Feature Extraction
Kernels
Scikit-learn
kernel
governance
provenance
suite
embeddings
word-embeddings
doi:10.5281/zenodo.19944926
Instructions to use SZLHOLDINGS/szl-kernels with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Kernels
How to use SZLHOLDINGS/szl-kernels with Kernels:
# !pip install kernels from kernels import get_kernel kernel = get_kernel("SZLHOLDINGS/szl-kernels") - Scikit-learn
How to use SZLHOLDINGS/szl-kernels with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("SZLHOLDINGS/szl-kernels", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
Forge SZL-MiniEmbed v1: REAL trained embeddings (co-occurrence+PPMI+TruncatedSVD, no gensim) + vocab/config + receipt + eval + bundled corpus + honest card & provenance
7842caa verified | { | |
| "model": "SZL-MiniEmbed", | |
| "method": "term-term co-occurrence (distance-weighted, window=5) -> PPMI -> TruncatedSVD", | |
| "dim": 128, | |
| "vocab_size": 3290, | |
| "min_count": 5, | |
| "window": 5, | |
| "seed": 20260721, | |
| "normalization": "L2 row-normalized", | |
| "files": { | |
| "vectors": "vectors.npz (key 'vectors', float32 [V,dim])", | |
| "vocab": "vocab.json ({'vocab':[term...], 'index':{term:i}})" | |
| } | |
| } |