--- language: - en - fr license: mit tags: - lm-detection datasets: - hc3_multi_custom_ms_hg metrics: - f1 base_model: xlm-roberta-base model-index: - name: xlmr-chatgptdetect-noisy results: - task: type: text-classification name: Text Classification dataset: name: HC3 FULL_MULTI_1.0_0.5_0.5 type: glue config: full_multi_1.0_0.5_0.5 split: vsl args: full_multi_1.0_0.5_0.5 metrics: - type: f1 value: 0.963274059512108 name: F1 --- # xlmr-chatgptdetect-noisy Multilingual ChatGPT detection model from [Towards a Robust Detection of Language Model-Generated Text: Is ChatGPT that easy to detect?](TODO:) This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the HC3 FULL_MULTI_1.0_0.5_0.5 dataset with noise added. It achieves the following results on the: Evaluation set: - Loss: 0.1573 - F1: 0.9633 Test Set: - F1: 0.97 Adversarial: - F1: 0.45 ## Model description This a model trained to detect text created by ChatGPT in French. The training data is the combination of the `hc3_fr_full` and `hc3_en_full` subsets of [almanach/hc3_multi](https://huggingface.co/datasets/almanach/hc3_french_ood), but with added misspelling and homoglyph attacks. ## Intended uses & limitations This model is for research purposes only. It is not intended to be used in production as we said in our paper: **We would like to emphasize that our study does not claim to have produced an universally accurate detector. Our strong results are based on in-domain testing and, unsurprisingly, do not generalize in out-of-domain scenarios. This is even more so when used on text specifically designed to fool language model detectors and on text intentionally stylistically similar to ChatGPT-generated text, especially instructional text.** ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.0317 | 1.0 | 8538 | 0.1732 | 0.9492 | | 0.008 | 2.0 | 17076 | 0.3541 | 0.9270 | | 0.0085 | 3.0 | 25614 | 0.1161 | 0.9726 | | 0.0015 | 4.0 | 34152 | 0.2557 | 0.9516 | | 0.0 | 5.0 | 42690 | 0.2286 | 0.9650 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu115 - Datasets 2.8.0 - Tokenizers 0.13.2