Text Classification
Transformers
Safetensors
GLiNER2
English
Text classification
Intent classification
Sentiment Analysis
Topic classification
Named Entity Recognition
decision-model
Instructions to use fastino/GLiNER2.5-Decide with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fastino/GLiNER2.5-Decide with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="fastino/GLiNER2.5-Decide")# pip install -U transformers accelerate # Load model directly from transformers import Gliner2ForSchemaExtraction model = Gliner2ForSchemaExtraction.from_pretrained("fastino/GLiNER2.5-Decide", device_map="auto") - GLiNER2
How to use fastino/GLiNER2.5-Decide with GLiNER2:
from gliner2 import AutoExtractor extractor = AutoExtractor.from_pretrained("fastino/GLiNER2.5-Decide") # Extract entities text = "Apple CEO Tim Cook announced iPhone 15 in Cupertino yesterday." result = extractor.extract_entities(text, ["company", "person", "product", "location"]) print(result) - Inference
- Notebooks
- Google Colab
- Kaggle
Add decision-model tag
#10
by victor HF Staff - opened
Hi fastino team,
This PR adds the decision-model tag to the model card metadata. GLiNER2.5-Decide scores labels passed at call time in a single forward pass, the kind of model the Hub groups under this tag, so it shows up next to the other decision models: https://huggingface.co/models?other=decision-model
The GGUF conversion 3ntr0py-t4m3r/GLiNER2.5-Decide-GGUF already has this tag: the Hub adds it automatically from its llama.cpp metadata. This PR adds it to the original checkpoint too.
It's a one-line metadata change, nothing else in the card is touched.
Thanks Victor, merging now!
kzfastino changed pull request status to merged