Code for our paper at EACL'23: UDAPTER-Efficient Domain Adaptation Using Adapters. Domadapter trains adapters for Domain Adaptation in NLP. The idea is to use principles of unsupervised domain adaptation and parameter efficient fine-tuning to make domain adaptation more efficient.

The weights are now available at https://huggingface.co/domadapter

• Python >= 3.8
• Poetry for dependency and environment management
• direnv (Optional) - For automatically exporting environment variables

We use environment variables to store certain paths

• Pretrained Transformer Models (PT_MODEL_CACHE_DIR)
• Datasets (DATASET_CACHE_DIR)
• Experiments (OUTPUT_DIR)
• Results (OUTPUT_DIR)

Change the following variables in the .envrc file.

• export DATASET_CACHE_DIR=""
• export PT_MODELS_CACHE_DIR=""
• export OUTPUT_DIR=""

Run source .envrc

• Run poetry install to install the dependencies
• Use poetry shell to create a virtual environment

Commit your poetry.lock file if you install any new library.

Note: We have tested this on a linux machine. If you are using Macbook M1 then you might encounter in to some errors installing scipy, sklearn etc.

• Run domadapter download mnli to download the mnli dataset
• Run domadapter download sa to download the amazon dataset.

Using Google Colab? !pip install pytorch-lightning==1.4.2 datasets transformers pandas click wandb numpy rich

You can run this on CPU and it will take a couple of minutes

Join Domain Task Adapter for MNLI

domadapter results mnli-joint-dt --source [source_domain] --target [target_domain]

For MNLI, The source and target domain can be one of Fiction, Slate, Government, Telephone, Travel

Other models and other datasets are coming soon.

See the scripts folder to train models. For example to train the Joint-DT-:electric_plug: on the MNLI dataset run

bash train_joint_da_ta_mnli.sh

.
├── commands (domadapter terminal commands)
├── datamodules (Pytorch Lightning DataModules to load the SA and MNLI Dataset)
├── divergences (Different Divergence measures)
├── models (All the models listed in the paper)
├── orchestration (Instantiates the model, dataset, trainer and runs the experiments)
├── scripts (Bash scripts to run experiments)
├── utils (Useful utilities)
└── console.py (Python `Rich` console to pretty print everything)

@misc{https://doi.org/10.48550/arxiv.2302.03194,
  doi = {10.48550/ARXIV.2302.03194},
  
  url = {https://arxiv.org/abs/2302.03194},
  
  author = {Malik, Bhavitvya and Kashyap, Abhinav Ramesh and Kan, Min-Yen and Poria, Soujanya},
  
  keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {UDApter -- Efficient Domain Adaptation Using Adapters},
  
  publisher = {arXiv},
  
  year = {2023},
  
  copyright = {Creative Commons Attribution 4.0 International
}