ERC Panels Classifier

This model is a fine-tuned version of allenai/specter2_base for multilabel scientific domain classification aligned with ERC panel taxonomy.
It achieves the following results on the held-out test set:

• Best validation loss: 0.0361
• Micro F1: 0.9386
• Micro ROC-AUC: 0.9718
• Subset accuracy: 0.7943

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Model description

This model is a fine-tuned variant of SPECTER2 (allenai/specter2_base) adapted for multilabel classification of scientific documents into ERC research panels.

The model takes as input the title and abstract of a scientific publication and predicts one or more research panels.
Since scientific outputs may legitimately span multiple domains, the model is trained using sigmoid activation with binary cross-entropy loss, allowing independent assignment of multiple labels.

Key characteristics

• Base model: allenai/specter2_base
• Task: multilabel document classification
• Labels: 28 ERC scientific panels
• Activation: sigmoid (independent scores per label)
• Loss: BCEWithLogitsLoss
• Output: list of predicted panels with associated probabilities
• Decision threshold: 0.5 (tunable)

This model enables automatic research-domain tagging aligned with the ERC panel structure.

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Intended uses & limitations

Intended uses

This model is designed for:

• Automatic assignment of ERC research panels
• Metadata enrichment for:
  • research project databases
  • institutional repositories
  • funding and grant analysis pipelines
• Large-scale analytics such as:
  • portfolio mapping
  • thematic analysis of research outputs
  • monitoring disciplinary coverage of funded projects
• Predicting subject areas for documents lacking structured domain metadata

The model supports:

• title only
• abstract only
• title + abstract (recommended)

Limitations

• ERC panels are high-level categories and do not represent fine-grained subdisciplines
• Labels are derived from curated datasets, semi-automatically annotated data
• Class imbalance may affect recall for underrepresented panels
• The model does not encode explicit hierarchical relationships between panels

Not suited for:

• fine-grained subfield classification
• journal recommendation
• evaluation of research quality or impact
• clinical, legal, or regulatory decision-making

Predictions should be treated as supportive metadata, not authoritative classifications.

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How to use

from transformers import pipeline

# Replace with your actual model repo name on HuggingFace
MODEL_NAME = "nicolauduran45/erc_classifier_demo"

classifier = pipeline(task="text-classification", model=MODEL_NAME, tokenizer=MODEL_NAME)

text = ["Climate change impacts on Arctic ecosystems."]

classifier(text)

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Training and evaluation data

Training data

• Scientific documents with ERC-style panel annotations
• Inputs:
  • title
  • abstract
• Task type: multilabel classification

Dataset characteristics

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Training procedure

Preprocessing

• Input text constructed as:

  title + ". " + abstract

• Tokenization using the SPECTER2 tokenizer

• Maximum sequence length: 512 tokens

Model

• Base model: allenai/specter2_base
• Classification head: linear → sigmoid
• Loss function: BCEWithLogitsLoss
• Predictions: independent probability per label

Training hyperparameters

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Training results

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Evaluation results (multilabel test set)

Overall performance

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ERC-funded projects evaluation (multiclass recall)

This evaluation uses ERC-funded projects, where each project belongs to exactly one panel.
Only recall is reported.

Overall performance Overall recall

• Micro recall: 0.77
• Macro recall: 0.76

Citation

@inproceedings{bovenzi2022mapping,
  title={Mapping STI ecosystems via Open Data: Overcoming the limitations of conflicting taxonomies. A case study for Climate Change Research in Denmark},
  author={Bovenzi, Nicandro and Duran-Silva, Nicolau and Massucci, Francesco Alessandro and Multari, Francesco and Parra-Rojas, C{\'e}sar and Pujol-Llatse, Josep},
  booktitle={International Conference on Theory and Practice of Digital Libraries (TPDL)},
  pages={495--499},
  year={2022},
  publisher={Springer International Publishing}
}

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Framework versions

• Transformers: 4.57.x
• PyTorch: 2.8.0
• Datasets: 3.x
• Tokenizers: 0.22.x