Add model card v1.

README.md

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-license: apache-2.0
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+# 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-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)
+```
+
+
+
+<!-- ## 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, 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}
+}
+```