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---
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
---
<!-- 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-fulltext-cord19
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-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
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