LukeFP/Physh_Classification

PhySH Topic Classifier

Paste a physics title and abstract; get back its PhySH disciplines and top-level research-area concepts.

How it works

text ──EmbeddingGemma-300m──> 768-d vector
          │
          ├──> discipline head   768 → 1024 → 512 → 18    sigmoid
          │                                    │
          └──> concept head  [768 + 18] → 1024 → 512 → 186 sigmoid
                                   ▲
                       discipline probabilities

Both heads are multi-label MLPs with ReLU and dropout 0.3, trained on EmbeddingGemma vectors. The concept head is conditioned on the discipline head's output: its 786-dimensional input is the text embedding concatenated with the 18 discipline probabilities (the checkpoint records use_logits: False, so probabilities rather than logits are what it expects).

Weights live in `LukeFP/physh_topic_supervised_classifier` and are downloaded at startup, so retraining only requires a push to that repo — no change here.

Setup

google/embeddinggemma-300m is a gated repo. Accept the Gemma license on the model page, then add a read token as a Space secret named HF_TOKEN (Settings → Variables and secrets). Without it the Space boots but the first classification fails.

This Space runs on ZeroGPU: infer() carries the @spaces.GPU decorator, the models are loaded on CPU in the main process, and device placement happens inside the decorated function. The same code runs unchanged on CPU hardware — spaces is optional at import and torch.cuda.is_available() picks the device.

Prompt format

EmbeddingGemma prepends a task-specific prefix, and the prefix used here must match the one used to build the training embeddings — a mismatch degrades accuracy quietly instead of erroring. The default is the document prompt (title: none | text: …); the Advanced panel lets you switch and compare.

Running locally

pip install -r requirements.txt
export HF_TOKEN=hf_...
python app.py

Set PHYSH_WEIGHTS_DIR=/path/to/physh_topic_supervised_classifier to load the .pt files from a local clone instead of the Hub.

API

Gradio exposes the Space as an API, which is the practical route for batch labelling:

from gradio_client import Client

client = Client("LukeFP/Physh_Classification")
disciplines, concepts, summary = client.predict(
    "Title and abstract…", 0.5, "document — title: none | text: {}", 8,
    api_name="/classify",
)