| --- |
| 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 |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # PubMedBert-abstract-cord19-v2 |
|
|
| This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the [pritamdeka/cord-19-abstract](https://huggingface.co/datasets/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 |
| |
