---
license: mit
tags:
  - coreml
  - sentence-transformers
  - embedding
  - code
  - roberta
base_model: microsoft/codebert-base
library_name: coremltools
pipeline_tag: feature-extraction
---

# codebert-base — CoreML (.mlpackage)

CoreML conversion of [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) for native Apple Neural Engine / GPU inference on macOS and iOS.

## Files

| File | Description |
|------|-------------|
| `model.mlpackage/` | CoreML model (FP16, flexible shapes) |
| `tokenizer.json` | HF fast tokenizer |

## Details

- **Architecture**: RoBERTa (encoder-only, no token_type_ids)
- **Precision**: FP16 (native ANE precision)
- **Compute units**: `.all` — CoreML schedules across ANE, GPU, and CPU
- **Input shapes**: batch=1..512, seq_len=1..512 (flexible range)
- **Embedding dimension**: 768

## Usage with cai

```bash
cai index --embed-backend swift --embed-model "rsvalerio/codebert-base-coreml"
```

The Swift backend downloads the `.mlpackage` from this repo, compiles it to `.mlmodelc` on first run (~30-60s), and caches the compiled model for subsequent runs.

## Conversion

Converted using [rsvalerio/models](https://github.com/rsvalerio/models) CI pipeline with `coremltools`.

```bash
pip install coremltools transformers torch
python convert.py
```