Instructions to use FluidInference/gliner2-5-base-coreml with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER2
How to use FluidInference/gliner2-5-base-coreml with GLiNER2:
from gliner2 import AutoExtractor extractor = AutoExtractor.from_pretrained("FluidInference/gliner2-5-base-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-application100.json from FluidInference/gliner2-5-base-coreml: direct link, hf CLI and curl.
- Browser
- Download file 604 Bytes
-
https://huggingface.co/FluidInference/gliner2-5-base-coreml/resolve/main/verify-application100.json
- Command line
-
hf download hf://FluidInference/gliner2-5-base-coreml/verify-application100.json
-
curl -L -o verify-application100.json https://huggingface.co/FluidInference/gliner2-5-base-coreml/resolve/main/verify-application100.json
604 Bytes
| { | |
| "model": "fastino/gliner2.5-base-v1", | |
| "revision": "1a8bc24e00dc7300b9017c81d63e3dcdabb26596", | |
| "package": "build/gliner2_base_classification_fp16_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": 7, | |
| "duplicate_labels": 0, | |
| "mismatches": 0 | |
| }, | |
| "maximum_confidence_error": 0.0029987096786499023, | |
| "mean_confidence_error": 0.00020762801170349122, | |
| "model_call_p50_ms": 8.819750044494867, | |
| "model_call_p95_ms": 13.183709001168609, | |
| "failures": [] | |
| } | |