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README.md

@@ -63,7 +63,7 @@ will produce model files `trialmodel` and `trialmodel.params` in folder `SOURCE_
 
 ### Utilize a pre-trained model
 
-Pre-trained joint models for *universal* POS tagging and dependency parsing to 40+ languages can be found at   [__HERE__](https://drive.google.com/drive/folders/0B5eBgc8jrKtpTXhfRmpKbUEtdlE?usp=sharing). The pre-trained models are learned with **DEFAULT** hyper-parameters, using dependency treebanks from [the Universal Dependencies project](http://universaldependencies.org/) [v2.0](http://hdl.handle.net/11234/1-1983). It is likely that using pre-trained word embeddings for initialization and performing a grid search of hyper-parameters (e.g. varying the initial learning rate in {0.0002, 0.0005, 0.001, 0.002, 0.005}) would help return better results.
+Pre-trained joint models for *universal* POS tagging and dependency parsing to 40+ languages can be found at   [__HERE__](https://drive.google.com/drive/folders/0B5eBgc8jrKtpTXhfRmpKbUEtdlE?usp=sharing). The pre-trained models are learned with **DEFAULT** hyper-parameters, using dependency treebanks from [the Universal Dependencies project](http://universaldependencies.org/) [v2.0](http://hdl.handle.net/11234/1-1983). It is likely that using pre-trained word embeddings for initialization and performing a grid search of hyper-parameters (e.g. varying the initial learning rate in {0.0002, 0.0005, 0.001}) would help return better results.
 
 Assume that you are going to utilize a pre-trained model for annotating a corpus whose _each line represents a tokenized/word-segmented sentence_. You  should use `converter.py` in folder `SOURCE_DIR/utils`  to obtain a 10-column data format of this corpus: