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
Download verify-fp32-application100.json from FluidInference/gliner2-5-multi-coreml: direct link, hf CLI and curl.
- Browser
- Download file 607 Bytes
-
https://huggingface.co/FluidInference/gliner2-5-multi-coreml/resolve/main/verify-fp32-application100.json
- Command line
-
hf download hf://FluidInference/gliner2-5-multi-coreml/verify-fp32-application100.json
-
curl -L -o verify-fp32-application100.json https://huggingface.co/FluidInference/gliner2-5-multi-coreml/resolve/main/verify-fp32-application100.json
607 Bytes
| { | |
| "model": "fastino/gliner2.5-multi-v1", | |
| "revision": "a221b77a8baf4a613b8f8652661d41fa10a5641e", | |
| "package": "build/gliner2_multi_classification_fp32_L128_K8.mlpackage", | |
| "selected_manifest": "first eligible rows in source suite order; no gold labels used", | |
| "counts": { | |
| "checked": 100, | |
| "too_many_options": 300, | |
| "too_long": 31, | |
| "duplicate_labels": 0, | |
| "mismatches": 0 | |
| }, | |
| "maximum_confidence_error": 8.046627044677734e-06, | |
| "mean_confidence_error": 7.531046867370605e-07, | |
| "model_call_p50_ms": 11.235417041461915, | |
| "model_call_p95_ms": 19.665875006467104, | |
| "failures": [] | |
| } | |