---
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

- **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.

```json
{
  "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

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