|
--- |
|
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](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. |
|
|
|
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/xlm-roberta-base-offensive-text-detection-da` (this) | 74.81% | **89.77%** | **81.61%** | **86.32%** | |
|
| [`alexandrainst/electra-small-offensive-text-detection-da`](https://huggingface.co/alexandrainst/electra-small-offensive-text-detection-da) | 74.13% | 89.30% | 81.01% | 85.79% | |
|
| [`A&ttack`](https://github.com/ogtal/A-ttack) | **97.32%** | 50.70% | 66.67% | 56.07% | |
|
| [`DaNLP/da-electra-hatespeech-detection`](https://huggingface.co/DaNLP/da-electra-hatespeech-detection) | 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="xlm-roberta-base-offensive-text-detection-da") |
|
>>> offensive_text_pipeline("Din store idiot") |
|
[{'label': 'offensive', 'score': 0.9874388575553894}] |
|
``` |
|
|
|
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': 0.9874388575553894}, {'label': 'not offensive', 'score': 0.999760091304779}] |
|
``` |
|
|
|
## 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 |
