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 GLiNER2 model = GLiNER2.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: 1,209 Bytes
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 33 34 35 36 37 38 | {
"source_model": "fastino/gliner2.5-multi-v1",
"source_revision": "a221b77a8baf4a613b8f8652661d41fa10a5641e",
"package": "build/gliner2_multi_classification_fp32_L128_K8.mlpackage",
"package_bytes": 1152027886,
"native_total_parameters": 287355159,
"exported_parameters": 278719489,
"wrapper_max_logit_error": 0.0,
"coremltools": "9.0",
"torch": "2.7.0",
"cases": [
{
"text": "The rocket launched successfully.",
"native_label": "sports",
"coreml_label": "sports",
"native_confidence": 0.5060790777206421,
"coreml_confidence": 0.5060756206512451,
"absolute_confidence_error": 3.4570693969726562e-06
},
{
"text": "The team won the football championship.",
"native_label": "sports",
"coreml_label": "sports",
"native_confidence": 0.9999996423721313,
"coreml_confidence": 0.9999996423721313,
"absolute_confidence_error": 0.0
},
{
"text": "The budget was approved by parliament.",
"native_label": "politics",
"coreml_label": "politics",
"native_confidence": 0.9999977350234985,
"coreml_confidence": 0.9999977350234985,
"absolute_confidence_error": 0.0
}
]
}
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