README.md

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 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/danoff-base (this)	74.81%	89.77%	81.61%	86.32%
alexandrainst/danoff-small	74.13%	89.30%	81.01%	85.79%
A&ttack	97.32%	50.70%	66.67%	56.07%
DaNLP/da-electra-hatespeech-detection	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="xlm-roberta-base-offensive-text-detection-da")
>>> 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