Adapter xlm-roberta-base_formality_classify_gyafc_pfeiffer for xlm-roberta-base

Note: This adapter was not trained by the AdapterHub team, but by these author(s): Kalpesh Krishna. See author details below.

This adapter has been trained on the English formality classification GYAFC dataset and tested with other language adapters (like hindi) for zero-shot transfer. Make sure to remove tokenization, lowercase and remove trailing punctuation for best results.

This adapter was created for usage with the Adapters library.

Usage

First, install adapters:

pip install -U adapters

Now, the adapter can be loaded and activated like this:

from adapters import AutoAdapterModel

model = AutoAdapterModel.from_pretrained("xlm-roberta-base")
adapter_name = model.load_adapter("AdapterHub/xlm-roberta-base_formality_classify_gyafc_pfeiffer")
model.set_active_adapters(adapter_name)

Architecture & Training

• Adapter architecture: pfeiffer
• Prediction head: classification
• Dataset: Grammarly's Yahoo Answers Formality Corpus (GYAFC)

Author Information

• Author name(s): Kalpesh Krishna
• Author email: kalpesh@cs.umass.edu
• Author links: Website, GitHub, Twitter

Citation

@inproceedings{krishna-etal-2020-reformulating,
    title = "Reformulating Unsupervised Style Transfer as Paraphrase Generation",
    author = "Krishna, Kalpesh  and
      Wieting, John  and
      Iyyer, Mohit",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.emnlp-main.55",
    doi = "10.18653/v1/2020.emnlp-main.55",
    pages = "737--762",
}

This adapter has been auto-imported from https://github.com/Adapter-Hub/Hub/blob/master/adapters/martiansideofthemoon/xlm-roberta-base_formality_classify_gyafc_pfeiffer.yaml.