uci_drug

Drug Review Sentiment Classification

Jupyter notebook example

See the example notebook for a step-by-step walkthrough of how to use CNLPT to train a model for sentiment classification of drug reviews.

CLI example

If you prefer, you can instead use the CLI to train the model:

Download and preprocess the data

Use the prepare_data.py script to download the data and convert it to CNLPT's data format:

uv run prepare_data.py

[!TIP] About the dataset: This script downloads the Drug Reviews (Druglib.com) dataset. Please be aware of the terms of use:

Important Notes:

When using this dataset, you agree that you

1. only use the data for research purposes
2. don't use the data for any commerical purposes
3. don't distribute the data to anyone else
4. cite UCI data lab and the source

Here is the dataset's BibTeX citation:

@misc{drug_reviews_(druglib.com)_461,
  author       = {Kallumadi, Surya and Grer, Felix},
  title        = {{Drug Reviews (Druglib.com)}},
  year         = {2018},
  howpublished = {UCI Machine Learning Repository},
  note         = {{DOI}: https://doi.org/10.24432/C55G6J}
}

Train a model

The following example fine-tunes the RoBERTa base model with an added projection layer for classification:

uv run cnlpt train \
 --model_type proj \
 --encoder roberta-base \
 --data_dir ./dataset \
 --task sentiment \
 --output_dir ./train_output \
 --overwrite_output_dir \
 --do_train --do_eval --do_predict \
 --evals_per_epoch 2 \
 --learning_rate 1e-5 \
 --metric_for_best_model 'sentiment.macro_f1' \
 --load_best_model_at_end \
 --save_strategy best