license: mit
base_model: microsoft/unixcoder-base
library_name: transformers
pipeline_tag: text-classification
datasets:
- mparts/CODer-aigcodeset
- basakdemirok/AIGCodeSet
tags:
- code
- python
- ai-generated-code-detection
metrics:
- f1
CODer: UniXcoder checkpoints
Fine-tuned UniXcoder classifiers that detect LLM-written Python code, trained on mparts/CODer-aigcodeset, a cleaned version of AIGCodeSet split by programming problem. They are the models behind Detecting LLM-Written Code on AIGCodeSet: Tuned Code Encoders versus N-gram Baselines. The code, the predictions and the paper are at github.com/mparts/CODer.
Layout
24 checkpoints: six seeds (seed3 to seed8) × four task variants. The paper's seed ensemble averages the six seeds' probabilities.
| Folder | Task | Labels |
|---|---|---|
seed<N>/binary |
human vs LLM, all data | 0 human, 1 ai |
seed<N>/binary_generate |
human vs LLM, without LLM-edited code | 0 human, 1 ai |
seed<N>/multiclass |
human or which LLM, all data | 0 Human, 1 GEMINI, 2 LLAMA, 3 CODESTRAL |
seed<N>/multiclass_generate |
human or which LLM, without LLM-edited code | 0 Human, 1 GEMINI, 2 LLAMA, 3 CODESTRAL |
The configs keep the default label names (LABEL_0, LABEL_1, ...); the table gives their meaning.
Usage
from transformers import AutoModelForSequenceClassification, AutoTokenizer
repo, variant = "mparts/CODer-unixcoder", "seed3/binary"
tok = AutoTokenizer.from_pretrained(repo, subfolder=variant)
model = AutoModelForSequenceClassification.from_pretrained(repo, subfolder=variant)
The models were trained on whitespace-normalised code (line endings unified, tabs expanded to four spaces, trailing spaces and outer blank lines removed: normalise() in src/code_origin/data/preprocess.py), truncated to 256 tokens. Normalise inputs the same way.
Training
Fine-tuned on the train split of mparts/CODer-aigcodeset, the repository's problem-grouped split of AIGCodeSet, in which no programming problem is shared between train, dev and test (rows flagged conflict dropped): 4 epochs, learning rate 2e-5, batch size 16, max length 256, weight decay 0.01, warmup ratio 0.1. The saved weights are from the epoch with the best dev macro-F1.
Limitations
Trained on Python solutions to 317 CodeNet problems, against three LLMs (CodeLlama 34B, Codestral 22B, Gemini 1.5 Flash). It is a research artefact for this setting, not a general-purpose detector of AI-written code.
License
The weights are released under the MIT license, like the code. The training data, mparts/CODer-aigcodeset (derived from AIGCodeSet), is under CDLA-Permissive-2.0.