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
Ctrl+K
- encoder_config
- gliner2_multi_classification_embedding_w8_linear_L128_K8.mlpackage
- gliner2_multi_classification_fp16_L128_K8.mlpackage
- gliner2_multi_classification_fp32_L128_K8.mlpackage
- gliner2_multi_explicit_fp16_W64_Q8_S64.mlpackage
- gliner2_multi_explicit_fp32_W64_Q8_S64.mlpackage
- gliner2_multi_extraction_features_fp32_L128_W64_Q8.mlpackage
- gliner2_multi_extraction_features_w8_embedding_fp32_L128_W64_Q8.mlpackage
- gliner2_multi_extraction_features_w8_embedding_linear_fp16_L128_W64_Q8.mlpackage
- gliner2_multi_extraction_scorer_fp16_L128_W64_Q8.mlpackage
- gliner2_multi_extraction_scorer_fp32_L128_W64_Q8.mlpackage
- gliner2_multi_record_anchorless_fp16_F8_C192_I1536.mlpackage
- gliner2_multi_record_anchorless_fp32_F8_C192_I1536.mlpackage
- gliner2_multi_record_assignment_fp16_F8_C192_I1536.mlpackage
- gliner2_multi_record_assignment_fp32_F8_C192_I1536.mlpackage
- gliner2_multi_relation_fp16_W64_R4_P256.mlpackage
- gliner2_multi_relation_fp32_W64_R4_P256.mlpackage
- reports
- tests
- tokenizer
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