This model is a fine-tuned version of DTAI-KULeuven/robbert-2023-dutch-large on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.7601
Accuracy: 0.8271
F1: 0.8098

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 256
eval_batch_size: 512
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 512
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 0.06
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.6922	1.0	5627	0.7867	0.8216	0.7971
0.6675	2.0	11254	0.7577	0.8269	0.8108
0.6406	3.0	16881	0.7601	0.8271	0.8098

Framework versions

Transformers 5.3.0
Pytorch 2.12.0.dev20260403+cu132
Datasets 4.8.2
Tokenizers 0.22.2

sytnaxerror/robbert-large-with-toxi-text

robbert-large-with-toxi-text

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

sytnaxerror/
robbert-large-with-toxi-text