DAEDRA: A language model for predicting outcomes in passive pharmacovigilance reporting
Paper • 2402.10951 • Published • 2
daedra
This model is a fine-tuned version of dmis-lab/biobert-base-cased-v1.2 on the VAERS outcomes data set. The model and its functionality is described in this paper.
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: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Framework versions
- Transformers 4.37.2
- Pytorch 2.1.2+cu121
- Datasets 2.3.2
- Tokenizers 0.15.1
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Model tree for chrisvoncsefalvay/daedra
Base model
dmis-lab/biobert-base-cased-v1.2