transformers pytorch safetensors bert fill-mask Twitter Multilingual en ja pt es ko ar tr th fr id ru de fa it zh pl hi ur nl el ms ca sr sv uk he fi cs ta ne vi hu eo bn mr ml hr no sw sl te az da ro gl gu ps mk kn bg lv eu pa et mn sq si sd la is jv lt ku am bs hy or sk uz cy my su br as af be fy kk ga lo ka km sa mg so ug ky gd yi arxiv:2209.07562 license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
language:
- en
- ja
- pt
- es
- ko
- ar
- tr
- th
- fr
- id
- ru
- de
- fa
- it
- zh
- pl
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- nl
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- sr
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- uk
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- false
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- am
- bs
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- or
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- uz
- cy
- my
- su
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- be
- fy
- kk
- ga
- lo
- ka
- km
- sa
- mg
- so
- ug
- ky
- gd
- yi
license: apache-2.0
tags:
- Twitter
- Multilingual
mask_token: <mask>
---

# TwHIN-BERT: A Socially-Enriched Pre-trained Language Model for Multilingual Tweet Representations
[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-green.svg?style=flat-square)](http://makeapullrequest.com)
[![arXiv](https://img.shields.io/badge/arXiv-2203.15827-b31b1b.svg)](https://arxiv.org/abs/2209.07562)


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

### 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](https://github.com/huggingface/transformers).


| Model | Size | Download Link (🤗 HuggingFace) |
| ------------- | ------------- | --------- |
| TwHIN-BERT-base   | 280M parameters | [Twitter/TwHIN-BERT-base](https://huggingface.co/Twitter/twhin-bert-base) |
| TwHIN-BERT-large  | 550M parameters | [Twitter/TwHIN-BERT-large](https://huggingface.co/Twitter/twhin-bert-large) |


To use these models in 🤗 Transformers:
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained('Twitter/twhin-bert-large')
model = AutoModel.from_pretrained('Twitter/twhin-bert-large')
inputs = tokenizer("I'm using TwHIN-BERT! #TwHIN-BERT #NLP", return_tensors="pt")
outputs = model(**inputs)
```



<!-- ## 2. Set up environment and data
### Environment
TBD


## 3. Fine-tune TwHIN-BERT

TBD -->


## Citation
If you use TwHIN-BERT or out datasets in your work, please cite the following:
```bib
@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}
}
```