Instructions to use adiZu/river-decider with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER2
How to use adiZu/river-decider with GLiNER2:
from gliner2 import AutoExtractor extractor = AutoExtractor.from_pretrained("adiZu/river-decider") # 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
river-decider
Mobile ONNX encoder for the on-device agent of River (MonkeyMesh). It is a quantized export of fastino/GLiNER2.5-multi-Decide (Apache-2.0), derived from the community ONNX conversion DanKau/gliner2.5-multi-decide-onnx.
encoder.onnx: int8 embedding table, weights stored fp16 and computed in fp32. 364 MB. sha2563078c2351e140f0c4f6f7a6d0349c167dd74f289c425e98490e9e807a0b5ec2a.- Max probability difference to the fp32 reference on 119 test posts: 0.054; labels 474/476 identical.
- Use with
onnx/classifier.onnxandtokenizer.jsonfrom the DanKau repo.
License: Apache-2.0, same as the original model. All credit for the model goes to Fastino.
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Model tree for adiZu/river-decider
Base model
fastino/gliner2.5-multi-v1 Finetuned
fastino/GLiNER2.5-multi-Decide