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seq2seq

README.md

We use a modified fork of huggingface transformers for our experiments.

Setup

$ git clone https://github.com/csebuetnlp/CrossSum
$ cd crossum/seq2seq
$ conda create python==3.7.9 pytorch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2 cudatoolkit=10.2 -c pytorch -p ./env
$ conda activate ./env # or source activate ./env (for older versions of anaconda)
$ bash setup.sh
  • Note: For newer NVIDIA GPUS such as A100 or 3090 use cudatoolkit=11.1.

Downloading data

This script downloads the metadata-stripped version of the dataset required for training. The extracted files will be saved inside the dataset/ directory.

$ bash download_data.sh

Training

To see the list of all available options related to training, do python pipeline.py -h

Running ablation experiments

  • See available training settings: bash trainer.sh -h. You can try out different training hyperparameters by modifying the default values in this script.

Some sample commands for training on a 8 GPU node are given below. More can be found in training_runner.sh.

bash trainer.sh --ngpus 8 --training_type m2m --sampling multistage # trains the many-to-many model with multistage sampling
bash trainer.sh --ngpus 8 --training_type m2o --pivot_lang arabic # trains the many-to-one model using arabic as the target language
bash trainer.sh --ngpus 8 --training_type o2m --pivot_lang english # trains the one-to-many model using english as the source language
  • Available pivot language names: oromo, french, amharic, arabic, azerbaijani, bengali, burmese, chinese_simplified, chinese_traditional, welsh, english, kirundi, gujarati, hausa, hindi, igbo, indonesian, japanese, korean, kyrgyz, marathi, spanish, scottish_gaelic, nepali, pashto, persian, pidgin, portuguese, punjabi, russian, serbian_cyrillic, serbian_latin, sinhala, somali, swahili, tamil, telugu, thai, tigrinya, turkish, ukrainian, urdu, uzbek, vietnamese, yoruba

Evaluation

  • See available evaluation options: python evaluator.py -h.

For example, to compute ROUGE and LaSE scores on all language pairs of the CrossSum test set using a trained cross-lingual model, run the following:

python evaluator.py \
    --dataset_dir <path/to/dataset/directory> \
    --output_dir <path/to/output/directory> \
    --evaluation_type xlingual \
    --data_type test \
    --xlingual_summarization_model_name_or_path <path/to/model/directory>

More detailed examples can be found in evaluate.sh and evaluation_runner.sh