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---
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
|