical-error-correction
  - language-learning
  - distillation
size_categories:
  - 1K<n<10K

qninhdt/lexi-grader-sft

Teacher-generated training data for a sentence grader: given a learner's English sentence and one specific dictionary sense of a target word, produce an inline correction, a meaning band (0-4), and one line of feedback.

This card is generated from the pipeline's own report JSON. Every number below was measured by the run that produced the data.

What is in here

1636 generated learner sentences (raw/raw_texts.parquet)
1578 accepted gradings (raw/raw_labels.parquet)
190 distinct dictionary senses

Splits

Split	Rows
`train`	1225
`val`	179
`test`	174

Grouped by target word, not by row. Sentences appearing in more than one split: 0. Rejected during validation: 0 of 1578.

{
  "correction": "He [speak>speaks:agr] very [eloquent>eloquently:form].",
  "meaning": 3,
  "feedback": "Right sense, but the verb needs to agree with the subject."
}

Operation	Syntax	Example
Replace	`[A>B:tag]`	`[speak>speaks:agr]`
Delete	`[A>:tag]`	`the [the>:art] very`
Insert	`[>B:tag]`	`went [>to the:art] store`

A clean sentence is re-emitted verbatim. An unreadable one yields correction: null. grammar and naturalness are computed from the correction's tags by the formula in band_config.json, not generated by the model — so identical error sets always score identically, and thresholds stay retunable without regenerating anything.

How it was built

Two calls, deliberately separated:

Diversifier — knows a spec (learner profile, target band, error recipe) and writes learner-like text. The spec is a diversity knob and never a label.
Grader — sees only {target, sense, text} and produces the answer. This is byte-for-byte the prompt the student model runs at inference.

Single-call self-labelling was rejected: it produces labels that describe the instruction rather than the text, and the defect is invisible afterwards because a correct row and a wrong one look identical.


Teacher model	`claude-opus-5`
Endpoint	`https://api.vilao.ai/v1`
Call 1 requests	190
Call 2 requests	190
Format validity	0.6947
Batch diversity (distinct-2)	0.9555

The teacher model string is the one the endpoint reported. It was reached through an OpenAI-compatible proxy, so it identifies the endpoint's advertised model rather than an independently verified checkpoint.

Distribution

`meaning`	Rows	Share
0	143	9.1%
1	353	22.4%
2	114	7.2%
3	271	17.2%
4	697	44.2%

Middle bands {1,2,3} hold 0.4394 of rows.

Error tags

Tag	Count
`art`	617
`agr`	428
`tense`	402
`num`	357
`prep`	351
`form`	274
`word`	174
`pron`	112
`other`	96
`order`	90
`punc`	85
`coll`	84
`poss`	48
`unnat`	47
`sp`	39
`part`	25

Quality gates

Gate	Value	Threshold	Blocking	Result
`G1_self_consistency`	0.9679	0.7	yes	pass
`G2_band_coverage`	0.4677	0.4	yes	pass
`G3_format_validity`	0.9645	0.9	no	pass
`G4_batch_diversity`	0.9555	0.7	no	pass
`G5_other_tag_share`	0.0297	0.05	no	pass
`G6_batch_single_parity`	1.0	0.8	no	pass

Overall: pass; blocking gates: pass.

Teacher self-consistency (the ceiling on any student)

Measure	Value
`meaning` QWK	0.967852
`correction` edit-F1	0.857129
Rows re-graded	197

The same sentences were graded twice, blind, with the cache disabled. A student cannot exceed the agreement its teacher has with itself, so report fidelity against these numbers rather than against 1.0.

Limitations

No human gold set. Every label is a teacher's opinion. Nothing here is verified against ground truth.
The real-learner distribution is unverified. Sentences are model-written imitations of learner errors, not collected from learners.
Bands are uncalibrated until lexi data calibrate has run; band_config.json carries "calibrated": false when that is still true.
feedback is unmeasured. No metric in this snapshot evaluates it.
The error mix is skewed relative to human-annotated learner text. Against W&I+LOCNESS train split (18,224 rows, the same 16 tags), this data over-produces agr (13.2% vs 2.8%), num (11.1% vs 4.8%), poss (1.5% vs 0.7%) and under-produces punc (2.6% vs 15.4%), sp (1.2% vs 8.5%), unnat (1.5% vs 9.0%), word (5.4% vs 17.1%). The cause is the learner profiles: their error_bias fields drive what call 1 writes, and punc, sp, pron, poss and other appear in no profile at all. Punctuation and spelling are among the commonest real learner errors, so a model trained on this alone will be weakest there. Full comparison in reports/tag-distribution-reference.json.
grammar and naturalness are uncalibrated. band_config.json carries "calibrated": false, so those two bands come from the shipped design guesses rather than from this corpus's penalty distribution. meaning is unaffected — it is the teacher's own answer. Calibration on this corpus produced duplicate cut points, because 81% of rows carry no usage error at all and no threshold exists inside that mass of zeros; a five-band usage scale is not supported by data this clean.

Not included

The stage-A correction-format data converted from W&I+LOCNESS is not part of this dataset. That corpus's licence forbids redistributing any part of it to a third party, so it stays local to the machine that built it.

Citation

@misc{lexi_grader_dataset,
  title  = {qninhdt/lexi-grader-sft},
  note   = {Teacher-generated sentence-grading dataset},
  year   = {2026},
}