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README.md

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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
+```