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license: mit
base_model: microsoft/unixcoder-base-nine
tags:
  - code
  - ai-generated-code-detection
  - classifier
library_name: transformers

code-detection-confound checkpoints

Three fine-tuned AI-generated-code detection classifiers from the AMindToThink/code-detection-confound research project. All three are cross-entropy-only (CE) fine-tunes of microsoft/unixcoder-base-nine; they differ only in training data.

Subfolder	Training data
`unixcoder_dc_ce/`	DroidCollection-Python
`python_raw_ce/`	HMCorp / Python
`java_raw_ce/`	HMCorp / Java

Each model.bin (~481 MB) is a raw PyTorch state_dict — no config.json or tokenizer is bundled. Load it on top of the microsoft/unixcoder-base-nine architecture + tokenizer. The exact training command (scripts/18_train_cgs_amp.py … --model_name_or_path microsoft/unixcoder-base-nine), data provenance, and the classification head are documented in the source repo's REPRODUCE.md.

Backed up here during a machine migration (2026-07-02); see the source repo for full reproduction details.