metadata
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 on a dataset consisting of approximately 5 million Facebook comments on DR's public Facebook pages. The labels have been automatically generated using weak supervision, based on the Snorkel 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 |
85.45% | 91.26% | 88.26% |
alexandrainst/xlm-roberta-base-offensive-text-detection-da (this) |
83.48% | 93.20% | 88.07% |
A-ttack |
99.17% | 58.25% | 73.39% |
DaNLP/da-electra-hatespeech-detection |
92.19% | 57.28% | 70.66% |
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