/nlp702_text_detox

NLP702 Final Project

Primary LanguagePythonMIT LicenseMIT

Setup

Setup the environment

conda create -n multidetox python=3.10

Then install all required dependencies.

pip install -r requirements.txt

How to run the PEFT methods experiment

This project uses hydra to manage the experiment configuration. Therefore you need to adjust the adapter_experiment_config.yaml file. The important parts to adjust are loggers, dataset (to change which datasets should be used for training), checkpoint, do_train (training toggle), do_test (test set inference toggle), in_filepath, out_filepath, out_src_eval_filepath, out_pred_eval_filepath , out_tgt_eval_filepath, and checkpoint_file. Once you have set the configuration, the next step is to run

python adapter_experiment.py

Evaluation

To do evaluation on validation set (please just differentiate the prediction and o argument here)

python evaluate_pred.py --prediction ./dataset/bloomz_pred_dev.json  -l ./dataset/lang_dev.json -i ./dataset/source_dev.json -g ./multidetox_gt_dev.json -o ./eval_files/bloomz_dev.txt

mT5

We adapted the notebook for fine-tuning text2text transformer models using HuggingFace trainer from here. To fine-tune the model, run the following command:

!python mt5/train_mt5.py \
        --learning_rate 5e-5 \
        --max_target_length 128 --max_source_length 128 \
        --per_device_train_batch_size 8 --per_device_eval_batch_size 8 \
        --model_name_or_path "google/mt5-base" \
        --output_dir "mt5/mT5_FT" --overwrite_output_dir \
        --num_train_epochs 22 \
        --train_file "dataset/train_mt5.tsv" \
        --validation_file "dataset/dev_mt5.tsv" \
        --task "detoxify" --text_column "toxic" --summary_column "neutral" \
        --load_best_model_at_end --metric_for_best_model "chrf" --greater_is_better True --evaluation_strategy steps --logging_strategy steps --predict_with_generate \
        --do_train --do_eval > log.log 2>&1

To run inference on the test set, you can run the following command:

python mt5/mt5_inference.py

Zero- and few-shot

To get zero- or few-shot outputs, run the following:

python zero_few_shot.py --filename <path-to-your-file> --model_tag gpt-3.5-turbo --prompt_English True --shots True --api_key <your-key>

Specify the following arguments:

  • --filename: The TSV file to evaluate.
  • --model_tag: The model used to generate the outputs: tag from OpenAI or Hugging Face. Only gpt-3.5-turbo, bigscience/bloomz-7b1, bigscience/mt0-xxl are available.
  • prompt_English: Whether the prompt is in English or not.
  • --shots: Whether to include examples in the prompt.
  • --api_key: The OpenAI API key if applicable.
    The output will be saved to the same directory with suffix _detoxified.

Checkpoints

To get checkpoints for our experiments you can download them from here (for PEFT and mT5 approaches).