readme: ---
  license: mit
  widget:
  - text: "Din store idiot"
  ---
  
  # Danish Offensive Text Detection based on ELECTRA-small
  
  This model is a fine-tuned version of [Maltehb/aelaectra-danish-electra-small-cased](https://huggingface.co/Maltehb/aelaectra-danish-electra-small-cased) 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 almost achieves SOTA results while being 20x smaller, 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`](https://huggingface.co/alexandrainst/danoff-base) | 74.81% | **89.77%** | **81.61%** | **86.32%** |
  | `alexandrainst/da-offensive-detection-small` (this) | 74.13% | 89.30% | 81.01% | 85.79% |
  | [`A&ttack`](https://github.com/ogtal/A-ttack) | **97.32%** | 50.70% | 66.67% | 56.07% |
  | [`alexandrainst/da-hatespeech-detection-small`](https://huggingface.co/alexandrainst/da-hatespeech-detection-small) | 86.43% | 56.28% | 68.17% | 60.50% |
  | [`Guscode/DKbert-hatespeech-detection`](https://huggingface.co/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:
  
  ```python
  >>> from transformers import pipeline
  >>> offensive_text_pipeline = pipeline(model="alexandrainst/da-offensive-detection-small")
  >>> offensive_text_pipeline("Din store idiot")
  [{'label': 'Offensive', 'score': 0.9997463822364807}]
  ```
  
  Processing multiple documents at the same time can be done as follows:
  
  ```python
  >>> 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