Hugging Face's logo

jambo
/
marker-associations-snp-binary-base

Text Classification Transformers PyTorch
bert generated_from_trainer Eval Results Inference Endpoints
Model card Files Community
2
marker-associations-snp-binary-base / README.md
jambo's picture
jambo
update model card README.md
16ef812
preview code
|
raw history blame contribute delete
No virus
2.69 kB
metadata
license: mit
tags:
  - generated_from_trainer
datasets:
  - marker-associations-snp-binary-base
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: marker-associations-snp-binary-base
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: marker-associations-snp-binary-base
          type: marker-associations-snp-binary-base
        metrics:
          - name: Precision
            type: precision
            value: 0.9384057971014492
          - name: Recall
            type: recall
            value: 0.9055944055944056
          - name: F1
            type: f1
            value: 0.9217081850533808
          - name: Accuracy
            type: accuracy
            value: 0.9107505070993914

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