| --- |
| 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. |
|
|
| ``` |
| |
