metadata

license: mit
tags:
  - generated_from_trainer
datasets:
  - pritamdeka/cord-19-fulltext
metrics:
  - accuracy
model-index:
  - name: pubmedbert-fulltext-cord19
    results:
      - task:
          name: Masked Language Modeling
          type: fill-mask
        dataset:
          name: pritamdeka/cord-19-fulltext
          type: pritamdeka/cord-19-fulltext
          args: fulltext
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.7175316733550737

pubmedbert-fulltext-cord19

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

Loss: 1.2667
Accuracy: 0.7175

Model description

The model has been trained using a maximum train sample size of 300K and evaluation size of 25K due to GPU limitations

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=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 10000
num_epochs: 3.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.7985	0.27	5000	1.2710	0.7176
1.7542	0.53	10000	1.3359	0.7070
1.7462	0.8	15000	1.3489	0.7034
1.8371	1.07	20000	1.4361	0.6891
1.7102	1.33	25000	1.3502	0.7039
1.6596	1.6	30000	1.3341	0.7065
1.6265	1.87	35000	1.3228	0.7087
1.605	2.13	40000	1.3079	0.7099
1.5731	2.4	45000	1.2986	0.7121
1.5602	2.67	50000	1.2929	0.7136
1.5447	2.93	55000	1.2875	0.7143

Framework versions

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