language:
- en
- zh
- de
- es
- fr
- ja
library_name: pytorch
pipeline_tag: text-classification
base_model: google/embeddinggemma-300m
tags:
- extreme-multi-label-classification
- embeddinggemma
- multilingual
UserNeeds2K (FP16)
This standalone variant stores all weights in FP16. EmbeddingGemma computes in BF16 for numerical stability, while the classifier computes in FP16.
An extreme multi-label classifier for 2,000 categories (Categoryv2-2000, trained on userneeds_samples_v3 and 2,000 encyclopedic domain overviews). Text is embedded with the bundled google_embeddinggemma-300m model; a 2,048-unit dynamic-negative MLP (userneeds2k_dynamic_negative_mlp) produces independent category scores. The package runs without downloading a separate embedding model.
Usage
pip install -r requirements.txt
python example.py
Example output:
0.6842 10004/20035/30200 Beauty & Fitness / Fitness / Fitness Instruction & Personal Training
0.5914 10004/20035/30203 Beauty & Fitness / Fitness / General Fitness
0.4418 10004/20035/31191 Beauty & Fitness / Fitness / Home Workouts, Bodyweight & Calisthenics
0.2875 10022/20214/30775 Reference / General Reference / How-To, DIY & Expert Content
0.1943 10004/20035/30202 Beauty & Fitness / Fitness / High Intensity Interval Training
Supported language keys
| Key | Language |
|---|---|
cn |
Chinese |
en |
English |
de |
German |
sp |
Spanish |
fr |
French |
jp |
Japanese |
Performance
| Test set | nDCG@5 | Precision@5 | Recall@5 | Top-1 hit | Tail recall@5 |
|---|---|---|---|---|---|
| English | 0.803369 | 0.562866 | 0.758034 | 0.890662 | 0.584744 |
| Chinese | 0.756571 | 0.527800 | 0.716975 | 0.857000 | 0.503401 |
On an H100, this optimized FP16-storage variant processed 811.41 examples per second with 6952.34 MB peak GPU memory and achieved 0.756280 nDCG@5 on the 1,000-example precision comparison set.
No training records, translated samples, document embeddings, targets, split files, or training-derived document centroids are included in this package.