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--- |
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language: |
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- en |
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- fr |
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license: mit |
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tags: |
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- lm-detection |
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datasets: |
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- hc3_multi_custom_ms_hg |
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metrics: |
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- f1 |
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base_model: xlm-roberta-base |
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model-index: |
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- name: xlmr-chatgptdetect-noisy |
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results: |
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- task: |
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type: text-classification |
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name: Text Classification |
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dataset: |
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name: HC3 FULL_MULTI_1.0_0.5_0.5 |
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type: glue |
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config: full_multi_1.0_0.5_0.5 |
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split: vsl |
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args: full_multi_1.0_0.5_0.5 |
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metrics: |
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- type: f1 |
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value: 0.963274059512108 |
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name: F1 |
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--- |
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# xlmr-chatgptdetect-noisy |
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Multilingual ChatGPT detection model from [Towards a Robust Detection of Language Model-Generated Text: Is ChatGPT that easy to detect?](TODO:) |
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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. |
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It achieves the following results on the: |
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Evaluation set: |
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- Loss: 0.1573 |
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- F1: 0.9633 |
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Test Set: |
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- F1: 0.97 |
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Adversarial: |
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- F1: 0.45 |
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## Model description |
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This a model trained to detect text created by ChatGPT in French. |
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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. |
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## Intended uses & limitations |
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This model is for research purposes only. |
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It is not intended to be used in production as we said in our paper: |
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**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.** |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 1 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 32 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 5.0 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:| |
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| 0.0317 | 1.0 | 8538 | 0.1732 | 0.9492 | |
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| 0.008 | 2.0 | 17076 | 0.3541 | 0.9270 | |
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| 0.0085 | 3.0 | 25614 | 0.1161 | 0.9726 | |
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| 0.0015 | 4.0 | 34152 | 0.2557 | 0.9516 | |
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| 0.0 | 5.0 | 42690 | 0.2286 | 0.9650 | |
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### Framework versions |
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- Transformers 4.26.1 |
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- Pytorch 1.11.0+cu115 |
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- Datasets 2.8.0 |
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- Tokenizers 0.13.2 |
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