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README.md

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+---
+license: apache-2.0
+pretty_name: graphjepa-psf-requests-200
+tags:
+  - temporal-graph
+  - code-graph
+  - graph-ssl
+  - jepa
+---
+
+# graphjepa-psf-requests-200
+
+Precomputed (TemporalGraph, node_features) cache for the
+[graphjepa](https://github.com/IDMedicine/code-transformer)
+project. Avoids repeated git-checkout + tree-sitter + BERT-feature
+preprocessing on every training run.
+
+## Contents
+
+| File | Description |
+|---|---|
+| `graph.pkl` | Pickle of `{'graph': TemporalGraph, 'features': dict_by_kind}` |
+
+## Source
+
+- Source repo: `./data/psf_requests`
+- First `n_commits`: **200**
+- Source HEAD at build time: `514c1623fefff760bfa15a693aa38e474aba8560`
+- AST expansion: **enabled**
+- Features: BERT base uncased, frozen embedding lookup (not forward pass)
+  * `content_vec`: BERT-mean-pool of node.content
+  * `type_vec`:    BERT-mean-pool of node.type_description
+
+## SHA-256
+
+`9267fb1c7157cf3d9ca9f9e26f4802e28c530c08e15a184a2b1c7938a9c8af70`
+
+## Usage
+
+```python
+from huggingface_hub import hf_hub_download
+import pickle
+
+path = hf_hub_download(
+    repo_id="IDMedicine/graphjepa-psf-requests-200",
+    filename="graph.pkl",
+    repo_type="dataset",
+)
+with open(path, 'rb') as f:
+    payload = pickle.load(f)
+graph, features = payload['graph'], payload['features']
+```
+
+Requires the `graphjepa` package to be importable so the pickled
+dataclasses resolve. Install from the source tree:
+```bash
+pip install -e /path/to/code-transformer
+```