TwHIN-BERT: A Socially-Enriched Pre-trained Language Model for Multilingual Tweet Representations
This repo contains models, code and pointers to datasets from our paper: TwHIN-BERT: A Socially-Enriched Pre-trained Language Model for Multilingual Tweet Representations. [PDF] [HuggingFace Models]
Overview
TwHIN-BERT is a new multi-lingual Tweet language model that is trained on 7 billion Tweets from over 100 distinct languages. TwHIN-BERT differs from prior pre-trained language models as it is trained with not only text-based self-supervision (e.g., MLM), but also with a social objective based on the rich social engagements within a Twitter Heterogeneous Information Network (TwHIN).
TwHIN-BERT can be used as a drop-in replacement for BERT in a variety of NLP and recommendation tasks. It not only outperforms similar models semantic understanding tasks such text classification), but also **social recommendation **tasks such as predicting user to Tweet engagement.
1. Pretrained Models
We initially release two pretrained TwHIN-BERT models (base and large) that are compatible wit the HuggingFace BERT models.
Model | Size | Download Link (🤗 HuggingFace) |
---|---|---|
TwHIN-BERT-base | 280M parameters | Twitter/TwHIN-BERT-base |
TwHIN-BERT-large | 550M parameters | Twitter/TwHIN-BERT-large |
To use these models in 🤗 Transformers:
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained('Twitter/twhin-bert-base')
model = AutoModel.from_pretrained('Twitter/twhin-bert-base')
inputs = tokenizer("I'm using TwHIN-BERT! #TwHIN-BERT #NLP", return_tensors="pt")
outputs = model(**inputs)
Citation
If you use TwHIN-BERT or out datasets in your work, please cite, please cite the following:
@article{zhang2022twhin,
title={TwHIN-BERT: A Socially-Enriched Pre-trained Language Model for Multilingual Tweet Representations},
author={Zhang, Xinyang and Malkov, Yury and Florez, Omar and Park, Serim and McWilliams, Brian and Han, Jiawei and El-Kishky, Ahmed},
journal={arXiv preprint arXiv:2209.07562},
year={2022}
}