---
license: apache-2.0
library_name: sentence-transformers
pipeline_tag: feature-extraction
tags:
  - sentence-transformers
  - feature-extraction
  - retrieval
  - devdata-search
datasets:
  - ai4data/devdatabench
base_model: avsolatorio/NoInstruct-small-Embedding-v0
---

# devdata-search-noinstruct-small-cmnrl

A bi-encoder embedding model for **search over structured statistical
metadata**, part of the **DevData Search** family. It is a fine-tune of
`avsolatorio/NoInstruct-small-Embedding-v0` produced with schema-invariant fine-tuning on
[DevDataBench](https://huggingface.co/datasets/ai4data/devdatabench): full-schema
serialization with per-example field-order permutation and field dropout, so the
encoder binds meaning to field labels rather than to serialization order. This is
an embedding model that powers retrieval; it is not a hosted search service.

See the paper *Field Order Should Not Matter: Permutation-Invariant Fine-Tuning
for Structured Metadata Retrieval*.

## Training

- Base model: `avsolatorio/NoInstruct-small-Embedding-v0`
- Loss: `cmnrl`
- Field permutation: `True`; field dropout: `0.15`
- Max sequence length: `512`
- No query/document prefixes

## Usage

```python
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("ai4data/devdata-search-noinstruct-small-cmnrl")
queries = ["mobile-broadband subscriptions per 100 people, reported annually"]
docs = ["name: Active mobile-broadband subscriptions | ..."]
q = model.encode(queries)
d = model.encode(docs)
```

Cosine similarity of `q` and `d` ranks documents for each query.

## License

Apache-2.0. Derived from `avsolatorio/NoInstruct-small-Embedding-v0`; trained on public World Bank Data360 metadata.