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
UserNeeds1K (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 1,091 categories. Text is embedded with
the bundled google_embeddinggemma-300m model; a 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:
0.6411 10004/20035/30203 Beauty & Fitness / Fitness / Other
0.4917 10004/20035/30200 Beauty & Fitness / Fitness / Fitness Instruction & Personal Training
0.2998 10022/20214/30775 Reference / General Reference / How-To, DIY & Expert Content
0.0853 10004/20035/30202 Beauty & Fitness / Fitness / High Intensity Interval Training
0.0628 10002/20013/30078 Arts & Entertainment / TV & Video / Online Video
Supported language keys
| Key | Language |
|---|---|
cn |
Chinese |
en |
English |
de |
German |
ge |
German alias |
sp |
Spanish |
fr |
French |
jp |
Japanese |
Performance
| Test set | nDCG@5 | Precision@5 | Recall@5 | Top-1 hit | Tail recall@5 |
|---|---|---|---|---|---|
| English | 0.858722 | 0.513768 | 0.833800 | 0.910088 | 0.629620 |
| Chinese | 0.821382 | 0.491000 | 0.792756 | 0.890000 | 0.583333 |
On an H100, this optimized FP16-storage variant processed 825.51 examples per second with 2,176.64 MB peak GPU memory and achieved 0.855899 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.