--- base_model: medicalai/ClinicalBERT tags: - generated_from_trainer model-index: - name: BioNLP13CG_ClinicalBERT_NER results: [] --- # BioNLP13CG_ClinicalBERT_NER This model is a fine-tuned version of [medicalai/ClinicalBERT](https://huggingface.co/medicalai/ClinicalBERT) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3339 - Seqeval classification report: precision recall f1-score support Amino_acid 0.81 0.59 0.68 297 Anatomical_system 0.70 0.78 0.74 297 Cancer 0.74 0.73 0.73 3490 Cell 0.72 0.87 0.79 1360 Cellular_component 0.00 0.00 0.00 99 Developing_anatomical_structure 0.00 0.00 0.00 11 Gene_or_gene_product 0.67 0.25 0.37 174 Immaterial_anatomical_entity 0.52 0.76 0.62 432 Multi-tissue_structure 0.83 0.59 0.69 317 Organ 0.00 0.00 0.00 49 Organism 0.71 0.48 0.57 464 Organism_subdivision 0.70 0.72 0.71 678 Organism_substance 0.00 0.00 0.00 128 Pathological_formation 0.62 0.05 0.09 108 Simple_chemical 0.00 0.00 0.00 56 Tissue 0.80 0.85 0.82 1566 micro avg 0.73 0.71 0.72 9526 macro avg 0.49 0.42 0.43 9526 weighted avg 0.71 0.71 0.70 9526 ## 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: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Seqeval classification report | |:-------------:|:-----:|:----:|:---------------:|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:| | No log | 0.99 | 95 | 0.4681 | precision recall f1-score support Amino_acid 1.00 0.02 0.04 297 Anatomical_system 0.44 0.68 0.54 297 Cancer 0.68 0.63 0.65 3490 Cell 0.59 0.85 0.70 1360 Cellular_component 0.00 0.00 0.00 99 Developing_anatomical_structure 0.00 0.00 0.00 11 Gene_or_gene_product 0.00 0.00 0.00 174 Immaterial_anatomical_entity 0.40 0.60 0.48 432 Multi-tissue_structure 0.86 0.06 0.11 317 Organ 0.00 0.00 0.00 49 Organism 0.88 0.02 0.03 464 Organism_subdivision 0.62 0.54 0.58 678 Organism_substance 0.00 0.00 0.00 128 Pathological_formation 0.00 0.00 0.00 108 Simple_chemical 0.00 0.00 0.00 56 Tissue 0.70 0.84 0.76 1566 micro avg 0.63 0.58 0.60 9526 macro avg 0.39 0.27 0.24 9526 weighted avg 0.63 0.58 0.55 9526 | | No log | 2.0 | 191 | 0.3526 | precision recall f1-score support Amino_acid 0.81 0.52 0.63 297 Anatomical_system 0.66 0.77 0.71 297 Cancer 0.74 0.73 0.73 3490 Cell 0.71 0.87 0.78 1360 Cellular_component 0.00 0.00 0.00 99 Developing_anatomical_structure 0.00 0.00 0.00 11 Gene_or_gene_product 0.76 0.20 0.32 174 Immaterial_anatomical_entity 0.46 0.76 0.57 432 Multi-tissue_structure 0.83 0.57 0.68 317 Organ 0.00 0.00 0.00 49 Organism 0.68 0.44 0.54 464 Organism_subdivision 0.71 0.67 0.69 678 Organism_substance 0.00 0.00 0.00 128 Pathological_formation 1.00 0.01 0.02 108 Simple_chemical 0.00 0.00 0.00 56 Tissue 0.78 0.85 0.81 1566 micro avg 0.72 0.70 0.71 9526 macro avg 0.51 0.40 0.41 9526 weighted avg 0.70 0.70 0.68 9526 | | No log | 2.98 | 285 | 0.3339 | precision recall f1-score support Amino_acid 0.81 0.59 0.68 297 Anatomical_system 0.70 0.78 0.74 297 Cancer 0.74 0.73 0.73 3490 Cell 0.72 0.87 0.79 1360 Cellular_component 0.00 0.00 0.00 99 Developing_anatomical_structure 0.00 0.00 0.00 11 Gene_or_gene_product 0.67 0.25 0.37 174 Immaterial_anatomical_entity 0.52 0.76 0.62 432 Multi-tissue_structure 0.83 0.59 0.69 317 Organ 0.00 0.00 0.00 49 Organism 0.71 0.48 0.57 464 Organism_subdivision 0.70 0.72 0.71 678 Organism_substance 0.00 0.00 0.00 128 Pathological_formation 0.62 0.05 0.09 108 Simple_chemical 0.00 0.00 0.00 56 Tissue 0.80 0.85 0.82 1566 micro avg 0.73 0.71 0.72 9526 macro avg 0.49 0.42 0.43 9526 weighted avg 0.71 0.71 0.70 9526 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0