Instructions to use FluidInference/gliner2-5-multi-coreml with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER2
How to use FluidInference/gliner2-5-multi-coreml with GLiNER2:
from gliner2 import AutoExtractor extractor = AutoExtractor.from_pretrained("FluidInference/gliner2-5-multi-coreml") # 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
File size: 680 Bytes
0a423df fcf4209 0a423df fcf4209 0a423df fcf4209 0a423df | 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 | [project]
name = "gliner2-multi-coreml"
version = "0.1.0"
description = "Pinned GLiNER 2.5 multi Core ML classification and extraction export"
requires-python = ">=3.12,<3.13"
dependencies = [
"coremltools==9.0",
"gliner2[local]==2.0.0",
"huggingface-hub>=0.34,<1",
"numpy<2.3",
"protobuf>=5,<7",
"psutil>=7,<8",
"sentencepiece>=0.2,<0.3",
"torch==2.7.0",
"transformers==4.57.6",
]
[dependency-groups]
dev = ["pytest>=8.4", "ruff>=0.13"]
compression = ["scikit-learn==1.5.1"]
[tool.pytest.ini_options]
testpaths = ["tests"]
pythonpath = ["."]
[tool.ruff]
line-length = 120
target-version = "py312"
[tool.ruff.lint]
select = ["E", "F", "I"]
|