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|
| --- |
| language: ["en"] |
| library_name: "pytorch" |
| pipeline_tag: "text-classification" |
| base_model: "google/embeddinggemma-300m" |
| tags: ["multi-label-classification", "hierarchical-classification", "physics", "physh", "transfer-learning"] |
| metrics: ["f1"] |
| --- |
| |
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| # PhySH Topic Supervised Classifier |
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| A hierarchical multi-label classifier that assigns APS Physics Subject Headings (PhySH) to physics papers using their titles and abstracts. |
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| ## Model description |
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| The model uses frozen [`google/embeddinggemma-300m`](https://huggingface.co/google/embeddinggemma-300m) representations with a two-stage conditioned classifier: |
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| 1. **Discipline classifier:** Predicts 18 broad APS disciplines. |
| 2. **Concept classifier:** Predicts 186 research-area concepts, conditioned on the discipline probabilities produced by the first stage. |
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| This classifier-chain design incorporates the hierarchy between broad disciplines and more specific research concepts. |
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| ## Architecture |
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| The input is formatted as: |
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| ```text |
| {title} [SEP] {abstract} |
| ``` |
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| EmbeddingGemma produces a 768-dimensional, L2-normalized representation. The encoder remains frozen during training. |
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| ### Frozen encoder |
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| ```text |
| google/embeddinggemma-300m → 768 dimensions |
| ``` |
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| ### Discipline head |
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| ```text |
| 768 → 1024 → 512 → 18 |
| ``` |
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| ### Concept head |
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| ```text |
| [768-dimensional embedding; 18 discipline probabilities] |
| 786 → 1024 → 512 → 186 |
| ``` |
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| Both classification heads use ReLU activations and dropout with `p = 0.3`. Their outputs are independent sigmoid scores rather than a softmax distribution, allowing multiple labels to be assigned to each paper. |
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| The two MLP heads contain approximately 2.75 million trainable parameters in total. |
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| ## Training |
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| * **Objective:** Multi-label binary cross-entropy with logits |
| * **Optimizer:** Adam |
| * **Encoder:** Frozen |
| * **Hardware:** Apple Metal Performance Shaders (MPS) |
| * **Optional objective:** Focal loss |
| * **Training run:** `20260130_140842` |
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| ## Evaluation |
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| | Prediction level | Micro-F1 | Macro-F1 | |
| | ----------------- | -------: | -------: | |
| | Disciplines | 0.799 | 0.683 | |
| | Research concepts | 0.641 | 0.423 | |
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| The model predicts approximately 2.12 labels per paper, compared with approximately 2.15 labels per paper in the evaluation data. |
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| > **Evaluation-data note:** Add the dataset source, number of examples, train/validation/test split sizes, and split methodology before treating these results as independently reproducible. |
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| ## Inference |
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| Inference applies a configurable threshold to each sigmoid score. If no score passes the threshold, the highest-scoring label is returned as a top-1 fallback. |
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| Thresholds should be selected according to the intended precision–recall tradeoff: |
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| * **Interactive demo threshold:** `0.35` |
| * **Batch-labeling threshold:** `0.85` |
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| Try the model in the interactive demo. |
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| ## Intended uses |
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| This model is intended for: |
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| * Organizing and exploring physics literature |
| * Suggesting PhySH labels for paper titles and abstracts |
| * Supporting search, recommendation, and bibliometric workflows |
| * Research on hierarchical multi-label classification |
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| Predictions should be treated as label suggestions rather than authoritative APS classifications. |
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| ## Limitations |
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| * Performance is lower on rare concepts, as reflected by the gap between micro-F1 and macro-F1. |
| * Predictions depend on the information available in the title and abstract. |
| * The model may not generalize to fields, terminology, or document types that differ substantially from its training data. |
| * Output quality and label frequency depend on the selected inference threshold. |
| * The frozen encoder limits task-specific representation learning. |
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| ## Citation |
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| If you use this model, please cite the model repository: |
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| ```bibtex |
| @misc{physh_topic_supervised_classifier, |
| author = {Luke F}, |
| title = {PhySH Topic Supervised Classifier}, |
| year = {2026}, |
| publisher = {Hugging Face}, |
| url = {https://huggingface.co/LukeFP/physh_topic_supervised_classifier} |
| } |
| ``` |
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