Create README.md

README.md

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+---
+language: 
+- nl
+tags:
+- text-classification
+- pytorch
+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"
+license: apache-2.0
+metrics:
+- Accuracy, F1 Score, Recall, Precision
+---
+# 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.