| ---
|
| 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.
|
|
|
