---
license: mit
base_model: intfloat/multilingual-e5-small
language:
- ru
- en
- multilingual
tags:
- coreml
- sentence-embeddings
- e5
library_name: coreml
pipeline_tag: feature-extraction
---

# multilingual-e5-small · Core ML

[intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small)
converted to a Core ML **ML Program** (fp16) for on-device semantic search in
the DND Master app (iPad / iPhone / Mac).

## What is inside

| File | Purpose |
|---|---|
| `MultilingualE5Small.mlpackage` | Core ML model. Inputs `input_ids`, `attention_mask` (`int32`, shape `[1, L]`, L ∈ {16, 32, 64, 128, 256, 512}); output `embedding` `[1, 384]` — masked mean pooling and L2 normalisation are inside the graph. |
| `tokenizer.json`, `tokenizer_config.json`, `special_tokens_map.json`, `config.json` | XLM-R tokenizer files from the upstream repo, unchanged. |

Pad token id is `1` (`<pad>`), attention mask is 1 for real tokens. Use the
e5 prefixes: `query: ` for queries, `passage: ` for documents.

## Conversion notes

- coremltools 9.0, torch 2.7.0, transformers 4.46.3, `minimum_deployment_target = iOS18`.
- Enumerated input shapes instead of a `RangeDim`: with a flexible range Core ML
  returned NaN (“Data-dependent shapes were disabled”).
- The additive attention mask was replaced by `-1e4` before tracing: the
  default `finfo(float32).min` overflows to `-inf` in fp16 and the softmax
  produces NaN.
- Parity with the PyTorch reference: cosine 1.000000 on test sentences; parity
  with an MLX fp16 embedding of a 12 270-chunk Russian corpus: min cosine 0.9997.
- Recommended compute units: `.cpuAndNeuralEngine`. The GPU path is ~25× slower
  for this graph on Apple silicon.

The conversion script lives in the app repository (`BestiaryEmbedder/convert-e5-coreml.py`).

## License

The weights are derived from `intfloat/multilingual-e5-small` (MIT).