---
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. Drop into
`graphjepa/cache/graph_<basename>_n<N>_ast.pkl` and training
loops will auto-hit the cache with no model downloads required.

## Contents

| File | Description |
|---|---|
| `graph_psf_requests_n200_ast.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

`2cca40c0bf0621005b90f96c091f73c4d15519eeed3a88896b5e6fc7619f087d`

## Usage

```python
import os, pickle
from huggingface_hub import hf_hub_download

os.makedirs('graphjepa/cache', exist_ok=True)
path = hf_hub_download(
    repo_id="IDMedicine/graphjepa-psf-requests-200",
    filename="graph_psf_requests_n200_ast.pkl",
    repo_type="model",
    local_dir='graphjepa/cache',
)

# Once in graphjepa/cache, load_or_build_graph() auto-hits:
from graphjepa.graph_cache import load_or_build_graph
graph, features = load_or_build_graph(
    './data/psf_requests', n_commits=200, expand_ast=True)
```

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
```