This model is a fine-tuned version of 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

                  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

judithrosell/BioNLP13CG_PubMedBERT_NER

BioNLP13CG_PubMedBERT_NER

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

judithrosell/
BioNLP13CG_PubMedBERT_NER