metadata
license: mit
base_model: lytang/MiniCheck-RoBERTa-Large
tags:
- coreml
- text-classification
- fact-checking
- grounding
language:
- en
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.