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library_name: pytorch
tags:
- table-retrieval
- embedding-adapter
- centroid-adapter
license: mit
---
# Centroid Adapter — splade
Lightweight **BottleneckResidualAdapter** trained on top of
[splade](https://huggingface.co/splade) embeddings to produce
representation-invariant table embeddings.
## Architecture
```
z = e + α · Up( Dropout( GELU( Down( LN(e) ) ) ) )
```
| Hyperparameter | Value |
|---|---|
| Embedding dim `d` | 30522 |
| Bottleneck rank `r` | 512 |
| Residual scale `α` | 0.01 |
| Use bias | True |
Trained on: WTQ, WIKISQL
## Usage
```python
import torch
from huggingface_hub import hf_hub_download
import json
# --- option A: use the from_pretrained helper in this repo ---
# (copy BottleneckResidualAdapter + from_pretrained from push_to_hub.py)
adapter = BottleneckResidualAdapter.from_pretrained("KBhandari11/centroid-adapter-subset-splade")
e = torch.randn(1, 30522) # your backbone embedding
z = adapter(e) # representation-invariant embedding
# --- option B: hf_hub_download one-liner ---
from safetensors.torch import load_file
weights_path = hf_hub_download("KBhandari11/centroid-adapter-subset-splade", "model.safetensors")
cfg_path = hf_hub_download("KBhandari11/centroid-adapter-subset-splade", "config.json")
with open(cfg_path) as f:
cfg = json.load(f)
adapter = BottleneckResidualAdapter(**cfg)
adapter.load_state_dict(load_file(weights_path))
adapter.eval()
```
# Research Paper
[Improving Robustness of Tabular Retrieval via Representational Stability](https://arxiv.org/abs/2604.24040v2)
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