resume-ner-xlm

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

• Loss: 0.1579
• Certification: {'precision': 0.937007874015748, 'recall': 0.85, 'f1': 0.8913857677902622, 'number': 140}
• Domain Skill: {'precision': 0.820036092510804, 'recall': 0.8150237179335897, 'f1': 0.8175222223537347, 'number': 42373}
• Language: {'precision': 0.9469964664310954, 'recall': 0.9816849816849816, 'f1': 0.9640287769784173, 'number': 273}
• Soft Skill: {'precision': 0.8625, 'recall': 0.9409090909090909, 'f1': 0.9000000000000001, 'number': 220}
• Tech Skill: {'precision': 0.8503740648379052, 'recall': 0.8938401048492791, 'f1': 0.8715654952076678, 'number': 763}
• Overall Precision: 0.8220
• Overall Recall: 0.8182
• Overall F1: 0.8201
• Overall Accuracy: 0.9433

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: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Certification	Domain Skill	Language	Soft Skill	Tech Skill	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
0.2056	1.0	566	0.2044	{'precision': 0.940677966101695, 'recall': 0.7928571428571428, 'f1': 0.8604651162790697, 'number': 140}	{'precision': 0.7562669888251283, 'recall': 0.7682250489698629, 'f1': 0.7621991196028847, 'number': 42373}	{'precision': 0.9434628975265018, 'recall': 0.978021978021978, 'f1': 0.9604316546762589, 'number': 273}	{'precision': 0.8817204301075269, 'recall': 0.37272727272727274, 'f1': 0.5239616613418531, 'number': 220}	{'precision': 0.6643356643356644, 'recall': 0.747051114023591, 'f1': 0.7032695866748919, 'number': 763}	0.7564	0.7673	0.7618	0.9245
0.1506	2.0	1132	0.1642	{'precision': 0.937007874015748, 'recall': 0.85, 'f1': 0.8913857677902622, 'number': 140}	{'precision': 0.8205880913939038, 'recall': 0.7916361834186865, 'f1': 0.8058521819557724, 'number': 42373}	{'precision': 0.9535714285714286, 'recall': 0.978021978021978, 'f1': 0.9656419529837251, 'number': 273}	{'precision': 0.8481012658227848, 'recall': 0.9136363636363637, 'f1': 0.8796498905908097, 'number': 220}	{'precision': 0.8265179677819083, 'recall': 0.8741808650065531, 'f1': 0.8496815286624204, 'number': 763}	0.8221	0.7950	0.8083	0.9404
0.1498	3.0	1698	0.1579	{'precision': 0.937007874015748, 'recall': 0.85, 'f1': 0.8913857677902622, 'number': 140}	{'precision': 0.820036092510804, 'recall': 0.8150237179335897, 'f1': 0.8175222223537347, 'number': 42373}	{'precision': 0.9469964664310954, 'recall': 0.9816849816849816, 'f1': 0.9640287769784173, 'number': 273}	{'precision': 0.8625, 'recall': 0.9409090909090909, 'f1': 0.9000000000000001, 'number': 220}	{'precision': 0.8503740648379052, 'recall': 0.8938401048492791, 'f1': 0.8715654952076678, 'number': 763}	0.8220	0.8182	0.8201	0.9433

Framework versions

• Transformers 5.12.1
• Pytorch 2.5.1+cu121
• Datasets 5.0.0
• Tokenizers 0.22.2