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
Download tokenizer.json from fastino/GLiNER2.5-multi-Decide: direct link, hf CLI and curl.
- Browser
- Download file 16 MB
-
https://huggingface.co/fastino/GLiNER2.5-multi-Decide/resolve/main/tokenizer.json
- Command line
-
hf download hf://fastino/GLiNER2.5-multi-Decide/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/fastino/GLiNER2.5-multi-Decide/resolve/main/tokenizer.json
16 MB
- Xet hash:
- 24db0ad7bfcaca9c5226fb8d822bf37b4ec4e22565a1cb72eb29076bad4186e6
- Size of remote file:
- 16 MB
- SHA256:
- c62446df87ae18ec98b133f8f84fc449a07cc89bbf8ef192a4cb5f9c53777a7a
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