metadata
license: cc-by-nc-3.0
PragS2: Pragmatic Masked Language Modeling with Emoji_any dataset followed by Hashtag-Based Surrogate Fine-Tuning
You can load these model and use for downstream fine-tuning. For example:
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained('UBC-NLP/prags1', use_fast = True)
model = AutoModelForSequenceClassification.from_pretrained('UBC-NLP/prags1',num_labels=lable_size)
More details are in our paper:
@inproceedings{zhang-abdul-mageed-2022-improving,
title = "Improving Social Meaning Detection with Pragmatic Masking and Surrogate Fine-Tuning",
author = "Zhang, Chiyu and
Abdul-Mageed, Muhammad",
booktitle = "Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment {\&} Social Media Analysis",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.wassa-1.14",
pages = "141--156",
}