Token Classification
GLiNER2
Safetensors
English
extractor
Text classification
Intent classification
Sentiment Analysis
Topic classification
Named Entity Recognition
Instructions to use fastino/GLiNER2.5-Decide-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER2
How to use fastino/GLiNER2.5-Decide-1B with GLiNER2:
from gliner2 import GLiNER2 model = GLiNER2.from_pretrained("fastino/GLiNER2.5-Decide-1B") # Extract entities text = "Apple CEO Tim Cook announced iPhone 15 in Cupertino yesterday." result = extractor.extract_entities(text, ["company", "person", "product", "location"]) print(result) - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from fastino/GLiNER2.5-Decide-1B: direct link, hf CLI and curl.
- Browser
- Download file 3.59 MB
-
https://huggingface.co/fastino/GLiNER2.5-Decide-1B/resolve/main/tokenizer.json
- Command line
-
hf download hf://fastino/GLiNER2.5-Decide-1B/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/fastino/GLiNER2.5-Decide-1B/resolve/main/tokenizer.json
3.59 MB
File too large to display, you can check the raw version instead.