--- base_model: dmis-lab/biobert-v1.1 tags: - generated_from_trainer model-index: - name: BioNLP13CG_bioBERT_NER results: [] --- # BioNLP13CG_bioBERT_NER This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1928 - Seqeval classification report: precision recall f1-score support Amino_acid 0.89 0.88 0.88 576 Anatomical_system 0.96 0.82 0.89 317 Cancer 0.92 0.91 0.91 1649 Cell 0.00 0.00 0.00 25 Cellular_component 0.00 0.00 0.00 12 Developing_anatomical_structure 0.75 0.85 0.80 438 Gene_or_gene_product 0.87 0.18 0.29 74 Immaterial_anatomical_entity 0.84 0.84 0.84 4142 Multi-tissue_structure 0.85 0.84 0.84 451 Organ 0.51 0.23 0.31 80 Organism 0.52 0.66 0.58 182 Organism_subdivision 0.81 0.80 0.81 314 Organism_substance 0.73 0.66 0.69 96 Pathological_formation 0.75 0.68 0.71 262 Simple_chemical 0.55 0.44 0.49 112 Tissue 0.82 0.91 0.87 300 micro avg 0.84 0.82 0.83 9030 macro avg 0.67 0.60 0.62 9030 weighted avg 0.84 0.82 0.83 9030 ## 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 | 0.2929 | precision recall f1-score support Amino_acid 0.68 0.81 0.73 576 Anatomical_system 0.93 0.74 0.82 317 Cancer 0.89 0.89 0.89 1649 Cell 0.00 0.00 0.00 25 Cellular_component 0.00 0.00 0.00 12 Developing_anatomical_structure 0.56 0.79 0.65 438 Gene_or_gene_product 0.00 0.00 0.00 74 Immaterial_anatomical_entity 0.79 0.76 0.77 4142 Multi-tissue_structure 0.84 0.75 0.79 451 Organ 0.00 0.00 0.00 80 Organism 0.62 0.08 0.15 182 Organism_subdivision 0.64 0.78 0.70 314 Organism_substance 0.00 0.00 0.00 96 Pathological_formation 0.63 0.44 0.52 262 Simple_chemical 0.79 0.13 0.23 112 Tissue 0.82 0.45 0.58 300 micro avg 0.78 0.72 0.75 9030 macro avg 0.51 0.41 0.43 9030 weighted avg 0.76 0.72 0.73 9030 | | No log | 2.0 | 191 | 0.2053 | precision recall f1-score support Amino_acid 0.87 0.87 0.87 576 Anatomical_system 0.98 0.80 0.88 317 Cancer 0.89 0.92 0.91 1649 Cell 0.00 0.00 0.00 25 Cellular_component 0.00 0.00 0.00 12 Developing_anatomical_structure 0.74 0.84 0.79 438 Gene_or_gene_product 1.00 0.05 0.10 74 Immaterial_anatomical_entity 0.83 0.83 0.83 4142 Multi-tissue_structure 0.85 0.82 0.83 451 Organ 0.48 0.15 0.23 80 Organism 0.49 0.66 0.56 182 Organism_subdivision 0.79 0.80 0.80 314 Organism_substance 0.75 0.58 0.65 96 Pathological_formation 0.76 0.66 0.71 262 Simple_chemical 0.48 0.42 0.45 112 Tissue 0.80 0.90 0.85 300 micro avg 0.82 0.82 0.82 9030 macro avg 0.67 0.58 0.59 9030 weighted avg 0.82 0.82 0.81 9030 | | No log | 2.98 | 285 | 0.1928 | precision recall f1-score support Amino_acid 0.89 0.88 0.88 576 Anatomical_system 0.96 0.82 0.89 317 Cancer 0.92 0.91 0.91 1649 Cell 0.00 0.00 0.00 25 Cellular_component 0.00 0.00 0.00 12 Developing_anatomical_structure 0.75 0.85 0.80 438 Gene_or_gene_product 0.87 0.18 0.29 74 Immaterial_anatomical_entity 0.84 0.84 0.84 4142 Multi-tissue_structure 0.85 0.84 0.84 451 Organ 0.51 0.23 0.31 80 Organism 0.52 0.66 0.58 182 Organism_subdivision 0.81 0.80 0.81 314 Organism_substance 0.73 0.66 0.69 96 Pathological_formation 0.75 0.68 0.71 262 Simple_chemical 0.55 0.44 0.49 112 Tissue 0.82 0.91 0.87 300 micro avg 0.84 0.82 0.83 9030 macro avg 0.67 0.60 0.62 9030 weighted avg 0.84 0.82 0.83 9030 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0