Token Classification
Transformers
Safetensors
PyTorch
English
bert
ner
travel
trip-planning
fine-tuned
bio-tagging
custom-dataset
english
tripplan
lightweight
nlp
Instructions to use boltuix/RouteNER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use boltuix/RouteNER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="boltuix/RouteNER")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("boltuix/RouteNER") model = AutoModelForTokenClassification.from_pretrained("boltuix/RouteNER") - Notebooks
- Google Colab
- Kaggle
How is it different than GLiNER?
#1
by Andyrasika - opened
How is it different than GLiNER?
RouteNER vs GLiNER β Key Differences
RouteNER
β
Just 20MB super lightweight
β
Trained on custom travel data (from, to, vehicle, etc.)
β
Single language, high accuracy, fast inference
β
Best for lightweight apps chatbots where speed & size matter
GLiNER
βOver 200MB to GB+
π Supports multiple languages
π― Zero-shot entity extraction β define entities at runtime
β οΈ Heavier, but more flexible for general-purpose tasks
π Use RouteNER if you need small, fast, and domain-specific.
π Use GLiNER for multilingual..
boltuix changed discussion status to closed