---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
license: mit
base_model: intfloat/multilingual-e5-small
language:
  - vi
  - en
tags:
  - sentence-similarity
  - sentence-transformers
  - e5
  - vietnamese
  - onnx
  - fp32
  - retrieval
  - document-search
---

# E5-small mix50 v2 — Vietnamese archive embedder

Fine-tuned [`intfloat/multilingual-e5-small`](https://huggingface.co/intfloat/multilingual-e5-small) for retrieval on Vietnamese archived administrative documents. Trained on a 50/50 mix of (a) in-domain Vietnamese corpus and (b) general retrieval pairs, exported to ONNX fp32.

Used as the dense passage encoder in the [ScanIndex](https://github.com/welcomyou/scanindex) hybrid search (Tantivy BM25 + FAISS HNSW + RRF fusion).

## Files

- `archive_models/e5-small-mix50-v2-onnx-fp32/model.onnx` (+ `model.onnx_data`)
- Tokenizer + sentence-transformers metadata (`config.json`, `tokenizer.json`, `sentencepiece.bpe.model`, `1_Pooling/`, `modules.json`, …)

## Asymmetric input

E5 requires query/passage prefixes:

```python
queries  = [f"query: {q}" for q in raw_queries]
passages = [f"passage: {p}" for p in raw_passages]
```

## Loading (ONNX)

```python
import onnxruntime as ort
from transformers import AutoTokenizer
from huggingface_hub import snapshot_download

local = snapshot_download("welcomyou/e5-small-vn-archive-mix50", local_dir="models")
sub = f"{local}/archive_models/e5-small-mix50-v2-onnx-fp32"
tok = AutoTokenizer.from_pretrained(sub)
sess = ort.InferenceSession(f"{sub}/model.onnx")
```

## Training

See [train-convert/archive-embedder/train/mix50_v2/](https://github.com/welcomyou/scanindex/tree/main/train-convert/archive-embedder/train/mix50_v2).

## License

MIT, inherited from `intfloat/multilingual-e5-small`.