Instructions to use cp500/infon-extract with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use cp500/infon-extract with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("cp500/infon-extract") - Notebooks
- Google Colab
- Kaggle
infon-extract (fp16)
Grounded infon extractor: paragraph -> typed, grounded, polarity-aware spans/relations.
Fine-tuned from fastino/gliner2-multi-v1 (real span_rep + classifier weights, inherited
calibration) on a 50k EN/JA/KO automotive corpus, + a 3-way polarity head.
Key fix vs base GLiNER2: subword splitter + char-offset grounding -> CJK grounds 100% (base GLiNER2 tags whole JA sentences as one span). Partial-freeze fine-tune (emb+layers0-5 frozen) so calibration survives. GATE: 100% grounded EN/JA/KO, fp16 lossless vs fp32.
Files: model_fp16.safetensors (encoder+span_rep+classifier+count), polarity_head_fp16.pt,
infonex_config.json (arch), tokenizer/config. Use via the infonex package.
Precision: fp16 (588MB). Runs on EC2 GPU (native) and Lambda CPU (upcast).
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