File size: 2,043 Bytes
5576acd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 | ---
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
```bash
pip install -r requirements.txt
python example.py
```
Example output:
```text
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.
|