metadata

license: mit
tags:
  - generated_from_trainer
datasets:
  - marker-associations-binary-base
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: marker-associations-binary-base
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: marker-associations-binary-base
          type: marker-associations-binary-base
        metrics:
          - name: Precision
            type: precision
            value: 0.7981651376146789
          - name: Recall
            type: recall
            value: 0.9560439560439561
          - name: F1
            type: f1
            value: 0.87
          - name: Accuracy
            type: accuracy
            value: 0.8884120171673819

marker-associations-binary-base

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

Gene Results

Precision = 0.808
Recall = 0.940
F1 = 0.869
Accuracy = 0.862
AUC = 0.944

Chemical Results

Precision = 0.774
Recall = 1.0
F1 = 0.873
Accuracy = 0.926
AUC = 0.964

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	88	0.3266	0.8191	0.8462	0.8324	0.8670	0.9313
No log	2.0	176	0.3335	0.7870	0.9341	0.8543	0.8755	0.9465
No log	3.0	264	0.4243	0.7982	0.9560	0.87	0.8884	0.9516
No log	4.0	352	0.5388	0.825	0.7253	0.7719	0.8326	0.9384
No log	5.0	440	0.7101	0.8537	0.7692	0.8092	0.8584	0.9416
0.1824	6.0	528	0.6175	0.8242	0.8242	0.8242	0.8627	0.9478

Framework versions

Transformers 4.11.3
Pytorch 1.9.0+cu111
Tokenizers 0.10.3