simst/MiniCheck-RoBERTa-Large-CoreML

MiniCheck-RoBERTa-Large — Core ML (Apple Neural Engine)

Core ML conversion of lytang/MiniCheck-RoBERTa-Large (MIT) — a specialized grounding / fact-verification model — for in-app use on the Apple Neural Engine via Core ML. Used by Marvel Mirror AI as a claim-by-claim faithfulness judge: does a source support a claim?

Contents

• MiniCheckRoBERTa.mlpackage — the Core ML model (fp16 weights). Inputs: input_ids, attention_mask (int32, length 512). Output: support_prob = probability the claim is supported (class 1).
• RoBERTa fast-tokenizer files (tokenizer.json, vocab.json, merges.txt, tokenizer_config.json, special_tokens_map.json).

Input format

doc + </s> + claim, tokenized with the RoBERTa tokenizer (max_length 512, padded). support_prob > 0.5 = supported.

Provenance

Converted with coremltools 9.0 (torch 2.7.0 / transformers 4.46.3), targeting CPU + Neural Engine. ~97% of compute-bearing ops run on the ANE. Verdict-parity with the PyTorch source (max probability diff < 0.007); reproduces the source's full-set accuracy (21/21 fabrications caught, incl. all meaning- and numeric-inversions). ~60 ms/check on the ANE.