metadata

license: mit
base_model: microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext
tags:
  - generated_from_trainer
model-index:
  - name: CRAFT_PubMedBERT_NER
    results: []

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

Training Loss	Epoch	Step	Validation Loss	Seqeval classification report
No log	1.0	347	0.1260	precision recall f1-score support

   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