Danish Offensive Text Detection based on XLM-Roberta-Base
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 SOTA on a test set consisting of 600 Facebook comments annotated using majority vote by three annotators, of which 35.8% were labelled as offensive:
| Model | Precision | Recall | F1-score | F2-score |
|---|---|---|---|---|
alexandrainst/da-offensive-detection-base (this) |
74.81% | 89.77% | 81.61% | 86.32% |
alexandrainst/da-offensive-detection-small |
74.13% | 89.30% | 81.01% | 85.79% |
A&ttack |
97.32% | 50.70% | 66.67% | 56.07% |
alexandrainst/da-hatespeech-detection-small |
86.43% | 56.28% | 68.17% | 60.50% |
Guscode/DKbert-hatespeech-detection |
75.41% | 42.79% | 54.60% | 46.84% |
Using the model
You can use the model simply by running the following:
>>> from transformers import pipeline
>>> offensive_text_pipeline = pipeline(model="alexandrainst/da-offensive-detection-base")
>>> offensive_text_pipeline("Din store idiot")
[{'label': 'Offensive', 'score': 0.9997463822364807}]
Processing multiple documents at the same time can be done as follows:
>>> offensive_text_pipeline(["Din store idiot", "ej hvor godt :)"])
[{'label': 'Offensive', 'score': 0.9997463822364807}, {'label': 'Not offensive', 'score': 0.9996451139450073}]
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
