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BioNLP13CG_PubMedBERT_NER

judithrosell / BioNLP13CG_PubMedBERT_NER

Run on huggingface.co
huggingface.co
Total runs: 16
24-hour runs: 0
7-day runs: -8
30-day runs: -39
Model's Last Updated: December 14 2023
token-classification

Introduction of BioNLP13CG_PubMedBERT_NER

Model Details of BioNLP13CG_PubMedBERT_NER

BioNLP13CG_PubMedBERT_NER

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

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

Runs of judithrosell BioNLP13CG_PubMedBERT_NER on huggingface.co

16
Total runs
0
24-hour runs
-2
3-day runs
-8
7-day runs
-39
30-day runs

More Information About BioNLP13CG_PubMedBERT_NER huggingface.co Model

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BioNLP13CG_PubMedBERT_NER install

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judithrosell
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