CLAUSE-Bielefeld/InvOntDef-DeBERTa

InvDef-DeBERTa Model Card

The InvOntDef-DeBERTa is a transformer encoder model pretrained for the domain of invasion biology. In addition to MLM pretraining on scientific abstracts (ca. 35000) from the domain of invasion biology, we pretrain it as embedding model on concept definitions for domain-relevant concepts. This dataset of concepts with definitions was created using the INBIO and ENVO ontologies, and was augmented with an LLM by generating four additional definitions for each concept.

Model Details

Model Description

• Developed by: CLAUSE group at Bielefeld University
• Model type: DeBERTa-base
• Languages: Mostly english
• Finetuned from model: microsoft/deberta-base

Model Sources

• Repository: github.com/inas-argumentation/Ontology_Pretraining
• Paper: aclanthology.org/2025.findings-emnlp.1238/

How to Get Started with the Model

Minimal example on how to process texts with this model:

from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("CLAUSE-Bielefeld/InvOntDef-DeBERTa")
model = AutoModel.from_pretrained("CLAUSE-Bielefeld/InvOntDef-DeBERTa")

text = "Your text to be embedded."
batch = tokenizer([text], return_tensors="pt")
model_output = model(**batch)

Training Details

This model was trained on a dataset of about 35000 scientific abstracts from the domain of invasion biology. Additionally, we used a dataset of 5,197 unique concepts extracted from the ENVO and INBIO ontologies, each accompanied by one ontology-derived and four LLM-generated concept definitions. We used a triplet loss to encourage definitions of the same concept to be placed nearby in the embedding space, and to also place related concepts (i.e., linked in the ontology) in proximity. The dataset and exact training procedure can be found in our GitHub repo,

Evaluation

Model	INAS Clf: Macro F1	INAS Clf: Micro F1	INAS Span: Token F1	INAS Span: Span F1	EICAT Clf: Macro F1	EICAT Clf: Micro F1	EICAT Evidence: NDCG	Avg.
DeBERTa base	0.674	0.745	0.406	0.218	0.392	0.416	0.505	0.483
InvOntDef-DeBERTa	0.750	0.812	0.414	0.242	0.504	0.518	0.530	0.538
InvDef-DeBERTa	0.740	0.805	0.415	0.220	0.469	0.489	0.511	0.520

The InvDef-DeBERTa model was also trained by us, using a purely LLM-based pipeline to see if the ontology-derived information can be replaced.

Citation

BibTeX:

@inproceedings{brinner-etal-2025-enhancing,
    title = "Enhancing Domain-Specific Encoder Models with {LLM}-Generated Data: How to Leverage Ontologies, and How to Do Without Them",
    author = "Brinner, Marc Felix  and
      Al Mustafa, Tarek  and
      Zarrie{\ss}, Sina",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.findings-emnlp.1238/",
    doi = "10.18653/v1/2025.findings-emnlp.1238",
    pages = "22740--22754",
    ISBN = "979-8-89176-335-7"
}