Update README.md

README.md

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 <img align="center" src="https://i.ibb.co/0yz81K9/sec-bert-logo.png" alt="SEC-BERT" width="400"/>
 
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 SEC-BERT is a family of BERT models for the financial domain, intended to assist financial NLP research and FinTech applications.
 SEC-BERT consists of the following models:
 * SEC-BERT-BASE (this model): Same architecture as BERT-BASE trained on financial documents.
 * [SEC-BERT-NUM](https://huggingface.co/nlpaueb/sec-bert-num): Same as SEC-BERT-BASE but we replace every number token with a [NUM] pseudo-token handling all numeric expressions in a uniform manner, disallowing their fragmentation
 * [SEC-BERT-SHAPE](https://huggingface.co/nlpaueb/sec-bert-shape): Same as SEC-BERT-BASE but we replace numbers with pseudo-tokens that represent the number’s shape, so numeric expressions (of known shapes) are no longer fragmented, e.g., '53.2' becomes '[XX.X]' and '40,200.5' becomes '[XX,XXX.X]'.
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 ## Pre-training corpus
 
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 ## Pre-training details
 
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 * We created a new vocabulary of 30k subwords by training a [BertWordPieceTokenizer](https://github.com/huggingface/tokenizers) from scratch on the pre-training corpus.
 * We trained BERT using the official code provided in [Google BERT's GitHub repository](https://github.com/google-research/bert)</a>.
 * We then used [Hugging Face](https://huggingface.co)'s [Transformers](https://github.com/huggingface/transformers) conversion script to convert the TF checkpoint in the desired format in order to be able to load the model in two lines of code for both PyTorch and TF2 users.
 * We release a model similar to the English BERT-BASE model (12-layer, 768-hidden, 12-heads, 110M parameters).
 * We chose to follow the same training set-up: 1 million training steps with batches of 256 sequences of length 512 with an initial learning rate 1e-4.
 * We were able to use a single Google Cloud TPU v3-8 provided for free from [TensorFlow Research Cloud (TRC)]((https://sites.research.google/trc), while also utilizing [GCP research credits](https://edu.google.com/programs/credits/research). Huge thanks to both Google programs for supporting us!
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 ## Load Pretrained Model
 
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 ## Publication
 
-The model has been officially released with the following article:<br> 
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+If you use this model cite the following article:<br> 
 **["FiNER: Financial Numeric Entity Recognition for XBRL Tagging"](https://arxiv.org/abs/2203.06482)**<br>
 Lefteris Loukas, Manos Fergadiotis, Ilias Chalkidis, Eirini Spyropoulou, Prodromos Malakasiotis, Ion Androutsopoulos and George Paliouras<br>
 In the Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL 2022) (Long Papers), Dublin, Republic of Ireland, May 22 - 27, 2022
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 ```
 @inproceedings{loukas-etal-2022-finer,
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 ## About Us
 
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 [AUEB's Natural Language Processing Group](http://nlp.cs.aueb.gr) develops algorithms, models, and systems that allow computers to process and generate natural language texts.
 
 The group's current research interests include:
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 machine learning in natural language processing, especially deep learning.
 
 The group is part of the Information Processing Laboratory of the Department of Informatics of the Athens University of Economics and Business.
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 [Manos Fergadiotis](https://manosfer.github.io) on behalf of [AUEB's Natural Language Processing Group](http://nlp.cs.aueb.gr)