Token Classification
Transformers
Safetensors
English
bert
veterinary
nlp
ner
named-entity-recognition
biomedical
feline
Instructions to use Statistical-Impossibility/Feline-NER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Statistical-Impossibility/Feline-NER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Statistical-Impossibility/Feline-NER")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Statistical-Impossibility/Feline-NER") model = AutoModelForTokenClassification.from_pretrained("Statistical-Impossibility/Feline-NER") - Notebooks
- Google Colab
- Kaggle
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## Model Description
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**Feline-NER** is a transformer-based named entity recognition (NER) model for **feline veterinary scientific literature**.
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The model follows the BERT architecture and was initialized from **BioBERT v1.2**, further domain-adapted on feline-related PubMed Central articles (*Feline-BERT*), and fine-tuned for NER (*Feline-NER*).
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This model is intended **solely for research and educational use**.
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## Model Description
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**Feline-NER** is a transformer-based named entity recognition (NER) model for **feline veterinary scientific literature**.
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The model follows the BERT architecture and was initialized from **BioBERT v1.2**, further domain-adapted on feline-related PubMed Central articles (**Feline-BERT**), and fine-tuned for NER (**Feline-NER**).
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This model is intended **solely for research and educational use**.
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