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bea2026-gec-adapters
synterr-nlp/bea2026-gec-adapters
bea2026-gec-adapters is a machine learning model from synterr-nlp. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft.
LoRA adapters from "What Aggregate Scores Hide: Per-Rule Evaluation of Russian Grammatical Error Correction" (BEA 2026). One repository, 29 adapters across 8 open base models × up to 4 training regimes, for Russian gr…
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Updated Jul 1, 2026
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At a glance
- Library
- peft
- Access
- Public
- Created
- Jul 1, 2026
- Updated
- Jul 1, 2026
- SHA
- 95aa4d62
Base models
Model card
From the Hugging Face model README
BEA 2026 — Russian GEC LoRA Adapters (SyntErr)
LoRA adapters from "What Aggregate Scores Hide: Per-Rule Evaluation of Russian Grammatical Error Correction" (BEA 2026). One repository, 29 adapters across 8 open base models × up to 4 training regimes, for Russian grammatical error correction.
- 📊 Training data:
synterr-nlp/synterr-v4-sft - 🔧 Generator: github.com/synterr-nlp/synterr
- 🌐 Paper & artifacts: https://synterr-nlp.github.io/papers/bea-2026/
Training regimes
| folder pattern | regime | training data |
|---|---|---|
v4_<model> | SyntErr-only | 39,209 synthetic examples (SyntErr v4) |
v4_<model>_lorugec | SyntErr → LORuGEC | SyntErr-only, then continued on 348 real LORuGEC val examples |
v4_lorugec_only_<model> | LORuGEC-only | 348 real LORuGEC val examples only |
v4_clean_only_<model> | clean control | clean text (src = tgt) only |
Results — LORuGEC test, M2 F₀.₅
| Base model | folder stem | Zero-shot | +lorugec | +synterr | +s→lorugec |
|---|---|---|---|---|---|
| Qwen3.5-0.8B | qwen35_08b | 13.7 | 33.8 | 45.2 | 54.0 |
| Qwen3.5-4B | qwen35_4b | 47.6 | 54.7 | 67.0 | 75.3 |
| Qwen3.5-9B | qwen35_9b | 49.2 | 61.2 | 57.3 | 70.9 |
| Qwen2.5-7B | qwen25_7b | 39.5 | 44.0 | 58.1 | 66.6 |
| Gemma-3-1B | gemma3_1b | 18.9 | 25.5 | 48.0 | 47.8 |
| Gemma-3-4B | gemma3_4b | 41.5 | 43.0 | 54.5 | 58.2 |
| Gemma-3-12B | gemma3_12b | 52.5 | 58.8 | 66.0 | 69.7 |
| Apertus-8B | apertus_8b | 48.7 | 54.1 | 69.4 | 72.4 |
Columns map to folders: +synterr → v4_<stem>, +s→lorugec →
v4_<stem>_lorugec, +lorugec → v4_lorugec_only_<stem>. clean control
adapters (v4_clean_only_qwen25_7b, v4_clean_only_qwen35_08b) are reported in
the paper appendix. GigaChat-3.1-lightning adapters (v4_gc31_lightning*) are
included as supplementary runs.
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
base_id = "Qwen/Qwen3.5-4B"
base = AutoModelForCausalLM.from_pretrained(base_id, device_map="auto")
tok = AutoTokenizer.from_pretrained(base_id)
# load one adapter by subfolder
model = PeftModel.from_pretrained(
base, "synterr-nlp/bea2026-gec-adapters", subfolder="v4_qwen35_4b_lorugec"
)
Inference prompt is the system prompt described in the paper (§ Experimental setup); see the generator repo for the exact template.
Licenses
These are LoRA adapters (weight deltas), not standalone models — using them requires the corresponding base model under its own license:
| Base family | License |
|---|---|
| Qwen2.5 / Qwen3.5 | Apache-2.0 |
| Gemma 3 | Gemma Terms of Use (Google) |
| Apertus-8B | Apache-2.0 |
| GigaChat-3.1 | GigaChat License (Sber) |
The adapter weights in this repo are released for research use; you remain responsible for complying with each base model's terms.
Citation
@inproceedings{smirnova2026aggregate,
title = {What Aggregate Scores Hide: Per-Rule Evaluation of Russian Grammatical Error Correction},
author = {Smirnova, Anna and Kopan, Artyom and Makeev, Vladislav and Chernishev, George},
booktitle = {Proceedings of the 21st Workshop on Innovative Use of NLP for Building Educational Applications (BEA)},
year = {2026},
url = {https://synterr-nlp.github.io/papers/bea-2026/},
}
- Library
- peft
- Languages
- ru
- Created
- Jul 1, 2026
- Updated
- Jul 1, 2026