--- license: mit base_model: microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext tags: - generated_from_trainer model-index: - name: BioNLP13CG_PubMedBERT_NER results: [] --- # BioNLP13CG_PubMedBERT_NER This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2066 - Seqeval classification report: precision recall f1-score support Amino_acid 0.78 0.81 0.79 301 Anatomical_system 0.00 0.00 0.00 3 Cancer 0.00 0.00 0.00 37 Cell 0.79 0.85 0.82 446 Cellular_component 0.00 0.00 0.00 19 Developing_anatomical_structure 0.55 0.78 0.65 399 Gene_or_gene_product 0.68 0.41 0.51 128 Immaterial_anatomical_entity 0.00 0.00 0.00 45 Multi-tissue_structure 0.25 0.02 0.04 98 Organ 0.00 0.00 0.00 19 Organism 0.90 0.93 0.92 1108 Organism_subdivision 0.71 0.12 0.21 120 Organism_substance 0.62 0.59 0.60 128 Pathological_formation 0.00 0.00 0.00 41 Simple_chemical 0.87 0.86 0.86 4397 Tissue 0.90 0.93 0.91 1790 micro avg 0.84 0.83 0.84 9079 macro avg 0.44 0.39 0.39 9079 weighted avg 0.83 0.83 0.82 9079 ## 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 | 0.3390 | precision recall f1-score support Amino_acid 0.81 0.10 0.18 301 Anatomical_system 0.00 0.00 0.00 3 Cancer 0.00 0.00 0.00 37 Cell 0.82 0.76 0.79 446 Cellular_component 0.00 0.00 0.00 19 Developing_anatomical_structure 0.90 0.07 0.13 399 Gene_or_gene_product 0.00 0.00 0.00 128 Immaterial_anatomical_entity 0.00 0.00 0.00 45 Multi-tissue_structure 0.00 0.00 0.00 98 Organ 0.00 0.00 0.00 19 Organism 0.64 0.86 0.73 1108 Organism_subdivision 0.00 0.00 0.00 120 Organism_substance 0.00 0.00 0.00 128 Pathological_formation 0.00 0.00 0.00 41 Simple_chemical 0.83 0.79 0.81 4397 Tissue 0.74 0.91 0.82 1790 micro avg 0.77 0.71 0.74 9079 macro avg 0.30 0.22 0.22 9079 weighted avg 0.73 0.71 0.69 9079 | | No log | 2.0 | 191 | 0.2209 | precision recall f1-score support Amino_acid 0.76 0.75 0.76 301 Anatomical_system 0.00 0.00 0.00 3 Cancer 0.00 0.00 0.00 37 Cell 0.78 0.87 0.82 446 Cellular_component 0.00 0.00 0.00 19 Developing_anatomical_structure 0.52 0.75 0.61 399 Gene_or_gene_product 0.65 0.24 0.35 128 Immaterial_anatomical_entity 0.00 0.00 0.00 45 Multi-tissue_structure 0.00 0.00 0.00 98 Organ 0.00 0.00 0.00 19 Organism 0.89 0.92 0.91 1108 Organism_subdivision 0.50 0.05 0.09 120 Organism_substance 0.61 0.52 0.56 128 Pathological_formation 0.00 0.00 0.00 41 Simple_chemical 0.86 0.86 0.86 4397 Tissue 0.87 0.93 0.90 1790 micro avg 0.83 0.82 0.83 9079 macro avg 0.40 0.37 0.37 9079 weighted avg 0.81 0.82 0.81 9079 | | No log | 2.98 | 285 | 0.2066 | precision recall f1-score support Amino_acid 0.78 0.81 0.79 301 Anatomical_system 0.00 0.00 0.00 3 Cancer 0.00 0.00 0.00 37 Cell 0.79 0.85 0.82 446 Cellular_component 0.00 0.00 0.00 19 Developing_anatomical_structure 0.55 0.78 0.65 399 Gene_or_gene_product 0.68 0.41 0.51 128 Immaterial_anatomical_entity 0.00 0.00 0.00 45 Multi-tissue_structure 0.25 0.02 0.04 98 Organ 0.00 0.00 0.00 19 Organism 0.90 0.93 0.92 1108 Organism_subdivision 0.71 0.12 0.21 120 Organism_substance 0.62 0.59 0.60 128 Pathological_formation 0.00 0.00 0.00 41 Simple_chemical 0.87 0.86 0.86 4397 Tissue 0.90 0.93 0.91 1790 micro avg 0.84 0.83 0.84 9079 macro avg 0.44 0.39 0.39 9079 weighted avg 0.83 0.83 0.82 9079 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0