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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
```
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