Instructions to use brenorb/doc-quality with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER2
How to use brenorb/doc-quality with GLiNER2:
from gliner2 import AutoExtractor extractor = AutoExtractor.from_pretrained("brenorb/doc-quality") # 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
Commit History
2 days ago
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feat(quality): add Linux harness pinned to immutable model data
Verified signatureSmall CPU harness for Linux x86-64/ARM64 with frozen dependencies and SHA-256 verification. Model data pinned at cbc1fecdd5ad2a24ee535e952c5a8bea9d6271ef. Linux ARM64 checked on all 24 development pages with no decision changes.967e71e -
feat(quality): publish frozen quality model and provenance
Verified signatureUnchanged GLiNER encoder and learned document head, with recall-first cutoff 0.71. Includes model card, upstream attribution/license and SHA-256 manifest. No training documents or credentials.cbc1fec -
initial commit
Verified signature3b3460e