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--- |
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language: |
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- nl |
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tags: |
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- text-classification |
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- pytorch |
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widget: |
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- text: "Ik heb je lief met heel mijn hart" |
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example_title: "Non toxic comment 1" |
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- text: "Dat is een goed punt, zo had ik het nog niet bekeken." |
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example_title: "Non toxic comment 2" |
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- text: "Wat de fuck zei je net tegen me, klootzak?" |
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example_title: "Toxic comment 1" |
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- text: "Rot op, vuile hoerenzoon." |
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example_title: "Toxic comment 2" |
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license: apache-2.0 |
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metrics: |
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- Accuracy, F1 Score, Recall, Precision |
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--- |
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# RobBERT-dutch-base-toxic-comments |
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## Model description: |
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This model was created with the purpose to detect toxic or potentially harmful comments. |
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For this model, we finetuned a dutch RobBerta-based model called [RobBERT](https://huggingface.co/pdelobelle/robbert-v2-dutch-base) on the translated [Jigsaw Toxicity dataset](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge). |
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The original dataset was translated using the appropriate [MariantMT model](https://huggingface.co/Helsinki-NLP/opus-mt-en-nl). |
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The model was trained for 2 epochs, on 90% of the dataset, with the following arguments: |
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``` |
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training_args = TrainingArguments( |
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learning_rate=1e-5, |
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per_device_train_batch_size=8, |
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per_device_eval_batch_size=8, |
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gradient_accumulation_steps=6, |
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load_best_model_at_end=True, |
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metric_for_best_model="recall", |
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epochs=2, |
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evaluation_strategy="steps", |
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save_strategy="steps", |
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save_total_limit=10, |
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logging_steps=100, |
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eval_steps=250, |
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save_steps=250, |
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weight_decay=0.001, |
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report_to="wandb") |
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``` |
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## Model Performance: |
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Model evaluation was done on 1/10th of the dataset, which served as the test dataset. |
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| Accuracy | F1 Score | Recall | Precision | |
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| --- | --- | --- | --- | |
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| 95.63 | 78.80 | 78.99 | 78.61 | |
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## Dataset: |
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Unfortunately we cannot open-source the dataset, since we are bound by the underlying Jigsaw license. |
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