README.md

metadata

license: mit
tags:
  - generated_from_trainer
datasets:
  - pritamdeka/cord-19-abstract
metrics:
  - accuracy
base_model: microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext
model-index:
  - name: pubmedbert-abstract-cord19
    results:
      - task:
          type: fill-mask
          name: Masked Language Modeling
        dataset:
          name: pritamdeka/cord-19-abstract
          type: pritamdeka/cord-19-abstract
          args: fulltext
        metrics:
          - type: accuracy
            value: 0.7246798699728464
            name: Accuracy

PubMedBert-abstract-cord19-v2

This model is a fine-tuned version of microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext on the pritamdeka/cord-19-abstract dataset. It achieves the following results on the evaluation set:

• Loss: 1.2371
• Accuracy: 0.7247

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: 42
• optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 10000
• num_epochs: 4.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.27	0.53	5000	1.2425	0.7236
1.2634	1.06	10000	1.3123	0.7141
1.3041	1.59	15000	1.3583	0.7072
1.3829	2.12	20000	1.3590	0.7121
1.3069	2.65	25000	1.3506	0.7154
1.2921	3.18	30000	1.3448	0.7160
1.2731	3.7	35000	1.3375	0.7178

Framework versions

• Transformers 4.17.0.dev0
• Pytorch 1.10.0+cu111
• Datasets 1.18.3
• Tokenizers 0.11.0