Transformers
Safetensors
GLiNER2
multilingual
English
Text classification
Intent classification
Sentiment Analysis
Topic classification
Named Entity Recognition
decision-model
schema-extraction
Instructions to use fastino/GLiNER2.5-multi-Decide with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fastino/GLiNER2.5-multi-Decide with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import Gliner2ForSchemaExtraction model = Gliner2ForSchemaExtraction.from_pretrained("fastino/GLiNER2.5-multi-Decide", device_map="auto") - GLiNER2
How to use fastino/GLiNER2.5-multi-Decide with GLiNER2:
from gliner2 import AutoExtractor extractor = AutoExtractor.from_pretrained("fastino/GLiNER2.5-multi-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) - Notebooks
- Google Colab
- Kaggle
Load this checkpoint as a Transformers schema-extraction model.
#7
by MattThomas-fastino - opened
Point this checkpoint at the native Transformers schema-extraction model.
model_type is gliner2 and architectures is Gliner2ForSchemaExtraction, so pipeline("schema-extraction", model=...) can select the class. The encoder config is inlined from encoder_config/config.json. That directory, the weight file, and the existing gliner2 fields (model_name, span_head or boundary_head) stay, so AutoExtractor.from_pretrained still loads this repo.
The model card front matter now says library_name: transformers and pipeline_tag: schema-extraction. The card body is unchanged.
urchade changed pull request status to merged