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README.md

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 The data used to train the classifier comes from the NADI 2021 dataset for Arabic Dialect Identification [(Abdul-Mageed et al., 2021)](#cite-mageed-2021).  
 It is a corpus of tweets collected using Twitter's API and labeled thanks to the users' locations with the country and region.  
 
-I used the language model `https://huggingface.co/moussaKam/AraBART` to extract features from the input text by taking the output of its last hidden layer. I used these vector embeddings as the input for a Multinomial Logistic Regression to classify the input text into one of the 21 dialects (Countries).
+In the current version, I used the language model `https://huggingface.co/moussaKam/AraBART` to extract features from the input text by taking the output of its last hidden layer. I used these vector embeddings as the input for a Multinomial Logistic Regression to classify the input text into one of the 21 dialects (Countries).
 
 For more details, you can refer to the docs directory.
 
+## Releases
+### v0.0.1
+In the first release, I used the language model `https://huggingface.co/moussaKam/AraBART` to extract features from the input text by taking the output of its last hidden layer. I used these vector embeddings as the input for a Multinomial Logistic Regression to classify the input text into one of the 21 dialects (Countries).
+### v0.0.2
+
 ## References:
 - <a name="cite-mageed-2021"></a>
 [Abdul-Mageed et al., 2021](https://arxiv.org/abs/2103.08466)