medical_condition_classification

<!-- 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. -->

medical_condition_classification

This model is a fine-tuned version of distilbert-base-uncased on an Drugs.com dataset. It achieves the following results on the test data set:

• Loss: 0.8930
• Accuracy: 0.7951

Model description

The Goal of the model is to predict the medical condition based on the review of the drug. There're 751 classes.

Intended uses & limitations

More information needed

Training and evaluation data

The training, evaluation & testing data can be found under samsaara/medical_condition_classification of the 🤗 Datasets and the process itself can be found in the modeling.ipynb notebook.

By default, the dataset has train, test splits. train is then further divided into train, validation splits with 0.8, 0.2 ratio. Final results shown are on the test dataset.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 3e-05
• train_batch_size: 24
• eval_batch_size: 24
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 5
• mixed_precision_training: Native AMP

Training results

Framework versions

• Transformers 4.45.2
• Pytorch 2.4.1
• Datasets 3.0.1
• Tokenizers 0.20.1