NERCat Classifier

Model Overview

The NERCat classifier is a fine-tuned version of the GLiNER Knowledgator model, designed specifically for Named Entity Recognition (NER) in the Catalan language. By leveraging a manually annotated dataset of Catalan-language television transcriptions, this classifier significantly improves the recognition of named entities across diverse categories, addressing the challenges posed by the scarcity of high-quality training data for Catalan.

The pre-trained version used for fine-tuning was: knowledgator/gliner-bi-large-v1.0.

Quickstart

import torch
from gliner import GLiNER

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = GLiNER.from_pretrained("Ugiat/NERCat").to(device)

text = "La Universitat de Barcelona és una de les institucions educatives més importants de Catalunya."

labels = [
    "Person",
    "Facility",
    "Organization",
    "Location",
    "Product",
    "Event",
    "Date",
    "Law"
]

entities = model.predict_entities(text, labels, threshold=0.5)

for entity in entities:
    print(entity["text"], "=>", entity["label"])

Performance Evaluation

We evaluated the fine-tuned NERCat classifier against the baseline GLiNER model using a manually classified evaluation dataset of 100 sentences. The results demonstrate significant performance improvements across all named entity categories:

Fine-Tuning Process

The fine-tuning process followed a structured approach, including dataset preparation, model training, and optimization:

• Data Splitting: The dataset was shuffled and split into training (90%) and testing (10%) subsets.
• Training Setup:
  • Batch size: 8
  • Steps: 500
  • Loss function: Focal loss (α = 0.75, γ = 2) to address class imbalances
  • Learning rates:
    • Entity layers: $5 \times 10^{-6}$
    • Other model parameters: $1 \times 10^{-5}$
  • Scheduler: Linear with a warmup ratio of 0.1
  • Evaluation frequency: Every 100 steps
  • Checkpointing: Every 1000 steps

The dataset included 13,732 named entity instances across eight categories:

Other

Citation Information

@misc{article_id,
  title        = {NERCat: Fine-Tuning for Enhanced Named Entity Recognition in Catalan},
  author       = {Guillem Cadevall Ferreres, Marc Bardeli Gámez, Marc Serrano Sanz, Pol Gerdt Basullas, Francesc Tarres Ruiz, Raul Quijada Ferrero},
  year         = {2025},
  archivePrefix = {arXiv},
  url          = {https://arxiv.org/abs/2503.14173}
}