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# Steps to train a transformer model based on WMT EN-DE
# Requirements
* `tensorflow` (`1.5`)
* `pyyaml`
-* `SentencePiece`
-
-Please follow instructions to install and build Google SentencePiece on this page: https://github.com/google/sentencepiece
+* `sentencepiece`
-Once it's installed, do not forget to change the SP_PATH variable in scripts.
+Please follow the instructions to install and build [sentencepiece](https://github.com/google/sentencepiece). Once it's installed, do not forget to change the `SP_PATH` variable in scripts.
## Data Preparation
-Before running the script, look at the links to download the Datasets.
-depending on the task, you may change the filenames and the Folders PATH.
+Before running the script, look at the links to download the datasets. Depending on the task, you may change the filenames and the folders paths.
+```bash
cd scripts/wmt
./prepare_data.sh
+```
-The script will train a SentencePiece Model using the same source and target vocab.
-It will tokenize the Dataset and prepare the train/valid/test files.
+The script will train a SentencePiece model using the same source and target vocabulary. It will tokenize the dataset and prepare the train/valid/test files.
## Training
+```bash
cd scripts/wmt
./run_wmt_ende.sh
+```
-By default (to be modified in wmt-ende.yml) training will be done on 4 GPUs and during 200 000 steps.
-
+By default (to be modified in `wmt-ende.yml`) training will be done on 4 GPUs and during 200,000 steps.
## Translate
+```bash
cd scripts/wmt
./eval_wmt_ende.sh
+```
-## Lazy run .....
+## Lazy run...
-You can download directly the pre-tokenized SP dataset here: https://s3.amazonaws.com/opennmt-trainingdata/wmt_ende_sp.tar.gz
-you can download the pre-trained averaged model here: https://s3.amazonaws.com/opennmt-trainingdata/averaged-ende-ckpt500k.tar.gz
+* Pre-tokenized SentencePiece dataset: https://s3.amazonaws.com/opennmt-trainingdata/wmt_ende_sp.tar.gz
+* Pre-trained averaged model: https://s3.amazonaws.com/opennmt-trainingdata/averaged-ende-ckpt500k.tar.gz
-Nist Bleu on Newstest 2014: 26.9
-Nist Bleu on Neswtest 2017: 28.0
+This model achieved the following scores:
+| Test set | NIST BLEU |
+| --- | --- |
+| newstest2014 | 26.9 |
+| neswtest2017 | 28.0 |
