CLAUSE-Bielefeld
/

InvOntDef-DeBERTa

@@ -1,199 +1,91 @@
 ---
 library_name: transformers
-tags: []
 ---
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 ---
 library_name: transformers
+tags:
+- embedding
+- scientific
+- abstract
+license: mit
+language:
+- en
+base_model:
+- microsoft/deberta-base
+pipeline_tag: feature-extraction
 ---
+# 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](https://huggingface.co/microsoft/deberta-base)
+### Model Sources
+- **Repository:** [github.com/inas-argumentation/Ontology_Pretraining](https://github.com/inas-argumentation/Ontology_Pretraining)
+- **Paper:** [aclanthology.org/2025.findings-emnlp.1238/](https://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](https://github.com/inas-argumentation/Ontology_Pretraining),
 ## 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](https://huggingface.co/CLAUSE-Bielefeld/InvDef-DeBERTa) | 0.740    | 0.805    | **0.415**    | 0.220   | 0.469              | 0.489              | 0.511 | 0.520 |
+The [InvDef-DeBERTa](https://huggingface.co/CLAUSE-Bielefeld/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:**
+```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"
+}
+```