Update README.md

README.md

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----
-license: apache-2.0
----
+---
+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.
+
+```