graphjepa-psf-requests-200

Precomputed (TemporalGraph, node_features) cache for the graphjepa 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

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:

pip install -e /path/to/code-transformer