Update README.md

Add transformer models and replace list with table

README.md

@@ -140,21 +140,25 @@ BPE continuation markers can be removed with the `--remove-bpe` flag.
 
 # Pre-trained Models
 
-We provide the following pre-trained fully convolutional sequence-to-sequence translation models:
+We provide the following pre-trained models and pre-processed, binarized test sets:
 
-* [wmt14.en-fr.fconv-py.tar.bz2](https://s3.amazonaws.com/fairseq-py/models/wmt14.v2.en-fr.fconv-py.tar.bz2): Pre-trained model for [WMT14 English-French](http://statmt.org/wmt14/translation-task.html#Download) including vocabularies
-* [wmt14.en-de.fconv-py.tar.bz2](https://s3.amazonaws.com/fairseq-py/models/wmt14.v2.en-de.fconv-py.tar.bz2): Pre-trained model for [WMT14 English-German](https://nlp.stanford.edu/projects/nmt) including vocabularies
+### Translation
 
-We also provide pre-trained language models:
-* [gbw_fconv_lm.tar.bz2](https://s3.amazonaws.com/fairseq-py/models/gbw_fconv_lm.tar.bz2): Pre-trained model for [Google Billion Words](https://github.com/ciprian-chelba/1-billion-word-language-modeling-benchmark) including vocabularies
-* [wiki103_fconv_lm.tar.bz2](https://s3.amazonaws.com/fairseq-py/models/wiki103_fconv_lm.tar.bz2): Pre-trained model for [WikiText-103](https://einstein.ai/research/the-wikitext-long-term-dependency-language-modeling-dataset) including vocabularies
+Description | Dataset | Model | Test set(s)
+---|---|---|---
+Convolutional <br> ([Gehring et al., 2017](https://arxiv.org/abs/1705.03122)) | [WMT14 English-French](http://statmt.org/wmt14/translation-task.html#Download) | [download (.tar.bz2)](https://s3.amazonaws.com/fairseq-py/models/wmt14.v2.en-fr.fconv-py.tar.bz2) | newstest2014: <br> [download (.tar.bz2)](https://s3.amazonaws.com/fairseq-py/data/wmt14.v2.en-fr.newstest2014.tar.bz2) <br> newstest2012/2013: <br> [download (.tar.bz2)](https://s3.amazonaws.com/fairseq-py/data/wmt14.v2.en-fr.ntst1213.tar.bz2)
+Convolutional <br> ([Gehring et al., 2017](https://arxiv.org/abs/1705.03122)) | [WMT14 English-German](https://nlp.stanford.edu/projects/nmt) | [download (.tar.bz2)](https://s3.amazonaws.com/fairseq-py/models/wmt14.v2.en-de.fconv-py.tar.bz2) | newstest2014: <br> [download (.tar.bz2)](https://s3.amazonaws.com/fairseq-py/data/wmt14.v2.en-de.newstest2014.tar.bz2)
+Transformer <br> ([Ott et al., 2018](https://arxiv.org/abs/1806.00187)) | [WMT14 English-French](http://statmt.org/wmt14/translation-task.html#Download) | [download (.tar.bz2)](https://s3.amazonaws.com/fairseq-py/models/wmt14.en-fr.joined-dict.transformer.tar.bz2) | newstest2014 (shared vocab): <br> [download (.tar.bz2)](https://s3.amazonaws.com/fairseq-py/models/wmt14.en-fr.joined-dict.transformer.tar.bz2)
+Transformer <br> ([Ott et al., 2018](https://arxiv.org/abs/1806.00187)) | [WMT16 English-German](https://drive.google.com/uc?export=download&id=0B_bZck-ksdkpM25jRUN2X2UxMm8) | [download (.tar.bz2)](https://s3.amazonaws.com/fairseq-py/models/wmt16.en-de.joined-dict.transformer.tar.bz2) | newstest2014 (shared vocab): <br> [download (.tar.bz2)](https://s3.amazonaws.com/fairseq-py/models/wmt16.en-de.joined-dict.newstest2014.tar.bz2)
 
-In addition, we provide pre-processed and binarized test sets for the models above:
-* [wmt14.en-fr.newstest2014.tar.bz2](https://s3.amazonaws.com/fairseq-py/data/wmt14.v2.en-fr.newstest2014.tar.bz2): newstest2014 test set for WMT14 English-French
-* [wmt14.en-fr.ntst1213.tar.bz2](https://s3.amazonaws.com/fairseq-py/data/wmt14.v2.en-fr.ntst1213.tar.bz2): newstest2012 and newstest2013 test sets for WMT14 English-French
-* [wmt14.en-de.newstest2014.tar.bz2](https://s3.amazonaws.com/fairseq-py/data/wmt14.v2.en-de.newstest2014.tar.bz2): newstest2014 test set for WMT14 English-German
-* [wiki103_test_lm.tar.bz2](https://s3.amazonaws.com/fairseq-py/data/wiki103_test_lm.tar.bz2)
-* [gbw_test_lm.tar.bz2](https://s3.amazonaws.com/fairseq-py/data/gbw_test_lm.tar.bz2) 
+### Language models
+
+Description | Dataset | Model | Test set(s)
+---|---|---|---
+Convolutional <br> ([Dauphin et al., 2017](https://arxiv.org/abs/1612.08083)) | [Google Billion Words](https://github.com/ciprian-chelba/1-billion-word-language-modeling-benchmark) | [download (.tar.bz2)](https://s3.amazonaws.com/fairseq-py/models/gbw_fconv_lm.tar.bz2) | [download (.tar.bz2)](https://s3.amazonaws.com/fairseq-py/data/gbw_test_lm.tar.bz2)
+Convolutional <br> ([Dauphin et al., 2017](https://arxiv.org/abs/1612.08083)) | [WikiText-103](https://einstein.ai/research/the-wikitext-long-term-dependency-language-modeling-dataset) | [download (.tar.bz2)](https://s3.amazonaws.com/fairseq-py/models/wiki103_fconv_lm.tar.bz2) | [download (.tar.bz2)](https://s3.amazonaws.com/fairseq-py/data/wiki103_test_lm.tar.bz2)
+
+### Usage
 
 Generation with the binarized test sets can be run in batch mode as follows, e.g. for WMT 2014 English-French on a GTX-1080ti:
 ```