gerskill-bert-job-extended

This model is a fine-tuned version of dathi103/bert-job-german-extended on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.0894
• Hard: {'precision': 0.8381742738589212, 'recall': 0.8898678414096917, 'f1': 0.8632478632478633, 'number': 454}
• Soft: {'precision': 0.7976190476190477, 'recall': 0.8170731707317073, 'f1': 0.8072289156626505, 'number': 82}
• Overall Precision: 0.8322
• Overall Recall: 0.8787
• Overall F1: 0.8548
• Overall Accuracy: 0.9776

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: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Hard	Soft	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
No log	1.0	178	0.1001	{'precision': 0.6720720720720721, 'recall': 0.8215859030837004, 'f1': 0.7393458870168483, 'number': 454}	{'precision': 0.6931818181818182, 'recall': 0.7439024390243902, 'f1': 0.7176470588235295, 'number': 82}	0.6750	0.8097	0.7362	0.9602
No log	2.0	356	0.0736	{'precision': 0.8148148148148148, 'recall': 0.8237885462555066, 'f1': 0.8192771084337348, 'number': 454}	{'precision': 0.7972972972972973, 'recall': 0.7195121951219512, 'f1': 0.7564102564102565, 'number': 82}	0.8124	0.8078	0.8101	0.9744
0.1105	3.0	534	0.0760	{'precision': 0.8280922431865828, 'recall': 0.8700440528634361, 'f1': 0.8485499462943071, 'number': 454}	{'precision': 0.8227848101265823, 'recall': 0.7926829268292683, 'f1': 0.8074534161490684, 'number': 82}	0.8273	0.8582	0.8425	0.9768
0.1105	4.0	712	0.0821	{'precision': 0.820040899795501, 'recall': 0.8832599118942731, 'f1': 0.8504772004241782, 'number': 454}	{'precision': 0.7790697674418605, 'recall': 0.8170731707317073, 'f1': 0.7976190476190477, 'number': 82}	0.8139	0.8731	0.8425	0.9760
0.1105	5.0	890	0.0894	{'precision': 0.8381742738589212, 'recall': 0.8898678414096917, 'f1': 0.8632478632478633, 'number': 454}	{'precision': 0.7976190476190477, 'recall': 0.8170731707317073, 'f1': 0.8072289156626505, 'number': 82}	0.8322	0.8787	0.8548	0.9776

Framework versions

• Transformers 4.38.1
• Pytorch 2.1.2+cu121
• Datasets 2.18.0
• Tokenizers 0.15.2