File size: 2,709 Bytes
a245625 0356e12 a245625 6ad0f02 a245625 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 |
---
language:
- fr
tags:
- lm-detection
datasets:
- hc3_fr_custom_ms_hg
metrics:
- f1
model-index:
- name: camemberta-chatgptdetect-noisy
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: HC3 FULL_FR_1.0_0.5_0.5
type: glue
config: full_fr_1.0_0.5_0.5
split: val
args: full_fr_1.0_0.5_0.5
metrics:
- name: F1
type: f1
value: 0.9790566381351302
---
# camemberta-chatgptdetect-noisy
French 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 [almanach/camemberta-base](https://huggingface.co/almanach/camemberta-base) on the HC3 FULL_FR_1.0_0.5_0.5 dataset with noise added.
It achieves the following results on the evaluation set:
- Loss: 0.0430
- F1: 0.9791
## Model description
This a model trained to detect text created by ChatGPT in French.
The training data is the `hc3_fr_full` subset 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: 25
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.0199 | 1.0 | 4267 | 0.0430 | 0.9791 |
| 0.0104 | 2.0 | 8534 | 0.1457 | 0.9463 |
| 0.0026 | 3.0 | 12801 | 0.0805 | 0.9720 |
| 0.0 | 4.0 | 17068 | 0.2515 | 0.9419 |
| 0.0 | 5.0 | 21335 | 0.2000 | 0.9567 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu115
- Datasets 2.8.0
- Tokenizers 0.13.2
|