distilbert-multilabel-job-ablehnung_test9

This model is a fine-tuned version of distilbert-base-german-cased on the None dataset. It achieves the following results on the evaluation set:

• Loss: 2.0690
• Accuracy: 0.1542
• Precision: 0.0238
• Recall: 0.1542
• F1: 0.0412

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: 3e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 16
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 800
• num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
4.1505	1.0	60	2.0690	0.1542	0.0238	0.1542	0.0412

Framework versions

• Transformers 4.47.1
• Pytorch 2.5.1+cu121
• Datasets 2.14.4
• Tokenizers 0.21.0