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--- |
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license: mit |
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widget: |
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- text: "Din store idiot" |
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--- |
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# Danish Offensive Text Detection based on XLM-Roberta-Base |
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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. |
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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: |
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| **Model** | **Precision** | **Recall** | **F1-score** | **F2-score** | |
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| :-------- | :------------ | :--------- | :----------- | :----------- | |
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| `alexandrainst/danoff-base` (this) | 74.81% | **89.77%** | **81.61%** | **86.32%** | |
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| [`alexandrainst/danoff-small`](https://huggingface.co/alexandrainst/danoff-small) | 74.13% | 89.30% | 81.01% | 85.79% | |
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| [`A&ttack`](https://github.com/ogtal/A-ttack) | **97.32%** | 50.70% | 66.67% | 56.07% | |
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| [`alexandrainst/da-electra-hatespeech-detection`](https://huggingface.co/alexandrainst/da-electra-hatespeech-detection) | 86.43% | 56.28% | 68.17% | 60.50% | |
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| [`Guscode/DKbert-hatespeech-detection`](https://huggingface.co/Guscode/DKbert-hatespeech-detection) | 75.41% | 42.79% | 54.60% | 46.84% | |
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## Using the model |
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You can use the model simply by running the following: |
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```python |
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>>> from transformers import pipeline |
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>>> offensive_text_pipeline = pipeline(model="alexandrainst/danoff-base") |
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>>> offensive_text_pipeline("Din store idiot") |
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[{'label': 'Offensive', 'score': 0.9997463822364807}] |
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``` |
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Processing multiple documents at the same time can be done as follows: |
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```python |
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>>> offensive_text_pipeline(["Din store idiot", "ej hvor godt :)"]) |
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[{'label': 'Offensive', 'score': 0.9997463822364807}, {'label': 'Not offensive', 'score': 0.9996451139450073}] |
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``` |
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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: 32 |
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- eval_batch_size: 32 |
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- gradient_accumulation_steps: 1 |
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- total_train_batch_size: 32 |
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- seed: 4242 |
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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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- max_steps: 500000 |
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- fp16: True |
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- eval_steps: 1000 |
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- early_stopping_patience: 100 |
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### Framework versions |
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- Transformers 4.20.1 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.3.2 |
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- Tokenizers 0.12.1 |
