README.md

metadata

tags:
  - generated_from_trainer
datasets:
  - pv_dataset
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: PV-Bio_clinicalBERT-superset
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: pv_dataset
          type: pv_dataset
          config: PVDatasetCorpus
          split: train
          args: PVDatasetCorpus
        metrics:
          - name: Precision
            type: precision
            value: 0.7055946686730801
          - name: Recall
            type: recall
            value: 0.7473672226333467
          - name: F1
            type: f1
            value: 0.7258804666334938
          - name: Accuracy
            type: accuracy
            value: 0.9656573815513143

PV-Bio_clinicalBERT-superset

This model is a fine-tuned version of giacomomiolo/electramed_base_scivocab_1M on the pv_dataset dataset. It achieves the following results on the evaluation set:

• Loss: 0.2082
• Precision: 0.7056
• Recall: 0.7474
• F1: 0.7259
• Accuracy: 0.9657

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
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.063	1.0	1813	0.1061	0.6453	0.7306	0.6853	0.9623
0.0086	2.0	3626	0.1068	0.6620	0.7516	0.7040	0.9647
0.0089	3.0	5439	0.1265	0.7026	0.7300	0.7160	0.9657
0.004	4.0	7252	0.1369	0.6820	0.7601	0.7189	0.9638
0.0004	5.0	9065	0.1573	0.6937	0.7602	0.7254	0.9656
0.0184	6.0	10878	0.1707	0.7078	0.7475	0.7271	0.9662
0.0009	7.0	12691	0.1787	0.7116	0.7398	0.7254	0.9662
0.0006	8.0	14504	0.1874	0.6979	0.7576	0.7265	0.9655
0.0008	9.0	16317	0.1970	0.7083	0.7475	0.7273	0.9660
0.0003	10.0	18130	0.2082	0.7056	0.7474	0.7259	0.9657

Framework versions

• Transformers 4.21.0
• Pytorch 1.12.0+cu113
• Datasets 2.4.0
• Tokenizers 0.12.1