license: gemma
base_model: google/gemma-4-31b-it
tags:
- relation-extraction
- romanian
- cross-lingual
- qlora
- peft
- lora
- end-to-end
language:
- ro
- en
library_name: peft
pipeline_tag: text-generation
Gemma 4 31B QLoRA adapter — Romanian/English End-to-End Relation Extraction
Dragoș Mitruț Vasile · Elena-Simona Apostol · Stefan-Adrian Toma · Adrian Paschke · Ciprian-Octavian Truică
QLoRA adapter for google/gemma-4-31b-it, fine-tuned for End-to-End Relation Extraction on a Romanian translation of SemEval-2010 Task 8 plus the original English data. Unlike the classification adapter, entity tags are not given: the model reads a plain sentence and generates both entities and the relation between them as a single structured output.
Results (SemEval-2010 Task 8 test set)
| Language | Exact match | Relation match | Entity match |
|---|---|---|---|
| English | 0.719 | 0.816 | 0.796 |
| Romanian | 0.674 | 0.809 | 0.751 |
QLoRA raises exact match by about 39pp over zero-shot in both languages. This is the setting where the LLM clearly outperforms encoder classifiers, which require a separate entity recognition stage and do not apply directly.
Training
- Base:
google/gemma-4-31b-it, loaded in 4-bit - LoRA: rank 32, alpha 64, dropout 0.05, all attention and MLP projections
- 3 epochs, effective batch size 16, peak LR 2e-4, cosine decay, 5% warmup
- Combined English + Romanian training data
- Single NVIDIA A100 40GB
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
base = AutoModelForCausalLM.from_pretrained("google/gemma-4-31b-it", load_in_4bit=True, device_map="auto")
model = PeftModel.from_pretrained(base, "<HF_REPO_E2E>")
tok = AutoTokenizer.from_pretrained("<HF_REPO_E2E>")
The model outputs a JSON object with the two entities and the relation. The exact prompt and parsing logic are in run_inference.py in the code repository.
Limitations
The Romanian training data is machine-translated with automatic post-validation. Because end-to-end extraction scores predictions against the gold entity spans taken from the markers, untranslated or misplaced gold spans penalize the Romanian numbers, which should be read as a lower bound. See the paper for the translation quality analysis.
Citation
@misc{vasile2026crosslingual,
title = {Cross-lingual Relation Extraction with Large Language Models: Zero-Shot, Few-Shot, and Fine-Tuned Evaluation on Romanian},
author = {Vasile, Drago\c{s}-Mitru\c{t} and Apostol, Elena-Simona and Toma, \c{S}tefan-Adrian and Paschke, Adrian and Truic\u{a}, Ciprian-Octavian},
year = {2026},
note = {Preprint}
}