Text Classification
GLiNER
English
coreai
coreai-aimodel
core-ai
coreaikit
apple
on-device
zero-shot-classification
deberta
typed-decisions
Instructions to use mlboydaisuke/GLiNER2.5-Decide-CoreAI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use mlboydaisuke/GLiNER2.5-Decide-CoreAI with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("mlboydaisuke/GLiNER2.5-Decide-CoreAI") text = "Cristiano Ronaldo dos Santos Aveiro was born on 5 February 1985 in Funchal, Madeira, Portugal." labels = ["person", "date", "location"] entities = model.predict_entities(text, labels) for entity in entities: print(entity["text"], "=>", entity["label"]) - Notebooks
- Google Colab
- Kaggle
File size: 2,504 Bytes
7464c91 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | GLiNER2.5-Decide-CoreAI
Core AI (.aimodel) conversion of the classification path of Fastino's GLiNER2.5-Decide.
Origin
Model: fastino/GLiNER2.5-Decide
Source: https://huggingface.co/fastino/GLiNER2.5-Decide
Revision: 7ee5da4c2415e32259bcdc0b1a7367c32ce8d6f6 (2026-09-24)
Weights: model.safetensors, sha256 40a5a23ff860dc3dff426cecd1048cacdd29c648c96db209dad818e9686dc997
Provider: Fastino
License: Apache License 2.0 (declared on the model card; the source repository has no LICENSE file)
Base: microsoft/deberta-v3-large (MIT License)
Software used
gliner2 2.0.0 (Apache License 2.0): the fp32 reference the conversion was checked against.
transformers 4.57.6 (Apache License 2.0): DebertaV2Model, into which the encoder weights were loaded.
Conversion
The released fp32 weights were loaded strictly into transformers' DebertaV2Model and the classification head
(Linear 1024->2048, ReLU, Linear 2048->1) and exported as one static graph with Apple coreai-torch 0.4.1 /
coreai-core 1.0.0b2, at two sequence lengths (256 and 512). The bundles store the released weights in half
precision. No weights were retrained, pruned or quantized. The span and count heads, which classification does
not use, are not in the graph. DeBERTa-v3's relative-position bucket table is computed in PyTorch at export time
and stored in each graph as a constant. The ios/ bundles are the same graphs compiled ahead of time with
coreai-build for the iPhone 18 Pro (h19p). Converted and published by mlboydaisuke
(https://huggingface.co/mlboydaisuke), 2026-09.
Changed files
tokenizer/tokenizer_config.json is the source file with one change: tokenizer_class DebertaV2Tokenizer ->
XLMRobertaTokenizer, so that swift-transformers loads the same SentencePiece Unigram model. tokenizer.json and
special_tokens_map.json are unchanged. source/config.json and source/encoder_config.json are the source
repository's config.json and encoder_config/config.json, unchanged.
Test fixtures (gate/)
readme21.json: the 21 classify_text examples of the model card at the revision above.
fast_decisions_s256.json, fast_decisions_long.json: rows of fastino/fast-decisions (Apache License 2.0), revision
1a33070cabf94ce2e29105482dd2ef6c157ad7f2.
Each case carries gliner2 2.0.0's fp32 inputs, logits and decisions.
This distribution is provided under the Apache License 2.0 (see LICENSE). When redistributing any part of it,
keep this NOTICE and the LICENSE file with it.
|