gbert-large-topic_classification

This model is a fine-tuned version of deepset/gbert-large on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.5093
• Precision: 0.9100
• Recall: 0.8993
• F1: 0.9042
• Accuracy: 0.9167

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: 5e-05
• train_batch_size: 16
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	44	0.6465	0.8897	0.8138	0.8179	0.8480
No log	2.0	88	0.2949	0.9116	0.9110	0.9106	0.9118
No log	3.0	132	0.4110	0.9298	0.8939	0.9020	0.9167
No log	4.0	176	0.6242	0.9261	0.8756	0.8911	0.9020
No log	5.0	220	0.5606	0.9208	0.8757	0.8897	0.9020
No log	6.0	264	0.6164	0.9201	0.8867	0.9001	0.9069
No log	7.0	308	0.4898	0.9155	0.9001	0.9071	0.9167
No log	8.0	352	0.4999	0.9191	0.9029	0.9102	0.9216
No log	9.0	396	0.5073	0.9100	0.8993	0.9042	0.9167
No log	10.0	440	0.5093	0.9100	0.8993	0.9042	0.9167

Framework versions

• Transformers 4.44.2
• Pytorch 2.4.0+cu121
• Datasets 2.21.0
• Tokenizers 0.19.1