|
|
--- |
|
|
dataset_name: transformers_code_embeddings |
|
|
license: apache-2.0 |
|
|
language: code |
|
|
tags: |
|
|
- embeddings |
|
|
- transformers-internal |
|
|
- similarity-search |
|
|
--- |
|
|
|
|
|
# Transformers Code Embeddings |
|
|
|
|
|
Compact index of function/class definitions from `src/transformers/models/**/modeling_*.py` for cross-model similarity. Built to help surface reusable code when modularizing models. |
|
|
|
|
|
## Contents |
|
|
|
|
|
- `embeddings.safetensors` — float32, L2-normalized embeddings shaped `[N, D]`. |
|
|
- `code_index_map.json` — `{int_id: "relative/path/to/modeling_*.py:SymbolName"}`. |
|
|
- `code_index_tokens.json` — `{identifier: [sorted_unique_tokens]}` for Jaccard. |
|
|
|
|
|
## How these were built |
|
|
|
|
|
- Source: 🤗 Transformers repository, under `src/transformers/models`. |
|
|
- Units: top-level `class`/`def` definitions. |
|
|
- Preprocessing: |
|
|
- Strip docstrings, comments, and import lines. |
|
|
- Replace occurrences of model names and symbol prefixes with `Model`. |
|
|
- Encoder: `Qwen/Qwen3-Embedding-4B` via `transformers` (mean pooling over tokens, then L2 normalize). |
|
|
- Output dtype: float32. |
|
|
|
|
|
> Note: Results are tied to a specific Transformers commit. Regenerate when the repo changes. |
|
|
|
|
|
## Quick usage |
|
|
|
|
|
```python |
|
|
from huggingface_hub import hf_hub_download |
|
|
from safetensors.numpy import load_file |
|
|
import json, numpy as np |
|
|
|
|
|
repo_id = "hf-internal-testing/transformers_code_embeddings" |
|
|
|
|
|
emb_path = hf_hub_download(repo_id, "embeddings.safetensors", repo_type="dataset") |
|
|
map_path = hf_hub_download(repo_id, "code_index_map.json", repo_type="dataset") |
|
|
tok_path = hf_hub_download(repo_id, "code_index_tokens.json", repo_type="dataset") |
|
|
|
|
|
emb = load_file(emb_path)["embeddings"] # (N, D) float32, L2-normalized |
|
|
id_map = {int(k): v for k, v in json.load(open(map_path))} |
|
|
tokens = json.load(open(tok_path)) |
|
|
|
|
|
# cosine similarity: dot product |
|
|
def topk(vec, k=10): |
|
|
sims = vec @ emb.T |
|
|
idx = np.argpartition(-sims, k)[:k] |
|
|
idx = idx[np.argsort(-sims[idx])] |
|
|
return [(id_map[int(i)], float(sims[i])) for i in idx] |
|
|
```` |
|
|
|
|
|
## Intended use |
|
|
|
|
|
* Identify similar symbols across models (embedding + Jaccard over tokens). |
|
|
* Assist refactors and modularization efforts. |
|
|
|
|
|
## Limitations |
|
|
|
|
|
* Embeddings reflect preprocessing choices and the specific encoder. |
|
|
* Symbols from the same file are present; filter by model name if needed. |
|
|
|
|
|
## Repro/build |
|
|
|
|
|
See `utils/modular_model_detector.py` in `transformers` repo for exact build & push commands. |
|
|
|
|
|
## License |
|
|
|
|
|
Apache-2.0 for this dataset card and produced artifacts. Source code remains under its original license in the upstream repo. |
|
|
|
|
|
``` |
|
|
|
