cs-file-uploads/ie-ner-adapter

General-IE Baseline Adapter — NER (Gemma 3 27B)

LoRA adapter for google/gemma-3-27b-it trained with an IEInstruct-style named-entity-recognition only objective. Released as a general information-extraction baseline for the anonymous submission "From Lengthy Narrative to Structured Data: Instruction Fine-Tuning Open-Weight LLMs for Information Extraction from Corporate Disclosures."

On the paper's compensation-consultant task this generic-IE adapter reaches F1 75.8%, well below the domain-specific adapters (up to 96.1%) — evidence that task-specific fine-tuning outperforms generic IE instruction-tuning for this domain.

Base model	google/gemma-3-27b-it
Method	LoRA (r=8, α=16), 4-bit QLoRA
Training objective	named-entity-recognition only
Instance-level F1 (consultant task)	75.8%

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base = "google/gemma-3-27b-it"
tok = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(base, device_map="auto", load_in_4bit=True)
model = PeftModel.from_pretrained(model, "cs-file-uploads/ie-ner-adapter")

License

Derived from Google Gemma 3; use is subject to the Gemma Terms of Use. Adapter weights are released for research use.

Citation

@misc{anonymous2026fromlengthy,
  title={From Lengthy Narrative to Structured Data: Instruction Fine-Tuning Open-Weight LLMs for Information Extraction from Corporate Disclosures},
  author={Anonymous},
  year={2026},
  note={Under review}
}