metadata

license: mit
tags:
  - generated_from_trainer
datasets:
  - marker-associations-snp-binary-base
metrics:
  - precision
  - recall
  - f1
  - accuracy
base_model: microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext
model-index:
  - name: marker-associations-snp-binary-base
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: marker-associations-snp-binary-base
          type: marker-associations-snp-binary-base
        metrics:
          - type: precision
            value: 0.9384057971014492
            name: Precision
          - type: recall
            value: 0.9055944055944056
            name: Recall
          - type: f1
            value: 0.9217081850533808
            name: F1
          - type: accuracy
            value: 0.9107505070993914
            name: Accuracy

marker-associations-snp-binary-base

This model is a fine-tuned version of microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext on the marker-associations-snp-binary-base dataset. It achieves the following results on the evaluation set:

Loss: 0.4027
Precision: 0.9384
Recall: 0.9056
F1: 0.9217
Accuracy: 0.9108
Auc: 0.9578

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: 5e-05
train_batch_size: 16
eval_batch_size: 16
seed: 1
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy	Auc
No log	1.0	153	0.2776	0.9	0.9441	0.9215	0.9067	0.9613
No log	2.0	306	0.4380	0.9126	0.9126	0.9126	0.8986	0.9510
No log	3.0	459	0.4027	0.9384	0.9056	0.9217	0.9108	0.9578
0.2215	4.0	612	0.3547	0.9449	0.8986	0.9211	0.9108	0.9642
0.2215	5.0	765	0.4465	0.9107	0.9266	0.9185	0.9047	0.9636
0.2215	6.0	918	0.5770	0.8970	0.9441	0.9199	0.9047	0.9666

Framework versions

Transformers 4.11.3
Pytorch 1.9.0+cu111
Tokenizers 0.10.3