| --- |
| license: apache-2.0 |
| language: |
| - en |
| - ar |
| tags: |
| - retrieval |
| - search |
| - sparse-encoder |
| - splade |
| - bge-m3 |
| - lora |
| library_name: peft |
| --- |
| |
| # Search Stack — query towers |
|
|
| The **query-side** encoders for a two-stage product search over a multilingual |
| (EN + AR) fashion catalogue. These are the exact weights that produced the |
| vectors in the Qdrant collection they are used against — a cosine ANN is only |
| meaningful if query and documents share an encoder, so these are part of the |
| retrieval contract, not an interchangeable dependency. |
|
|
| | path | size | what it is | |
| |---|---|---| |
| | `adapter/` | 4.7 MB | LoRA (r=8, q/k/v) on `BAAI/bge-m3` — the dense query tower | |
| | `tokenizer/` | 17 MB | matching bge-m3 tokenizer | |
| | `splade_v6_v2/` | 545 MB | fine-tuned SPLADE sparse encoder (distilbert-base-multilingual-cased) | |
|
|
| The `BAAI/bge-m3` backbone is not included — it is unmodified upstream and |
| `transformers` fetches it on first run. |
|
|
| ## Use |
|
|
| ```python |
| from huggingface_hub import snapshot_download |
| snapshot_download("rdxtremity/jev-reranking", |
| allow_patterns=["adapter/*", "tokenizer/*", "splade_v6_v2/*"], |
| local_dir="models") |
| ``` |
|
|
| Dense query encoding is CLS-pool + L2-normalise, max 64 tokens, producing a |
| 1024-d unit vector: |
|
|
| ```python |
| from transformers import AutoModel, AutoTokenizer |
| from peft import PeftModel |
| import torch |
| |
| tok = AutoTokenizer.from_pretrained("models/tokenizer") |
| model = PeftModel.from_pretrained(AutoModel.from_pretrained("BAAI/bge-m3"), |
| "models/adapter").eval() |
| enc = tok(["red dress"], return_tensors="pt", truncation=True, max_length=64) |
| with torch.no_grad(): |
| out = model(**enc) |
| vec = torch.nn.functional.normalize(out.last_hidden_state[:, 0], p=2, dim=-1) |
| ``` |
|
|
| Sparse encoding uses `sentence_transformers.SparseEncoder` on `splade_v6_v2/`, |
| with `max_seq_length = 128`. |
|
|
| Only one short forward pass runs at query time, so CPU is sufficient. |
|
|
