judithrosell / CRAFT_PubMedBERT_NER

CRAFT_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: 0.1043

• Seqeval classification report: precision recall f1-score support

   CHEBI       0.71      0.73      0.72       616
      CL       0.85      0.89      0.87      1740
     GGP       0.84      0.76      0.80       611
      GO       0.89      0.90      0.90      3810
      SO       0.81      0.83      0.82      8854
   Taxon       0.58      0.60      0.59       284
  

  micro avg 0.82 0.84 0.83 15915 macro avg 0.78 0.79 0.78 15915

weighted avg 0.82 0.84 0.83 15915

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

   CHEBI       0.66      0.61      0.63       616
      CL       0.81      0.86      0.83      1740
     GGP       0.74      0.54      0.63       611
      GO       0.86      0.89      0.87      3810
      SO       0.73      0.78      0.76      8854
   Taxon       0.47      0.57      0.52       284

micro avg 0.76 0.80 0.78 15915 macro avg 0.71 0.71 0.71 15915 weighted avg 0.76 0.80 0.78 15915 | | 0.182 | 2.0 | 695 | 0.1089 | precision recall f1-score support

   CHEBI       0.69      0.74      0.71       616
      CL       0.84      0.88      0.86      1740
     GGP       0.83      0.74      0.78       611
      GO       0.88      0.90      0.89      3810
      SO       0.79      0.82      0.81      8854
   Taxon       0.57      0.60      0.58       284

micro avg 0.81 0.84 0.82 15915 macro avg 0.77 0.78 0.77 15915 weighted avg 0.81 0.84 0.82 15915 | | 0.0443 | 3.0 | 1041 | 0.1043 | precision recall f1-score support

   CHEBI       0.71      0.73      0.72       616
      CL       0.85      0.89      0.87      1740
     GGP       0.84      0.76      0.80       611
      GO       0.89      0.90      0.90      3810
      SO       0.81      0.83      0.82      8854
   Taxon       0.58      0.60      0.59       284

micro avg 0.82 0.84 0.83 15915 macro avg 0.78 0.79 0.78 15915 weighted avg 0.82 0.84 0.83 15915 |

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu118
• Datasets 2.15.0
• Tokenizers 0.15.0