murat_chem_model_extra_data_2epoch

This model is a fine-tuned version of alvaroalon2/biobert_chemical_ner on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0100
• Chemical: {'precision': 0.9535374868004224, 'recall': 0.9485294117647058, 'f1-score': 0.9510268562401265, 'support': 952}
• Micro avg: {'precision': 0.9535374868004224, 'recall': 0.9485294117647058, 'f1-score': 0.9510268562401265, 'support': 952}
• Macro avg: {'precision': 0.9535374868004224, 'recall': 0.9485294117647058, 'f1-score': 0.9510268562401265, 'support': 952}
• Weighted avg: {'precision': 0.9535374868004224, 'recall': 0.9485294117647058, 'f1-score': 0.9510268562401264, 'support': 952}

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-06
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 2
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Chemical	Micro avg	Macro avg	Weighted avg
0.0381	1.0000	16252	0.0113	{'precision': 0.9494204425711275, 'recall': 0.9464285714285714, 'f1-score': 0.9479221462388217, 'support': 952}	{'precision': 0.9494204425711275, 'recall': 0.9464285714285714, 'f1-score': 0.9479221462388217, 'support': 952}	{'precision': 0.9494204425711275, 'recall': 0.9464285714285714, 'f1-score': 0.9479221462388217, 'support': 952}	{'precision': 0.9494204425711275, 'recall': 0.9464285714285714, 'f1-score': 0.9479221462388217, 'support': 952}
0.0105	1.9999	32504	0.0100	{'precision': 0.9535374868004224, 'recall': 0.9485294117647058, 'f1-score': 0.9510268562401265, 'support': 952}	{'precision': 0.9535374868004224, 'recall': 0.9485294117647058, 'f1-score': 0.9510268562401265, 'support': 952}	{'precision': 0.9535374868004224, 'recall': 0.9485294117647058, 'f1-score': 0.9510268562401265, 'support': 952}	{'precision': 0.9535374868004224, 'recall': 0.9485294117647058, 'f1-score': 0.9510268562401264, 'support': 952}

Framework versions

• Transformers 4.44.2
• Pytorch 2.3.0+cu121
• Datasets 2.21.0
• Tokenizers 0.19.1