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twhin-bert-base

Twitter

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]](https://arxiv.org/pdf/2209.07562.pdf) [[HuggingFace Models]](https://huggingface.co/Twitter) 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.

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