| ---
|
| 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
|
|
|
| ```bash
|
| pip install -r requirements.txt
|
| python example.py
|
| ```
|
|
|
| Example output:
|
|
|
| ```text
|
| 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.
|
|
|
