RobCaamano/toxicity_weighted

This model was trained from scratch on Distilbert Base Uncased. It achieves the following results on the evaluation set:

• Train Loss: 0.0240
• Train Precision: 0.9522
• Train Recall: 0.9190
• Epoch: 11

Model description

Finetuned model that uses Distilbert Base Uncased to detect types of toxic text. These include: "toxic", "severe_toxic", "obscene", "threat", "insult" & "identity_hate".

Intended uses & limitations

Intended to classify text into different types of toxicity when it is detected. Trained off a small dataset with underrepresented categories.

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• optimizer: {'name': 'Adam', 'learning_rate': 3e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
• training_precision: float32

Training results

Train Loss	Train Precision	Train Recall	Epoch
0.0440	0.9059	0.8294	7
0.0380	0.9223	0.8632	8
0.0314	0.9335	0.8838	9
0.0282	0.9437	0.9075	10
0.0240	0.9522	0.9190	11

Framework versions

• Transformers 4.28.1
• TensorFlow 2.10.0
• Datasets 2.11.0
• Tokenizers 0.13.3