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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`](https://github.com/AMindToThink/code-detection-confound)
research project. All three are cross-entropy-only (CE) fine-tunes of
[`microsoft/unixcoder-base-nine`](https://huggingface.co/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.
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