File size: 1,885 Bytes
9088957 c20cf7b 6a7fa81 c20cf7b 657b74b c20cf7b 9088957 6d2d95f 9088957 0cc82d6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 | ---
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
---
# RobBERT-dutch-base-toxic-comments
## Model description:
This model was created with the purpose to detect toxic or potentially harmful comments.
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).
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=1e-5,
per_device_train_batch_size=8,
per_device_eval_batch_size=8,
gradient_accumulation_steps=6,
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 |
| --- | --- | --- | --- |
| 95.63 | 78.80 | 78.99 | 78.61 |
## Dataset:
Unfortunately we cannot open-source the dataset, since we are bound by the underlying Jigsaw license.
|