|
--- |
|
license: mit |
|
model-index: |
|
- name: xlm-roberta-base-offensive-text-detection-da |
|
results: [] |
|
widget: |
|
- text: "Din store idiot" |
|
--- |
|
|
|
# Danish Offensive Text Detection based on ELECTRA-small |
|
|
|
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on a dataset consisting of approximately 5 million Facebook comments on [DR](https://dr.dk/)'s public Facebook pages. The labels have been automatically generated using weak supervision, based on the [Snorkel](https://www.snorkel.org/) framework. |
|
|
|
The model achieves second place on a test set consisting of 500 Facebook comments annotated by two people, of which 41.2% were labelled as offensive: |
|
|
|
| **Model** | **Precision** | **Recall** | **F1-score** | |
|
| :-------- | :------------ | :--------- | :----------- | |
|
| [`alexandrainst/electra-small-offensive-text-detection-da`](https://huggingface.co/alexandrainst/electra-small-offensive-text-detection-da) | 85.45% | 91.26% | **88.26%** | |
|
| `alexandrainst/xlm-roberta-base-offensive-text-detection-da` (this) | 83.48% | **93.20%** | 88.07% | |
|
| [`A-ttack`](https://github.com/ogtal/A-ttack) | **99.17%** | 58.25% | 73.39% | |
|
| [`DaNLP/da-electra-hatespeech-detection`](https://huggingface.co/DaNLP/da-electra-hatespeech-detection) | 92.19% | 57.28% | 70.66% | |
|
| [`Guscode/DKbert-hatespeech-detection`](https://huggingface.co/Guscode/DKbert-hatespeech-detection) | 84.91% | 43.69% | 57.69% | |
|
|
|
## Training procedure |
|
|
|
### Training hyperparameters |
|
|
|
The following hyperparameters were used during training: |
|
- learning_rate: 2e-05 |
|
- train_batch_size: 32 |
|
- eval_batch_size: 32 |
|
- gradient_accumulation_steps: 1 |
|
- total_train_batch_size: 32 |
|
- seed: 4242 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- max_steps: 500000 |
|
- fp16: True |
|
- eval_steps: 1000 |
|
- early_stopping_patience: 100 |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.20.1 |
|
- Pytorch 1.11.0+cu113 |
|
- Datasets 2.3.2 |
|
- Tokenizers 0.12.1 |
