# distilbert-base-dutch-toxic-comments

## Model description:
This model was created with the purpose to detect toxic or potentially harmful comments.

For this model, we finetuned a multilingual distilbert model [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the translated [Jigsaw Toxicity dataset](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge).

The original dataset was translated using the appropriate [MariantMT model](https://huggingface.co/Helsinki-NLP/opus-mt-en-nl).

The model was trained for 2 epochs, on 90% of the dataset, with the following arguments:
```
training_args = TrainingArguments(
    learning_rate=3e-5,
    per_device_train_batch_size=16,
    per_device_eval_batch_size=16,
    gradient_accumulation_steps=4,
    load_best_model_at_end=True,
    metric_for_best_model="recall",
    epochs=2,
    evaluation_strategy="steps",
    save_strategy="steps",
    save_total_limit=10,
    logging_steps=100,
    eval_steps=250,
    save_steps=250,
    weight_decay=0.001,
    report_to="wandb")
```

## Model Performance:

Model evaluation was done on 1/10th of the dataset, which served as the test dataset.

| Accuracy | F1 Score |  Recall  |  Precision  |
| --- | --- | --- | --- |
| 95.75 | 78.88 | 77.23 | 80.61 |

## Dataset:
Unfortunately we cannot open-source the dataset, since we are bound by the underlying Jigsaw license.

{"language": ["nl"], "license": "apache-2.0", "tags": ["text-classification", "pytorch"], "metrics": ["Accuracy, F1 Score, Recall, Precision"], "widget": [{"text": "Ik heb je lief met heel mijn hart", "example_title": "Non toxic comment 1"}, {"text": "Dat is een goed punt, zo had ik het nog niet bekeken.", "example_title": "Non toxic comment 2"}, {"text": "Wat de fuck zei je net tegen me, klootzak?", "example_title": "Toxic comment 1"}, {"text": "Rot op, vuile hoerenzoon.", "example_title": "Toxic comment 2"}]}
transformers pytorch distilbert text-classification nl license:apache-2.0 autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #distilbert #text-classification #nl #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 

distilbert-base-dutch-toxic-comments
====================================


Model description:
------------------


This model was created with the purpose to detect toxic or potentially harmful comments.


For this model, we finetuned a multilingual distilbert model distilbert-base-multilingual-cased on the translated Jigsaw Toxicity dataset.


The original dataset was translated using the appropriate MariantMT model.


The model was trained for 2 epochs, on 90% of the dataset, with the following arguments:


Model Performance:
------------------


Model evaluation was done on 1/10th of the dataset, which served as the test dataset.



Dataset:
--------


Unfortunately we cannot open-source the dataset, since we are bound by the underlying Jigsaw license.



TAGS
#transformers #pytorch #distilbert #text-classification #nl #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 

TAGS
#transformers #pytorch #distilbert #text-classification #nl #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
