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

• 12454 generated learner sentences (raw/raw_texts.parquet)
• 12103 accepted gradings (raw/raw_labels.parquet)
• 2000 distinct dictionary senses

Splits

Split	Rows
train	9545
val	1251
test	1307

Grouped by target word, not by row. Sentences appearing in more than one split: 0. Rejected during validation: 0 of 12103.

{
  "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:

1. 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.
2. 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	zm1/zyloo/gemini-3.1-pro-preview
Endpoint	https://api.vilao.ai/v1
Call 1 requests	6
Call 2 requests	6118
Format validity	0.9475
Batch diversity (distinct-2)	0.9367

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	2017	16.7%
1	2611	21.6%
2	128	1.1%
3	2281	18.8%
4	5066	41.9%

Middle bands {1,2,3} hold 0.4269 of rows.

Error tags

Tag	Count
art	4317
prep	3018
agr	2202
tense	2110
word	2055
form	1905
num	1800
order	1355
unnat	865
coll	737
pron	729
poss	339
sp	276
punc	244
part	162
other	58

Quality gates

The pilot gate has not been run against this snapshot.

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.
• 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},
}