Update README.md

README.md

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 **RDRPOSTagger** is a robust, easy-to-use and language-independent toolkit for POS and morphological tagging. It employs an error-driven approach to automatically construct tagging rules in the form of a binary tree. The main properties of RDRPOSTagger are as follows:
 
-- RDRPOSTagger obtains very fast performance in both learning and tagging process.
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-- RDRPOSTagger achieves a very competitive accuracy in comparison to the state-of-the-art results. See experimental results including performance speed and tagging accuracy for 13 languages in our *AI Communications* article.
+- RDRPOSTagger obtains very fast tagging speed and achieves a competitive accuracy in comparison to the state-of-the-art results. See experimental results including performance speed and tagging accuracy for 13 languages in our *AI Communications* article.
 
 - RDRPOSTagger supports pre-trained models for fine-grained POS and morphological tagging  for Bulgarian, Czech, Dutch, English, French, German, Hindi, Italian, Portuguese, Spanish, Swedish, Thai and Vietnamese.  
 
-- RDRPOSTagger also supports pre-trained universal POS tagging models for 40+ languages. These models are learned using training data from the Universal Dependencies (UD) v2.0. Universal POS tagging accuracies on UD v2.0 test sets are [HERE](https://github.com/datquocnguyen/RDRPOSTagger/blob/master/Models/UniPOS/Readme.md).
+- RDRPOSTagger also supports pre-trained universal POS tagging models for 40+ languages. These models are learned using training data from the Universal Dependencies (UD) v2.0. See the universal POS tagging accuracies on UD v2.0 test sets at [HERE](https://github.com/datquocnguyen/RDRPOSTagger/blob/master/Models/UniPOS/Readme.md).
 
 The general architecture and experimental results of RDRPOSTagger can be found in our following papers:
 
@@ -18,6 +16,8 @@ The general architecture and experimental results of RDRPOSTagger can be found i
 
 **Please cite** either the EACL or the AICom paper whenever RDRPOSTagger is used to produce published results or incorporated into other software.
 
+**Current release v1.2.4 is available to download (11MB .zip file including all pre-trained models) at:** [https://github.com/datquocnguyen/RDRPOSTagger/archive/master.zip](https://github.com/datquocnguyen/RDRPOSTagger/archive/master.zip)
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 **Find more information about RDRPOSTagger at its website:** [http://rdrpostagger.sourceforge.net/](http://rdrpostagger.sourceforge.net/)
 
-In addition, you might find my new toolkit [jPTDP](https://github.com/datquocnguyen/jPTDP) interesting: [jPTDP - A Novel Neural Network Model for Joint POS Tagging and Dependency Parsing](https://github.com/datquocnguyen/jPTDP). jPTDP also supports pre-trained models for 40+ languages from UD v2.0. Universal POS tagging accuracies of jPTDP on UD v2.0 test sets  are [HERE](https://github.com/datquocnguyen/jPTDP/blob/master/UDv2.0_results.txt).
+In addition, you might also find my new toolkit [jPTDP](https://github.com/datquocnguyen/jPTDP) interesting: [jPTDP - A Novel Neural Network Model for Joint POS Tagging and Dependency Parsing](https://github.com/datquocnguyen/jPTDP). jPTDP also provides pre-trained models for 40+ languages from UD v2.0.