Base: BERT (Devlin et al., 2019)
Domain-adapted: BioBERT v1.2 (Lee et al., 2019) - biomedical literature
Further adapted: Feline-BERT - 11,830 feline PubMed articles (MLM fine-tuning)
Task-specific: Feline-NER - 1,300 manually annotated sentences (token classification)

This model is intended solely for research and educational use.

Task

Span-level named entity recognition using a BIO tagging scheme over five entity types:

DISEASE
SYMPTOM
MEDICATION
PROCEDURE
ANATOMY

Training Data

Fine-tuned on a manually annotated dataset of 1,300 sentences extracted from feline-related PubMed Central articles. Annotations were produced by a non-veterinary researcher using an iterative human-in-the-loop workflow with LLM-assisted pre-labeling.

Evaluation

Macro F1: ~0.65
Micro F1: ~0.64
(Evaluated on a 150-sentence held-out test set)

Performance varies by entity type; PROCEDURE and ANATOMY remain challenging due to boundary ambiguity.

Intended Use

Veterinary NLP research
Information extraction from feline scientific literature
Educational demonstrations

Usage

This model can be used with the Hugging Face pipeline API:

python

from transformers import pipeline
ner = pipeline("ner", model="Statistical-Impossibility/Feline-NER", aggregation_strategy="simple")
print(ner("The cat was diagnosed with FIV and treated with prednisolone."))

⚠️ Limitations & Warnings

NOT FOR CLINICAL USE
Not validated for diagnosis or treatment
Annotation noise and boundary ambiguity are present
Single-annotator dataset

Author

Statistical-Impossibility

Project repository: Feline-Project